MODELING THE USER IN NATURAL LANGUAGE SYSTEMS 
Robert Kass and Tim Finin 
Department of Computer and Information Science/D2 
School of Engineering and Applied Science 
University of Pennsylvania 
Philadelphia, PA 19104 
For intelligent interactive systems to communicate with humans in a natural manner, they must have 
knowledge about the system users. This paper explores the role of user modeling in such systems. It 
begins with a characterization of what a user model is and how it can be used. The types of information 
that a user model may be required to keep about a user are then identified and discussed. User models 
themselves can vary greatly depending on the requirements of the situation and the implementation, so 
several dimensions along which they can be classified are presented. Since acquiring the knowledge for 
a user model is a fundamental problem in user modeling, a section is devoted to this topic. Next, the 
benefits and costs of implementing a user modeling component for a system are weighed in light of 
several aspects of the interaction requirements that may be imposed by the system. Finally, the current 
state of research in user modeling is summarized, and future research topics that must be addressed in 
order to achieve powerful, general user modeling systems are assessed. 
1 INTRODUCTION 
Systems that use natural language as a means of com- 
munication must do so in a natural manner. One of the 
features of communication between people is that they 
acquire and use considerable knowledge about their 
conversational partners. In order for machines to inter- 
act with people in a comfortable, natural manner, they 
too will have to acquire and use knowledge of the 
people with whom they are interacting. 
Early research on natural language interfaces tended 
to view natural language as a "very high level" query 
language. One of the important results of research in the 
latter half of the 1970s (Waltz 1978, Kaplan 1982) is the 
realization that natural language communication is 
much more. The use of natural language for communi- 
cation includes a host of conventions that must be 
followed in the dialog (Grice 1975). A person interacting 
with a computer via natural language will assume that 
these conventions are being followed, and will be quite 
unsatisfied if they are not. Most of these conventions 
require, in one way or another, that a conversational 
participant have particular knowledge about the goals, 
plans, capabilities, attitudes, and beliefs of the other 
person. 
This paper analyzes the role of user models in 
systems that interact with individual users in a natural 
language. Although the necessity of having and using a 
model of the user has been seen for some time, only 
within the last few years has it been actively pursued as 
a research topic. This research has been driven, in part, 
by attempts to create natural language interfaces to 
systems that can be characterized as cooperative prob- 
lem solvers. Examples of such systems include intelli- 
gent interfaces to expert systems (Finin et al 1986, 
Carbonell et al 1983), database systems (Carberry 1985, 
Webber 1986), intelligent tutoring systems (Kass 
1987b), and help and advisory systems (Wilensky et al 
1984). 
1.1 AN OVERVIEW OF THIS PAPER 
In the remainder of this section, the kinds of user 
models and systems to be discussed in this paper will be 
characterized, including a general definition of a user 
model and an outline of how it can be used by a 
cooperative, interactive system that converses in natu- 
ral language. The next section addresses the question 
"What is to be modeled?" by looking in some depth at 
the types of information that might be contained in a 
user model. These can be broadly classified as the 
user's goals (and the plans he may use to achieve them), 
capabilities, attitudes, and knowledge or belief. In sec- 
tion 3 a set of dimensions along which user models can 
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Computational Linguistics, Volume 14, Number 3, September 1988 5 
Kass and Finin Modeling the User in Natural Language Systems 
be classified is presented, while section 4 considers the 
methods that might be used to acquire information of 
the user, especially of his goals, plans, and beliefs. 
Section 5 considers several high-level features that have 
an impact on the design of a user modeling system, such 
as which participant in the interaction bears responsi- 
bility for ensuring the communication, or what the 
penalty for an error in the user model is. These consid- 
erations have an impact on the potential benefits and 
costs of employing a user model. The concluding sec- 
tion raises some issues that will require additional 
research in order to produce a powerful, general user 
modeling system. 
1.2 WHAT IS A USER MODEL? 
Specifying what a user model is is not an easy task. An 
initial, general definition is presented here, but is then 
narrowed to focus on explicit, knowledge-based mod- 
els. The various ways in which these user models can 
support a cooperative problem solving system are then 
outlined. 
The term "user model" has been used in many 
different contexts to describe knowledge that is used to 
support a man-machine interface. An initial definition 
for "user model" might be the following: 
A user model is the knowledge about the user, either 
explicitly or implicitly encoded, that is used by the 
system to improve the interaction. 
This definition is at once too strong and too weak. The 
definition is too strong in that it limits the range of 
modeling a natural language system might do to the user 
of the system only. Many situations require a natural 
language system to deal with several models concur- 
rently, as will be demonstrated later in this paper. The 
definition is too weak since it endows every interactive 
system with some kind of user model, usually of the 
implicit variety. The following paragraphs clarify these 
issues, and in so doing restrict the class of models to be 
considered. 
AGENT MODELS 
Imagine a futuristic data base query system: not only do 
humans communicate with the system to obtain infor- 
mation, but other software systems, or even other 
computer systems might query the data base as well. 
The individuals using the data base might be quite 
diverse. Rather than force all users to conform to 
interaction requirements imposed by the system, the 
system strives to communicate with them at their own 
level. Such a system will need to model both people and 
machines. A second situation is when a person uses an 
application such as an advisory system on behalf of 
another individual; the advisor in this case may be 
required to concurrently model both individuals. 
A useful distinction when discussing situations in 
which multiple models may be required is one between 
agent models and user models. Agent models are mod- 
els of individual entities, regardless of their relation to 
the sy,~tem doing the modeling, while user models are 
models of the individuals currently using the system. 
The class of user models is thus a subclass of the class 
of agent models. Most of the discussion in this paper 
applies to the broader class of agent models, however, 
theterm "user model" is well established and hard to 
avoid. Thus "user model" will be used in the remainder 
of this paper, even in situations where "agent model" is 
technically more correct. 
EXPLICIT MODELS 
Agent models that encode the knowledge of the agent 
implicitly are not very interesting. In such systems, the 
model knowledge really consists of the assumptions 
about the agent made by the designers of the system. 
Thus even the FORTRAN compiler can be said to have 
an implicit agent model. 
A more interesting class of models is one in which the 
information about the agent is explicitly encoded, such 
as models that are designed along the lines of knowledge 
bases. In the context of agent models, four features of 
explicitly encoded models are important. 
I. Separate Knowledge Base: Information about an 
agent is collected in a separate module rather then 
distributed throughout the system. 
2. Explicit Representation: The knowledge in the agent 
model is encoded in a representation language that is 
sufficiently expressive. Such a representation lan- 
guage will typically provide a set of inferential serv- 
ices, allowing some of the knowledge of an agent to 
be implicit, but automatically inferred when needed. 
3. Support for Abstraction: The modeling system pro- 
vides ways to describe abstract as well as concrete 
entities. For example, the system might be able to 
discuss classes of users and their general properties 
as well as individuals. 
4. Multiple Use: Since the user model is explicitly 
represented as a separate module, it can be used in 
several different ways (e.g., to support a dialog or to 
classify a new user). This requires that the knowl- 
edge be represented in a more general way that does 
not favor one use at the expense of another. It is 
highly desirable to express the knowledge in a way 
that allows it to be reasoned about as well as rea- 
soned with. 
Agent models that have these features fit nicely into 
current work in the broader field of knowledge repre- 
sentation. In fact, Brian Smith's knowledge representa- 
tion hypothesis (Smith 1982) could be paraphrased to 
address agent modeling as follows: 
Any agent model will be comprised of structural 
ingredients that a) we as external observers naturally 
take to represent a propositional account of the 
knowledge the system has of the agent and b) inde- 
pendent of such external semantical attribution, play 
a figrmal but causal and essential role in the behavior 
that manifests that knowledge. 
6 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
User Model Uses 
0 
*=,.- j r..o,.n. ".n,o 
Interpreting \] ~ v qlunleer / .,oo. info,ma,iun 
Reliolving P q, lnlurprating \[ 
ambiguity referring \] \ Recognizing Correctingisconcepllons 
Recognizing Recognizing ~ 
user goals user plans Modellin 9 Providing Constructing Lexical 
relevance prarequisile referrring choice 
information expressions 
Figure 1. Uses for Knowledge of the User. 
1.3 HOW USER MODELS CAN BE USED 
The knowledge about a user that a model provides can 
be used in a number of ways in a natural language 
system. These uses are generally categorized in the 
taxonomy in Figure 1. At the top level, user models can 
be used to support (1) the task of recognizing and 
interpreting the information seeking behavior of a user, 
(2) providing the user with help and advice, (3) eliciting 
information from the user, and (4) providing information 
to him. Situations where user models are used for many 
of these purposes can be seen in the examples presented 
throughout this paper. 
The characterization of user models remains quite 
broad to allow consideration of a wide range of factors 
involved in building user models. These factors provide 
dimensions upon which the various types of user mod- 
els can be plotted. Section 3 explores these dimensions 
to provide a better understanding of the range of user 
modeling possibilities. Given lhis range of possible 
types of user models, methods for their acquisition can 
be discussed (section 4), along with factors that influ- 
ence the feasibility and attractiveness of particular 
types of user models for given applications (section 5). 
First, however, the types of information a user model 
should be expected to keep are discussed. 
2 THE CONTENTS OF A USER MODEL 
A primary means of characterizing user models is by the 
type of knowledge they contain. This knowledge can be 
classified into four categories: goals and plans, capabil- 
ities, attitudes, and knowledge or belief. Each of these 
categories will be examined in this section to see 
situations where such knowledge is needed, and exam- 
ples of how that knowledge is used in natural language 
systems. 
2.1 GOALS AND PLANS 
The goal of a user is some state of affairs he wishes to 
achieve. A plan is some sequence of actions or events 
that is expected to result in the realization of a particular 
state of affairs. Thus plans are means for accomplishing 
goals. Furthermore, each step in a plan has its own 
subgoal to achieve, which may be realized by yet 
another subplan of the overall plan. As a result, goals 
and plans are intimately related to one another, and one 
can seldom discuss one without discussing the other. 
Knowledge of user goals and plans is essential in a 
natural language system. Individuals participate in a 
conversation with particular goals they wish to achieve. 
Examples of such goals are obtaining information, com- 
municating information, causing an action to be per- 
formed, and so on. A cooperative participant in a 
conversation will attempt to discover the goals of other 
participants in an effort to help those goals to be 
achieved, if possible. 
Recognizing an individual's goal (or goals) may range 
from being a straightforward task, to one that is very 
difficult. Situations in which a natural language system 
must infer goals or plans of user (roughly in order of 
increasing difficulty) include: 
• the user directly states a goal 
• the user's goal may be indirectly inferred from the 
user's utterances 
• the user has incorrect or incomplete goals and plans 
• the user has multiple goals and plans. 
DIRECT GOALS 
In the simplest situations the user may directly state a 
goal, such as 
"How do I get to Twelve Oaks Mall from here?" 
The speaker's goal is to obtain information. A hearer is 
capable of recognizing this goal directly from the ques- 
tion, without further inference. 
INDIRECT GOALS 
Unfortunately, people frequently do not state their 
goals directly. Instead, they may expect the hearer to 
infer their goal from their utterance. For example, when 
a speaker says, 
"Can you tell me what time it is?" 
the hearer readily infers that the questioner wishes to 
know what the current time is. The inferences required 
by the hearer may often be rather involved. Gershman 
looked at this problem with respect to an Automatic 
Yellow Pages Advisor (AYPA) (Gershman 1981). A 
sample interaction with this system might begin with the 
user stating: 
"My windshield is broken, help." 
Computational Linguistics, Volume 14, Number 3, September 1988 7 
Kass and Finin Modeling the User in Natural Language Systems 
The AYPA system must infer that the user wishes to 
replace the windshield and hence needs to know about 
automotive repair shops that replace windshields, or 
glass shops that handle automotive glass. 
Allen and Perrault (1980) studied interactions that 
occur between an information-booth attendant in a train 
station and people who come to the booth to ask 
questions. An example of such an interaction is 
Q. The 3:15 train to Windsor? 
A. Gate 10. 
From the question alone it is unclear what goal Q has in 
mind. However, the attendant has a model of the goals 
individuals who ask questions at train stations have. 
The attendant assumes Q has the goal of meeting or 
boarding the 3:15 train to Windsor. Once the attendant 
has determined Q's goal, he then tries to provide 
information to help Q achieve that goal. In Allen's 
model, the attendant seeks to find obstacles to the 
questioner's goal. Obstacles are subgoals in the plan of 
the Q that cannot be easily achieved by Q without 
assistance. In this case the obstacle in Q's plan of 
boarding the train is finding the location of the train, 
which the attendant resolves by telling Q which gate the 
train will leave from. 
INCORRECT OR INCOMPLETE GOALS AND PLANS 
Sometimes the plans or goals that can be inferred from 
the user's utterances may be incomplete or incorrect. 
Goodman (1985) has addressed the problem of incorrect 
utterances in the context of miscommunication in refer- 
ring to objects. He currently is working on dealing with 
miscommunication on a larger scale to deal with mis- 
communication at the level of plans and goals 
(Goodman 1986). Sidner and Israel (1981) have also 
studied the problem of recognizing when a user's plan is 
incorrect, by keeping a library of "buggy" plans. 1 
Incomplete specification of a goal by the user can be 
dealt with via clarification subdialogs, where the system 
attempts to elicit more information from the user before 
continuing. Litman and Allen (1984) have presented a 
model for recognizing plans in such situations. 
Situations where user goals are incomplete or incor- 
rect violate what Pollack calls the appropriate query 
assumption (Pollack 1985). The appropriate query as- 
sumption is adopted by many systems when they as- 
sume that the user is capable of correctly formulating a 
question to a system that will result in the system 
providing the information they need. As pointed out in 
Pollack et al (1982) this is frequently not the case. 
Individuals seeking advice from an expert often do not 
know what information they need, or how to express 
that need. Consequently such individuals will tend to 
make statements that do not provide enough informa- 
tion, or that indicate they have a plan that will not work. 
A system that makes the appropriate query assumption 
must be able to reason about the true intentions of the 
user when making a response. Often this response must 
address the user goals inferred by the system, and not 
the goal explicit in the user's question. 
MULTIPLE GOALS AND PLANS 
A further complication is the need to recognize multiple 
goals that a user might have. Allen, Frisch, and Litman 
distinguish between task goals and communicative goals 
in a discourse. The communicative goal is the immedi- 
ate goal of the utterance. Thus in the question 
"Can you tell me what time the next train to the 
airport departs?" 
the cornmunicative goal of the questioner is to discover 
when the next train leaves. The task goal of the user is 
to board the train. Carberry's TRACK system (Car- 
berry 1983, and this issue) allows for a complex domain 
of goals and plans. TRACK builds a tree of goals and 
plans that have been mentioned in a dialog. One node in 
the tree is recognized as the focused goal, the goal the 
user is currently pursuing. The path from the focused 
goal to the root of the tree represents the global context 
of the focused goal. The global context represents goals 
that are still viewed as active by the system. Other 
nodes in the tree represent goals that have been active 
in the past, or have been considered as possible goals of 
the user by the system. As the user shifts plans, some of 
these other nodes in the tree may become reactivated. 
2.2 CAPABILITIES 
Some natural language systems need to model the 
capabilities of their users. These capabilities may be of 
two types: physical capabilities, such as the ability to 
physically perform some action that the system may 
recommend, or (for lack of a better term) mental capabil- 
ities, such as the ability of a user to understand a recom- 
mendation or explanation provided by the system. 
Systems that make recommendations involving ac- 
tions on the part of the user must have knowledge of 
whether the user is physically capable of performing 
such actions. Expert and advisory systems have per- 
haps the strongest need for this form of knowledge. An 
expert system frequently asks the user questions to get 
information about the world. For example, medical 
diagnostic systems often need to know the results of 
particular tests that have been run or could be run. The 
system needs to know whether the user is capable of 
performing such tests or acquiring such data. Likewise, 
a recommendation made by an expert system or an 
advisor is of little use if the user is not capable of 
following the recommendation. 
A natural language system also needs to judge 
whether the user will be able to understand a response 
or explanation the system might make. Wallis and 
Shortliffe (1982) addressed this issue by controlling the 
amount of explanation provided, based on the expertise 
level of the current user. Paris's TAILOR system (Paris 
1987) goes beyond the work of Wallis and Shortliffe by 
providing different types of explanations depending on 
8 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
the user's domain knowledge. Paris, comparing expla- 
nations of phenomena from a range of encyclopedias, 
found that explanations geared towards persons naive 
to the domain focused on procedural accounts of the 
phenomena, while explanations for domain experts 
tended to give a hierarchical explanation of the compo- 
nents of the phenomena. TAILOR consequently gener- 
ates radically different explanations depending on 
whether the user is considered to be naive or expert 
with respect to the domain of explanation. Webber and 
Finin (1984) have surveyed ways that an interactive 
system might reason about its user's capabilities to 
improve the interaction. 
Care should be taken to distinguish between mental 
capabilities and domain knowledge possessed by the 
user. In each of the examples above, some global 
categorization of the user has been made (into classes 
such as naive or expert) with respect to the domain. 
This category is used as the basis for a judgment of the 
user's mental capabilities. Much more could be done: 
modeling of mental capabilities of users should also 
involve modeling of human learning, memory, and 
cognitive load limitations. Such modeling capabilities 
would allow a natural language system to tailor the 
length and content of explanations, based on the 
amount of information the user is capable of assimulat- 
ing. Modeling of this sort seems a long way off, how- 
ever. Cognitive scientists are just beginning to address 
some of the issues raised here, with current work 
focusing on very simple domains, such as how humans 
learn to use a four-function calculator (Halasz and 
Moran 1983). 
2.3 ATTITUDES 
People are subjective. They hold beliefs on various 
issues that may be well founded or totally unfounded. 
They exhibit preferences and bias toward particular 
options or solutions. A natural language system may 
often need to recognize the bias and preferences a user 
has in order to communicate effectively. 
One of the earliest user modeling systems dealt with 
modeling user preferences. GRUNDY (Rich 1979) rec- 
ommended books to users, based on a set of self- 
descriptive attributes that the users provided and on 
user reactions to books recommended by the system. 
Although GRUNDY dealt with personal preferences 
and attitudes, it had the advantage of being able to 
directly acquire these attitudes by asking the user. In 
most situations it is not socially acceptable to question 
a user about particular attitudes, hence the system must 
resort to acquiring this information implicitly--based on 
the behavior of the user. The Real-Estate Advisor 
(Morik and Rollinger 1985) and HAM-ANS (Hoeppner 
et al 1983, Morik 1988) do this to some degree in the 
domains of apartment and hotel room rentals. The user 
will express some preferences about particular types of 
rooms or locations, and each system can then make 
deeper inferences about preferences the user might 
have. This information is used to tailor the information 
provided and the suggestions made by the systems. 
A natural language system needs to consider per- 
sonal attitudes when generating responses. The choice 
of words used, the order of presentation or the presence 
or lack of specific items in an answer can drastically 
alter the impact a response has on the user. Jameson 
(1983, 1988) addresses this issue in the system IMP. 
IMP takes the role of an informant who responds to 
questions from a user concerned with evaluating a 
particular object (in this case, an apartment). IMP can 
assume a particular bias (for or against the apartment in 
question, or neutral) and uses this bias in the responses 
it makes to the user. Thus if IMP is favorably biased 
towards a particular apartment, it will include additional 
but related information in responses that favorably 
represent the apartment, while attempting to temper 
negative features with qualifiers or additional non- 
negative features. Thus IMP strives to be a cooperative, 
biased system while appearing to be objective. 
Swartout (1983) and McKeown (1985a) address the 
effects of the user's perspective or point of view on the 
explanations generated by a system. In the XPLAIN 
system built to generate explanations for the Digitalis 
Therapy Advisor, Swartout uses a very rudimentary 
technique to represent points of view. Attached to each 
rule in the knowledge base is a list of viewpoints. Only 
rules with a viewpoint held by the user are used in 
generating an explanation. McKeown uses intersecting 
multiple hierarchies in the domain knowledge base to 
represent the different perspectives a user might have. 
This partitioning of the knowledge base allows the 
system to distinguish between different types of infor- 
mation that support a particular fact. When selecting 
what to say the system can choose information that 
supports the point the system is trying to make, and that 
agrees with the perspective of the user. 
Utterances from the user must be considered in light 
of potential bias as well. Sparck Jones (1984) considers 
a situation where an expert system is used to compute 
benefits for retired people. The system is used directly 
by an agent who talks to the actual people under 
consideration by the system (the patients).2 In this case 
the system must recognize potential bias on the parts of 
both agent and patient. The patient may withhold infor- 
mation or try to "fudge" information in order to im- 
prove their benefits, while the bias of the agent may 
color information about the patient by the way the agent 
provides the information to the system. 
2.4 KNOWLEDGE AND BELIEF 
Any complete model of a user will include information 
about what the user knows, or what he believes. In the 
context of modeling other individuals, an agent does not 
have access to objective truth and hence cannot really 
distinguish whether a proposition is known or simply 
believed to be true. Thus the terms knowledge and 
belief will be used interchangeably. 
Computational Linguistics, Volume 14, Number 3, September 1988 9 
Kass and Finin Modeling the User in Natural Language Systems 
Modeling the knowledge of a user involves a variety 
of things. First, there is the knowledge the user has of 
the domain of the application system itself. In addition, 
a user model may need to model information the user 
has about concepts beyond the actual domain of the 
application (which might be called commonsense or 
worm knowledge). Finally, any user, being an intelligent 
agent, has a model of other agents (including the sys- 
tem) and even of himself or herself. These models are 
recursive, in that the user's model of the system will 
include information about what the user believes the 
system believes about the user, about what the user 
believes the system believes the user believes about the 
system, and so on. In the following paragraphs each 
type of belief is explored in more detail. 
DOMAIN KNOWLEDGE 
Knowing what the user believes to be true about the 
application domain is useful for many types of natural 
language systems. In generating responses, knowledge 
of the concepts and terms the user understands or is 
familiar with allows the system to produce responses 
incorporating those concepts and terms, while avoiding 
concepts the system feels the user might not under- 
stand. This is especially true for intelligent help systems 
(Finin 1982), which must provide clear, understandable 
explanations to be truly helpful. Providing definitions of 
database items (such as the TEXT system does (Mc- 
Keown 1985b)) has a similar requirement to express the 
definition at a level of detail and in terms the user 
understands. UC also uses its user model (KNOME) 
(Chin 1988) to help tailor responses, such as determin- 
ing whether to explain a command by using an analogy 
to commands the user already knows. 
Knowing what the user believes is also important 
when requesting information from the user. As Webber 
and Finin have pointed out (Webber and Finin 1984), 
systems that ask questions of the user (such as expert 
systems) should recognize that users may not be able to 
understand some questions, particularly when the sys- 
tem uses terminology or concepts the user is unfamiliar 
with. Such systems need knowledge of the user to aid in 
formalizing such questions. 
Modeling user knowledge of the application domain 
can take on two forms: overlay models and perturbation 
models. 3 An overlay model is based on the assumption 
that the user's knowledge is a subset of the domain 
knowledge. An overlay user model can thus be thought 
of as a template that is "laid over" the domain knowl- 
edge base. Domain concepts can then be marked as 
"known" or "not known" (or with some other method, 
such as an evidential scheme), reflecting beliefs inferred 
about the user. Overlay modeling is a very attractive 
technique because it is easy to implement and can be 
very effective. Unfortunately the underlying assump- 
tion of an overlay model, that the user's knowledge is a 
subset of the domain knowledge of the system, is quite 
wrong. An overlay model can not account for users who 
organize their knowledge of the domain in a structure 
different from that used in the domain model, nor can it 
account for misconceptions users may hold about 
knowledge in the knowledge base. 
The perturbation model is capable of representing 
user beliefs that the overlay model cannot handle. A 
perturbation user model assumes that the beliefs held by 
the user are similar to the knowledge the system has, 
although the user may hold beliefs that differ from the 
system's in some areas. These differences in the user 
model can be viewed as perturbations of the knowledge 
in the domain knowledge base. Thus the perturbation 
user model is still built with respect to the domain 
model, but allows for some deviation in the structure of 
that knowledge. 
McCoy's ROMPER system (McCoy 1985, and this 
issue) assumes a perturbation model in dealing with 
misconceptions the user might have about the meaning 
of terms or the relationship of concepts in the domain of 
financial instruments. When the user is recognized to 
hold a belief that is inconsistent with its own domain 
model, ROMPER tries to correct this misconception by 
providing an explanation that refutes the incorrect in- 
formation and supplies the user with corrective infor- 
mation. The domain knowledge in the ROMPER system 
is represented in a KL-ONE-like semantic network. 
ROMPER considers user misconceptions that result 
from misclassification of a concept ("I thought a whale 
was a fish") or misattribution ("What is the interest rate 
on this stock?"). 
WORLD KNOWLEDGE 
Often a natural language system requires knowledge 
beyond the narrow scope of the application domain in 
order to interact with the user in an appropriate manner. 
Sparck Jones (1984) has classified three types of knowl- 
edge about the user that an expert system might keep: 
• Decision Properties: domain-related properties used 
by the system in its reasoning process. 
• Non-Decision Properties: properties not directly used 
in making a decision, but that may be useful. Exam- 
ples of such properties might be the name, age, or sex 
of the user. 
• Subjective Properties: non-decision properties that 
tend to change over time. 
Decision properties primarily influence the effective- 
ness of expert system performance. Non-decision prop- 
erties can influence the efficiency of the system by 
enabling inferences that reduce the number of questions 
the system may need to ask the user. All three types of 
properties influence the acceptability of the system, the 
manner in which the system interacts with the user. 
Static non-decision properties and subjective properties 
comprise knowledge of the user outside the domain of 
the underlying application system. While such knowl- 
edge may not influence the effectiveness of the under- 
10 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
lying system, it has a great impact on the efficiency and 
acceptability of the system. Hence world or common- 
sense knowledge is useful for a natural language system 
to enhance its ability to interact with the user. 
A special case of modeling information outside the 
domain of the application is when that information is 
closely related to the domain. Schuster (1984, 1985) has 
explored this in the context of the tutoring system VP 2 
for students learning a second language. Such students 
tend to use the grammar of their native language as a 
model for the grammar of the language they are learn- 
ing. Since VP 2 has knowledge of the native language of 
the student, it can be much more effective in recogniz- 
ing misconceptions the student might have when they 
make mistakes. A tutoring system would also be able to 
use this second language knowledge in introducing new 
material, since frequently such material would have 
much in common with the student's native language. 
KNOWLEDGE OF OTHER AGENTS 
A final form of user knowledge that is very important 
for natural language systems is knowledge about other 
agents. As an interaction with a user progresses, not 
only will the system be building a model of the beliefs, 
goals, capabilities, and attitudes of the user, the user 
will also be building a model of the system. Sidner and 
Israel (1981) make the point that when individuals 
communicate, the speaker will have an intended mean- 
ing, consisting of both a propositional attitude and the 
propositional content of the utterance. The speaker 
expects the hearer to recognize the intended meaning, 
even though it is not explicitly stated. Thus a system 
must reason about what model the user has of the 
system when making an utterance, because this will 
affect what the system can conclude about what the 
user intends the system to understand by the user's 
statement. 
A further complication in the modeling a user's 
knowledge of other individuals are infinite-reflexive 
beliefs (Kobsa 1984). An example of such a belief is the 
following situation: 
S believes that U believes p. 
S believes that U believes that S believes that U 
believes p. 
. 
An important instance of such infinite-reflexive beliefs 
are mutual beliefs. A mutual belief occurs when two 
agents believe a fact, and further believe that the other 
believes the fact, and believes that they both believe the 
fact, and so on. Kobsa has pointed out that in the 
context of user modeling only one-sided mutual beliefs, 
i.e., what the system believes is mutually believed, are 
of interest. 
User's beliefs about other agents and mutual beliefs 
cause significant representational difficulties. Kobsa 
(1985) lists three techniques that have been used to 
represent beliefs of other agents: 
• The syntactic approach, where the beliefs of an agent 
are represented in terms of derivability in a first-order 
object-language theory of the agent (Konolige 1983, 
Joshi et al 1984, Joshi 1982); 
• The semantic approach, where knowledge and wants 
are represented by the accessibility relationships be- 
tween possible worlds in a modal logic (Moore 1984, 
Halpern and Moses 1985, Fagin and Halpern 1985); 
• The partition approach, where beliefs and wants of 
agents are represented in separate structures that can 
be nested within each other to arbitrary depths 
(Kobsa 1985, Kobsa 1988, Wilks and Bien 1983). 
While the first two approaches are primarily formal 
attempts, the partition approach has been implemented 
by Kobsa in the VIE-DPM system. VIE-DPM uses a 
KL-ONE-like semantic network to represent both ge- 
neric and individual concepts. The individual concepts 
(and associated individualized roles) form elementary 
situation descriptions. Every agent modeled by the 
system (including the system itself) can be thought of as 
looking at this knowledge base from a particular point of 
view, or context. The context contains the acceptance 
attitude the agent has towards each individual concept 
and role in the knowledge base. An acceptance attitude 
can be either belief, disbelief, or no belief. 4 An agent 
A's beliefs about another agent B is formed by applying 
acceptance attitudes in A's context to the acceptance 
attitudes of B. This technique can be applied as often as 
needed to build complex belief structures involving 
multiple agents. Kobsa has further extended the repre- 
sentation to handle infinite-reflexive beliefs in a straight- 
forward manner. 
To summarize, several types of knowledge may be 
required for a natural language system to effectively 
communicate with the user. This knowledge can be 
classified into four categories: goals and plans, capabil- 
ities, attitudes, and knowledge or belief. Not all of this 
information may be required for any given application. 
Each type of information is needed in some forms of 
interaction, however, and a truly versatile natural lan- 
guage system would require all forms. 
3 THE DIMENSIONS OF A USER MODEL 
User models are not a homogeneous lot. The range of 
applications for which they may be used and the differ- 
ent types of knowledge they may contain indicate that a 
variety of user models exist. In this section the types of 
user models themselves, classified according to several 
dimensions are studied. 
Several user modeling dimensions have been pro- 
posed in the past. Finin and Drager (1986) have distin- 
guished between models for individual users and models 
for classes of users (the degree of specialization) and 
between long- or short-term models (the temporal ex- 
tent of the model). Sparck Jones (1984) adds a third, 
whether the model is static or dynamic. Static models 
Computational Linguistics, Volume 14, Number 3, September 1988 11 
Kass and Finin Modeling the User in Natural Language Systems 
do not change once they are built, while dynamic 
models change over time. This dimension is the modi- 
fiability dimension of the model. 
Rich (1979, 1983), likewise has proposed these three 
dimensions, but treats the modifiability category a little 
differently. Instead of static models, she describes 
explicit models, models defined explicitly by the user 
and that remain permanent for the extent of the session. 
Examples of explicit models are "login" files or cus- 
tomizable environments. She uses the term implicit 
model for models that are acquired during the course of 
a session and that are hence dynamic. This characteri- 
zation seems to mix two separate issues: the method of 
model acquisition, and the modifiability of the model. 
Thus the modifiability category will be limited to refer 
only to whether the model can change during a session, 
while the acquisition issues will be discussed in the next 
section. 
Three other modeling dimensions are of interest: the 
method of use (either descriptive or prescriptive), the 
number of agents (modeling a given agent may depend 
upon the models of other agents as well), and the 
number of models (more than one model may be nec- 
essary to model an individual agent). Figure 2 summa- 
rizes these dimensions. 
3.1 DEGREE OF SPECIALIZATION 
User models may be generic or individual. A generic 
user model assumes a homogeneous set of users--all 
individuals using the program are similar enough with 
respect to the application that they can be treated as the 
same type of user. Most of the natural language systems 
that focus on inferring the goals and plans of the user 
use a single, generic model. These systems include 
ARGOT (Allen et al 1982), TRACK (Carberry 1983, and 
this issue), EXCALIBUR (Carbonell et al 1983) and 
AYPA (Gershman 1981). 
Individual user models contain information specific 
to a single user. A user modeling system that keeps 
individual models thus will have a separate model for 
each user of the system. This may become very expen- 
sive in terms of storage requirements, particularly if the 
system has a large number of users. 
A natural way to combine the system's knowledge 
about classes of users with its knowledge of individuals 
is through the use of stereotype models. A stereotype is 
a cluster of characteristics that tend to be related to 
each other. When building a model of a user, certain 
pieces of information serve as triggers (Rich 1979) to a 
stereotype. A trigger will cause the system to include its 
associated cluster of characteristics into the individual 
user model (unless overridden by other information). 
Systems that have used stereotypes such as GRUNDY 
(Rich 1979), the Real-Estate Advisor (Morik and Rol- 
linger 1985) and GUMS 1 (Finin and Drager 1986) further 
enhance the use of stereotypes by allowing them to be 
arranged in a hierarchy. As more information is discov- 
ered about the user, more specific stereotypes are 
Degree of Specialization ~.,,- 
individual gener=c 
Modifiability 
.~l-.-~-m.. - dynamic ~':~ static 
Temporal Extent 4.___ 
short term long term 
Method of Use 
~descriptive prescrip tive~ 
Number of Agents 
--, le ~ "single multi p 
Number of Models 
~single multiple" 
Figure 2. Dimensions of a User Model. 
activated (moving down the tree as in GUMS,), or the 
user model invokes several stereotypes concurrently (as 
in GRUNDY). 
A user modeling system might use a combination of 
these approaches. Consider a database query system. A 
generic user model may be employed for areas where 
the user population is homogeneous, such as modeling 
the goals of users of the system. At the same time, 
individual models might be kept of the domain knowl- 
edge of the users, their perspective on the system, and 
the level of detail they expect from the system. 
3.2 MODIFIABILITY 
Users models can be static or dynamic. A static user 
model is one where the model does not change during 
the course of interaction with the user, while dynamic 
models can be updated as new information is learned. A 
static model can be either pre-encoded (as is implicitly 
done with most programs) or acquired during an initial 
session with the user before entering the actual topic of 
the discourse. Dynamic models will incorporate new 
information about the user as it becomes available 
during the course of an interaction. User models that 
track the goals and plans of the user must be dynamic. 
Different types of knowledge may require different 
degrees of modifiability. Goal and plan modeling re- 
quires a dynamic model, but user attitudes or beliefs 
about domain knowledge in many situations may effec- 
tively be modeled with static information. Sparck Jones 
(19841) refers to objective properties of the user (things 
like age and sex) that are not expected to change over 
the course of a session. Objective properties, consisting 
of the decision and non-decision properties in her 
classification, require only static modeling. On the other 
hand, subjective properties are changeable and hence 
require a dynamic model. 
12 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
3.3 TEMPORAL EXTENT 
At the extremes, user models can be short term or long 
term. A short-term model might be one that is built 
during the course of a conversation, or even during the 
course of discussing a particular topic, then discarded at 
the end. Generic, dynamic user models are thus usually 
short term since they have no facility for remembering 
information about an individual user. 5 On the other 
hand, individual models and static models will be long 
term. Static models by their nature are long term, while 
individual models are of little use if the information they 
retain from session to session is no longer applicable. 
3.4 METHOD OF USE 
User models may be used either descriptively or pre- 
scriptively. The descriptive use of a user model is the 
more "traditional" approach to user models. In this 
view the user model is simply a data base of information 
about the user. An application queries the user model to 
discover the current view the system has of the user. 
Prescriptive use of a user model involves letting the 
model simulate the user for the benefit of the system. 
An example of a prescriptive use of a user model is in 
anticipation feedback loops (Wahlster and Kobsa 1988). 
In an anticipation feedback loop the system's language 
analysis and interpretation components are used to 
simulate the user's interpretation of a potential response 
of the system. The HAM-ANS system (Hoeppner et al 
1983) uses an anticipation feedback loop in its ellipsis 
generation component to ensure that the response con- 
templated by the system is not so brief as to be 
ambiguous or misleading. Jameson's IMP system (Ja- 
meson 1983, 1988) also makes use of an anticipation 
feedback loop to consider how its proposed response 
will affect the user's evaluation of the apartment under 
consideration. 
3.5 NUMBER OF AGENTS 
User-machine interaction need not be one-on-one. In 
some situations a system may need to actively deal with 
several individuals, or at least with their models. Recall 
Sparck Jones's (1984) distinction between the agent and 
patient in an expert system: the agent is the actual 
individual communicating with the system, while the 
patient is the object of the expert system's diagnosis or 
analysis. The patient may be human or not (for exam- 
ple, it might be a broken piece of equipment). In the 
case where the patient is a human, the system must be 
aware that system requests, explanations, and recom- 
mendations will have an impact on both the agent and 
patient, and that impact may be decidedly different on 
each individual. In her example of an expert system that 
advises on benefits for retired people, the agent is 
responsible for providing information to the system 
about the patient. The system must have a model of the 
patient not only for its analysis, but also to guide the 
communication with the patient. In this case, however, 
the only way of obtaining that model is through another 
individual who will filter information based on his own 
bias. Thus the system must use its model of the model 
the agent has of the patient in building its own model of 
the patient. 
3.6 NUMBER OF MODELS 
It is even possible to have multiple models for a given 
user. Some of the systems that employ stereotypes, 
such as GRUNDY, address this by allowing the user 
model to inherit characteristics from several stereo- 
types at once. When interaction with an individual 
triggers several different stereotypes, conflicts between 
stereotypes must be resolved in some manner. 
GRUNDY uses a numeric weighting method to indicate 
the degree of belief the system has in each item in the 
user model. When new information is added, either 
directly or through the triggering of another stereotype, 
evidence combination rules are invoked to resolve 
differences and strengthen similarities. Thus GRUNDY 
still maintains a single model of the user and attempts to 
resolve differences within that model. 
The ability to combine stereotypes is also useful for 
building composite models that cover more than one 
domain. For example, consider building a modeling 
system for a person's familiarity with the operating 
system of a computer, such as was done with the VMS 
operating system in (Shrager 1981, Shrager and Finin 
1982, Finin 1983). The overall domain, knowledge of the 
VMS system, is quite large and non-homogeneous and 
can be broken down into many subdomains (e.g., the 
file system, text editors, the DCL commands interface, 
interprocess communication, etc). It is more reasonable 
to build stereotypes that represent a person's familiarity 
with the subdomains rather than the overall domain. 
Rather than build global stereotypes such as VMS- 
Novice and VMS-Expert that attempt to model a ste- 
reotypical user's knowledge of the entire domain, it is 
more appropriate to build separate stereotype systems 
to cover each subdomain. This allows one to model a 
particular user as being simultaneously an emacs-novice 
and a teco-expert. 
Wahlster and Kobsa (1988) consider a situation 
where a system may require multiple, independent 
models for a single individual. Among humans this 
happens all the time when individuals represent busi- 
nesses or different organizations. Quite often two state- 
ments like the following will occur during the course of 
a business conversation. 
"Last time we met we had an excellent dinner 
together." 
"This product is going to be a big seller." 
The first statement is made by a salesman speaking as a 
"normal human," perhaps as a friend of the client. The 
second statement is made with the "salesman hat" on. 
Modeling such a situation cannot be handled by multiple 
stereotype inheritance, because frequently the two hats 
of the user will be drastically inconsistent. Further- 
Computational Linguistics, Volume 14, Number 3, September 1988 13 
Kass and Finin Modeling the User in Natural Language Systems 
more, the inconsistencies should not be resolved. 
Rather it is necessary to be able to switch from one hat 
to another. This problem is compounded because the 
two models of an individual are not separate. For 
example, the goals and plans of the individual may 
involve switching hats at various points in the conver- 
sation. Thus there needs to be a central model of the 
user, with submodels that are disjoint from each other. 
The system must then be able to decide which submodel 
is necessary, and recognize when to switch submodels. 
4 ACQUIRING USER MODELS 
How a user model is acquired is central to the whole 
enterprise of building user models. A user model is not 
useful unless it can support the needs of the larger 
system that uses it. The ability of a user model to 
support requests to it depends crucially on the rele- 
vance, accuracy, and amount of knowledge the user 
model has. This in turn depends on the acquisition of 
such knowledge for the user model. In this section two 
methods of user model acquisition are discussed, and 
techniques that have been used to acquire various types 
of knowledge about the user, particularly the user's 
goals, plans, and beliefs, will be described. 
4.1 METHOD OF ACQUISITION 
The knowledge that a user model contains can be 
acquired in two ways: explicitly or implicitly. Explicitly 
acquired knowledge is knowledge that is obtained when 
an individual provides specific facts to the user model. 
Explicit knowledge acquisition most often occurs with 
knowledge acquired for generic user models or for 
stereotypes. In these cases the user model is usually 
hand built by the system implementor according to the 
expectations the designers have for the class or classes 
of users of the system. 
Knowledge can also be acquired explicitly from the 
user. For example, when a user accesses the system for 
the first time, the system may begin by asking the user 
a series of questions that will give the system an 
adequate amount of information about the new user. 
This is how GRUNDY acquires most of its individual- 
ized information about the user. When a person uses the 
system for the first time GRUNDY asks for a list of 
words describing the user. From this list GRUNDY 
makes judgments about which stereotypes most accu- 
rately fit the user (the stereotypes had been hand coded 
by the system designer) and thus forms an opinion about 
the preferences of the user based on this initial list of 
attributes. 
Acquiring knowledge about the user implicitly is 
usually more difficult than acquiring it explicitly. Im- 
plicit user model acquisition means that the user model 
is built by observing the behavior of the user and 
inferring facts about the user from the observed behav- 
ior. For a natural language system this means that the 
user modeller must be able to "eavesdrop" on the 
system-user interaction and make its judgments based 
on the conversation between the two. 
4.2 TECHNIQUES FOR ACQUIRING USER MODELS 
In this section techniques that have been used to 
acquire information for a user model are presented, 
focu,dng primarily on how to acquire knowledge about 
user goals, plans, and beliefs, since these areas have 
received the most attention to date. 
GOALS 
At any given time, a computer system user will usually 
have several goals that he is trying to accomplish. Some 
of these goals may be assumed to apply to all users of 
the system. For example, a database query system can 
assume at the very least that the user has the goal of 
obtaining information from the system. These general 
goals may either be encoded explicitly in a generic user 
model, or may be omitted altogether, being assumed in 
the design of the system itself. 
A user modeling system will also need to model 
user's immediate goals. Sometimes the goals are explic- 
itly stated by the user. For example: 
"I want to get to the airport, when does the next train 
depart?" 
Often they are not. Frequently people do not explicitly 
state their goal, but expect the hearer to infer that goal 
from the utterance. Thus a speaker who says, 
"When does the next train to the airport depart?" 
probably has the same goal as the speaker of the first 
sentence, but the hearer must reason from the statement 
to determine that goal. This sort of goal inference from 
indirect questions was part of the work done by Allen 
and Perault (1980). 
PLANS 
As goals become more complex, the task of inferring a 
user's goals becomes mixed with the task of inferring 
the plans held by the user. Much work has been done in 
recognizing plans held by users. Kautz and Allen (1986) 
have categorized past approaches to plan inference as 
using either the explanation-based approach, the pars- 
ing approach, or the likely inference approach. 
In the explanation approach, the system attempts to 
come up with a set of assumptions that will explain the 
behavior of the user. The TRACK system (Carberry 
1983, and this issue) uses such an approach. In the 
context of a system to advise students about college 
courses, a user might ask, 
"Is Professor Smith teaching Expert Systems next 
semester?" 
TRACK will recognize three possible plans the user 
might have that would explain this statement. 
1. The student may want to take Expert Systems, 
taught by Professor Smith. 
14 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
2. The student may want to take Expert Systems, 
regardless of the professor. 
3. The student may want to take a course taught by 
Professor Smith. 
TRACK maintains a tree of the possible plans the user 
may have and refines its judgment as more information 
becomes available. 
The plan parsing approach was first used by Gene- 
sereth for the MACSYMA Advisor (Genesereth 1979, 
1982). Available to the MACSYMA Advisor is a record 
of the past interaction of the user with the symbolic 
mathematics system MACSYMA. When the user en- 
counters a problem and asks the Advisor for help, the 
MACSYMA Advisor is able to parse the past interac- 
tion of the user with the system to come up with the plan 
the user is pursuing. Such an approach depends on the 
availability of a great deal of information about the plan 
steps executed by the user. Plan parsing has not been 
used for user modeling in natural language systems 
because of the difficulty in getting such information 
from a solely natural language interaction. 
The likely inference approach relies on heuristics to 
reduce the space of possible plans that a system might 
attribute to the user. This approach is used by Pollack 
(Pollack 1985, Pollack 1986) to infer the plans of users 
who present inappropriate queries to the system. Pol- 
lack reasons that the inappropriate query by the user 
was an attempt to achieve some subgoal in the user's 
larger plan. Since this subgoal has failed, Pollack's 
system tries to identify what the overall goal is, and 
suggest an action that will salvage the user's plan. 
The plan inference approaches rely on two things to 
accomplish their task. First, all plan inference mecha- 
nisms must have a lot of knowledge about the domain 
and about the kinds of plans the user might have. Many 
systems implicitly assume that they know all possible 
plans that may be used to achieve the goals recognizable 
by the system. Some systems (such as the system 
described by Sidner and Israel (1981) and Shrager and 
Finin (1982) augment their domain knowledge with a 
bad plan library--a collection of plans that will not 
achieve the goals they seek, but that are likely to be 
employed by a user. 
BELIEFS 
Acquiring knowledge about user beliefs is a much more 
open-ended task than acquiring knowledge about goals 
and plans. Goals and plans have an inherent structure 
that helps acquisition of such information. Inferring the 
user's plan reaps the side benefit of inferring not only 
the main goal of the user, but also a number of subgoals 
for the steps in the plan. User plans tend to persist 
during a conversation, so new plan inference does not 
need to be going on continuously. Beliefs of the user, on 
the other hand, lack that unifying structure. Inferring 
user beliefs implicitly requires the user modeling system 
to be constantly alert for clues it can use to make 
inferences about user beliefs. 
Knowledge about user beliefs can be acquired in 
many ways. Sometimes users make explicit statements 
about what they do or don't know. If the system 
presumes that a user has accurate knowledge of his own 
beliefs and that the user is not lying (a reasonable 
assumption for the level of systems today), such explicit 
statements can be used to directly update the user 
model. 
Even when users do not explicitly state their beliefs, 
statements they make may contain information that can 
be used to infer user beliefs. Kaplan (1982) points out 
that user questions to a database system (as well as 
other systems) often depend on presuppositions held by 
the user. For example, the question 
"Who was the 39th president? 
presupposes that there was a 39th president. A user 
modeling system may thus add this belief to its model of 
the user. When a presupposition is wrong (does not 
agree with the domain knowledge of the system), it may 
be possible to infer more information about the beliefs 
of the user. The incorrect presupposition may reflect an 
object-related misconception, in which case a system 
such as ROMPER (McCoy 1985, 1986) could detect 
whether the misconception was due to a misclassifica- 
tion of the concept, or a misattribution. Such a miscon- 
ception may indicate a misunderstanding about other, 
related terms as well. 6 
Other techniques can be used to infer beliefs of the 
user based on the user's interaction with the system, but 
with conclusions that are less certain. These approaches 
can be classified as either primarily recognition oriented 
or primarily constructive. 
The recognition approaches use the statements made 
by the user in an attempt to recognize pre-encoded 
information in the user model that applies to the user. 
Stereotype modeling uses this approach: a stereotype is 
a way of making assumptions about an individual user's 
beliefs that cannot be directly inferred from interaction 
with the system. Thus if the user indicates knowledge of 
a concept that triggers a stereotype, the whole collec- 
tion of assumptions in the stereotype can be added to 
the model of the individual user (Rich 1979, Morik and 
Rollinger 1985, Chin 1988, Finin and Drager 1986). 
Stereotype modeling enables a robust model of an 
individual user to be developed after only a short period 
of interaction. 
Constructive modeling attempts to build up an indi- 
vidual user model primarily from the information pro- 
vided in the interaction between the user and the 
system. For example, a user modeling system might 
assume that the information provided by the system to 
the user is believed by the user thereafter. This assump- 
tion is reasonable, since if the user does not understand 
what the system says (or does not believe it), he is likely 
to seek clarification (Rich 1983), in which case the 
Computational Linguistics, Volume 14, Number 3, September 1988 15 
Kass and Finin Modeling the User in Natural Language Systems 
errant assumption will be quickly corrected. Another 
approach is based on Grice's Cooperative Principle 
(Grice 1975). If the system assumes that the user is 
behaving in a cooperative manner, it can draw infer- 
ences about what the user believes is relevant, and 
about the user's knowledge or lack of knowledge. 
Perrault (1987) has recently proposed a theory of speech 
acts that implements Grice's Maxims as default rules 
(Reiter 1980). Kass and Finin (Kass 1987a, Kass and 
Finin 1987c) have taken a related approach, suggesting 
a set of default rules for acquiring knowledge about the 
user in cooperative advisory systems, based on assump- 
tions about the type of interaction and general features 
of human behavior. 
Another technique mixes the implicit and explicit 
methods of acquiring knowledge about the user, by 
allowing the user modeling module to directly query the 
user. In human conversation this seems to happen 
frequently: often a hearer will interrupt the speaker to 
clarify a statement the speaker has made, or to seek 
elaboration or justification for a statement. In the envi- 
ronment of a natural language system one could envi- 
sion a user modeling module that occasionally proposes 
a question to the user that would help the user modeling 
module choose between two or more possible assump- 
tions about the user that are considered important to the 
main focus of the conversation. 7 
Finally, there is a close relationship between knowl- 
edge acquisition and knowledge representation. The 
very nature of user modeling implies uncertainty of the 
knowledge acquired about the user. Often a user model 
may make assumptions about the user that need to be 
• retracted when more information is obtained. In addi- 
tion, the subject being modeled is dynamic--as an 
interaction progresses the user being modeled will learn 
new information, alter plans, and change goals. The 
knowledge representation for a user model must be able 
to accommodate this change in knowledge about the 
user. To cope with the non-monotonicity of the user 
model, the knowledge representation system used will 
need to have some form of a truth maintenance system 
(Doyle 1979), or employ a form of evidential reasoning. 
5 DESIGN CONSIDERATIONS FOR USER MODELS 
Incorporating a user model into a natural language 
system may provide great benefits, but it also has some 
associated costs. The type of information the model is 
expected to maintain and how the model is used will 
affect the overall cost for employing a user modeling 
system. This section focuses primarily on how to weigh 
the benefits of employing a user model against the cost 
of acquiring that model. The benefit provided by a user 
model can be measured by comparing the performance 
of the system with a user model to the performance of 
the system without the user model. The cost of a user 
model may manifest itself in various ways. On systems 
that must do a lot of implicit modeling, the cost may 
appear as a great demand for computational resources 
such as; processor time and memory space. On systems 
that employ stereotypes or a generic user model, the 
cost may be in development time: the man hours spent 
by the system implementors encoding knowledge about 
the user. For some systems the cost of employing a user 
model may be very great, while the benefit is slight. 
Thus the issue of when user models should not be used 
is important as well. 
Several characteristics of the underlying application 
determine the relative benefits and costs of using a user 
modeling system. These issues are: 8 
• Who bears the burden of responsibility for communi- 
cation in the interaction? 
• What is the penalty for error? 
• How rich is the interaction space? 
• How adaptable must the system be, and how quickly 
must it adapt? 
• What mode of interaction will be used by the system? 
The following subsections will discuss how each of 
these issues affects the costs and benefits of a user 
modeling module, concluding with a summary of what 
types of systems may be expected to profitably employ 
a user model. 
5.1 RESPONSIBILITY 
In any dialog, one or more of the participants takes the 
responsibility to ensure that the communication is suc- 
cessful. In human dialogs this burden is usually shared 
by all participants, but not always. Tutors and advisors 
often assume most of the burden of responsibility for 
ensuring that the student or advisee understands the 
material presented, and that questions from the student 
or advisee are correctly handled by the tutor or advisor. 
Systems that make the appropriate query assumption 
place the communication responsibility primarily on the 
shoulders of the user. Since the system assumes the 
user atways provides appropriate queries, the user 
modeling module has much less work to do. The system 
can be content to answer the user's queries without 
having to worry about the possibility of bad plans, or 
goals that differ from those inferred directly from the 
user's statement. In the extreme, any failure in under- 
standing can be blamed on the user. Thus the cost of 
acquiring a user model is not high. On the other hand, a 
user model may not provide much benefit since the 
system need not worry about user goals outside the 
range of those explicitly stated by the user. 
A system that bears the responsibility for communi- 
cation (thus not assuming the user makes appropriate 
queries) has different user modeling requirements. Such 
systems (for example, consultative expert systems like 
MYCIN) need to know the knowledge of the user to aid 
in generating explanations and in posing questions to 
the user. Goal and plan recognition is not very impor- 
tant since these tend to be defined by the system itself. 
16 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
A user model can be quite beneficial in improving the 
acceptability (and maybe the efficiency) of the system. 
On the other hand, implicit acquisition of knowledge 
about the user is difficult since the user participation is 
constrained to responding to the system. Thus the user 
model will probably need to be acquired explicitly, 
either through generic models and stereotypes, or by 
explicit query of the user. 
Systems that share the burden of responsibility with 
the user require the most complex user models. When 
responsibility is shared, the system must be able to 
recognize when the user wants to shift topics or alter the 
focus of the interaction. Thus the system will require a 
very rich representation of possible user goals and plans 
to be able to recognize when the user shifts away from 
the system's plan or goal. A user model thus seems 
essential to support such mixed initiative interactions. 
Although goal and plan inference will be more difficult, 
the user modeling module should have more opportu- 
nity to acquire information from the user in a free- 
flowing exchange. Consequently the costs for acquiring 
knowledge about user beliefs may be less than in the 
two previous situations. Systems in which there is a real 
sharing of the responsibility are, for the most part, still 
a research goal. Reichman (1981) has analyzed this in 
the context of human-human dialogs in some detail. 
Sergot (1983) has studied the architecture of interactive 
logic programming systems where the initiative of ask- 
ing and answering queries can be mixed. In the author's 
own work, the assumption of a shared responsibility 
between system and user has proven beneficial in 
acquiring knowledge about the user implicitly. 
5.2 PENALTY FOR ERROR 
How will an error in the user model influence the 
performance of the application system? A high penalty 
for error means the user modeling module must limit the 
assumptions it makes about the user to those that are 
well justified. Use of stereotypes would be severely 
limited and inferences that were less than certain would 
be avoided. As a consequence, the user model may be 
less helpful to the application system. A high penalty for 
error thus reduces the benefits that may be obtained by 
employing a user modeling system. A low penalty for 
error, on the other hand, allows the user model to make 
assumptions if it has some justification. Mistakes will be 
made, but overall the model should be very helpful to 
the underlying system. 
Penalty for error is related to responsibility for com- 
munication. A high penalty for error in the user model 
can only occur when the system assumes some respon- 
sibility for the communication. In fact, systems that are 
solely responsible for ensuring that communication suc- 
ceeds in an interaction will tend to have the highest 
penalty for error. In mixed initiative dialogs both user 
and system are free to interrupt the conversation to 
correct mistakes that may occur. When the system 
assumes sole responsibility, the user has no method to 
stop the system and try to correct a mistake that has 
been made. Thus the lack of flexibility in such systems 
severely impairs the benefits of a user model. 
5.3 RICHNESS OF INTERACTION SPACE 
The range of interaction a system is expected to handle 
greatly affects the user modeling requirements. If the 
possible user goals are very limited (such as meeting or 
boarding trains) or the domain is limited, a user model 
need not record much information about user. Such 
situations do not require individual models of the user, 
and need only very simple acquisition techniques. Ac- 
quisition of knowledge about the user might be a simple 
search to see which collection of information best 
matches the behavior of the user. 
When the range of interaction increases, more is 
required of the user model. Inferring user plans is a 
typical example. The number of possible plans a user 
might have grows explosively as the complexity of the 
task increases. It is not possible to record all possible 
plans and simply search for a match. Instead, typical or 
likely plans must be entered by the system designers, or 
complex inferencing techniques must be employed. 
The range of possible users also influences the degree 
of specialization needed in the user model. If the users 
form a homogeneous class, a generic user model can be 
built that encompasses much of the information that a 
system might need to know about the user. Thus 
knowledge acquisition costs are limited to the time 
required by the system designers to encode the generic 
model, with very little effort for implicit modeling. As 
the range of possible users increases, so does the cost of 
acquiring information about them. On the other hand, 
user modeling is more important when the set of users is 
diverse, so the system is able to tailor its interaction to 
fit the particular user. 
5.4 ADAPTABILITY 
Adaptability is closely related to the richness of the 
interaction space and to the penalty for error. The 
greater the range of possible users, the more the system 
will be required to adapt. If the penalty for error is high 
as well, the acquisition abilities of the user model must 
be very good. The more adaptable the system must be, 
the greater the learning ability of the user modeling 
module must be. 
Adaptability also concerns how quickly the system is 
required to adapt. Some systems may deal with a wide 
range of users, but the user modeler has a relatively long 
time to develop a model of the individual. Such systems 
have a low penalty for error. If the system must adapt 
very quickly, stereotyping will be necessary, including 
the ability for the system to synthesize new, useful 
stereotypes when it recognizes the need. Such a user 
model will need to be concerned not only with modeling 
the current user, but also potential future users. 
Computational Linguistics, Volume 14, Number 3, September 1988 17 
Kass and Finin Modeling the User in Natural Language Systems 
Difficulty 
Simple 
Question Cooperative Non-cooperative Answering Consultation ConsultTtion 
low I I high Cooperative Biased 
Question Consultation 
Answering Pretending ° 
Objectivity 
Figure 3. Relative Difficulty of Modeling the User in 
Different Types of Interaction. 
5.5 MODE OF INTERACTION 
The mode of interaction with the user will also influence 
the relative cost and benefits of employing a user model. 
Wahlster and Kobsa (1988) present a scale of four 
modes of man-machine interaction that place increasing 
requirements on the user modeling capabilities of a 
system: 
• Simple question answering or biased consultation 
• Cooperative question answering 
• Cooperative consultation 
• Biased consultation pretending objectivity 
Figure 3 shows these four modes plus a final, very 
difficult category: 
• Non-cooperative interaction 
The following paragraphs take a short look at the user 
modeling requirements of each. 
No explicit user model is required for simple ques- 
tion answering systems such as current database query 
systems. Such systems are not concerned with user 
goals and plans, beyond the assumption that the user is 
seeking information. A minimal user model might be 
employed to model user knowledge of the domain itself. 
Biased consultation has similar requirements. No mat- 
ter what the user says the consultant will make the same 
recommendation. The only aid a user model might be is 
in helping the system select information likely to sway 
the user. 
Cooperative question answering requires the system 
to have some idea of the goals of the user. Typically the 
range of goals the system can be expected to recognize 
will be quite limited, since the system is being used 
primarily as an information source. The system must 
also be able to recognize when a response could lead to 
a user misconception. Such systems typically can em- 
ploy a generic user model since there will be little 
differentiation among users from the standpoint of the 
question answering system. 
Cooperative consultation requires an extensive user 
model. As noted in Pollack et al (1982), a consultation 
between an expert and the individual asking advice is 
like a negotiation. A consultation system must be able 
to recognize and understand a wide variety of user 
goals, further compounded by the fact that they may 
involve many misconceptions about facts in the domain 
of consultation. A good consultant should even be able 
to recognize analogies to other domains that the user is 
making (Schuster 1984, 1985). Such consultations fre- 
quently involve extended interactions where much in- 
formation about the user can be collected. In most cases 
this information about the user should be retained, since 
it is likely further consultations will occur. Thus user 
models for cooperative consultation need to record all 
types of information about the user, and save this 
information in long-term individual user models. 
A biased consultation in which the system pretends 
objectivity (such as an electronic salesman) requires 
even more inferences about the user than cooperative 
consultation. Biased consultation requires a deep model 
of user attitudes, and how particular terms or concepts 
affect the attitude of the user. The system must have 
good models of what the user feels is cooperative 
conversation (since the system must appear objective) 
and of the user's model of the system (since the system 
must ensure that the user feels the system is objective). 
Non-cooperative interaction makes the acquisition of 
information about the user very difficult. Even with 
cooperative interaction, much of the information as- 
sumed about the user is uncertain. If the user is not 
cooperating with the system, the possibility of the user 
lying, or withholding the truth, further complicates the 
acquisition of knowledge about the user. The system 
must be able to reason about the motivations of the user 
and be able to discern what information is likely to be 
untrue, and what information should not be influenced 
by the non-cooperative goals or attitudes of the user. 
User models in such situations require very extensive 
knowledge about people in general, and categories of 
people in particular. 
5.6 SUMMARY 
Given these criteria for judging the costs and benefits of 
a user model, some conclusions can be drawn about the 
types of systems that can profitably employ a user 
model. First, user models should only be used in 
situations where the range of interaction is sufficiently 
great that the user model can significantly affect the 
performance of the system. This does not preclude their 
use in more limited interactions, but the costs of imple- 
menting the user model can easily exceed the benefits 
that might be gained, particularly compared to other 
interaction techniques (such as menus) that are easier to 
implement and quite effective when the range of inter- 
action is limited. 
The fact that the user model will be used to alter the 
behavior of the system implies that the system will 
assume some degree of responsibility for ensuring the 
communication between user and system. This means 
the mode of interaction should at least be cooperative. 
18 Computational Linguistics, Volume 14, Number 3, September 1988 
Kass and Finin Modeling the User in Natural Language Systems 
Given the range of interaction types presented in Figure 
3, cooperative question answering and cooperative con- 
sultation are appropriate types of interactions for using 
a user model. The more difficult forms of interaction, 
such as biased consultation pretending objectivity or 
non-cooperative forms of interaction, are very difficult 
and at present have little practical use in the types of 
applications being built. 
Finally, user models are currently viable only in 
situations where there is a low penalty for error. A high 
penalty for error demands very robust user models, 
requiring either extensive explicit coding of the user 
model, or sophisticated acquisition techniques. The 
human costs of coding a robust user model are very 
high, while sophisticated acquisition techniques will not 
be forthcoming soon. Thus in applications where the 
penalty for error is high, responsibility needs to remain 
on the shoulders of the user, with user modeling playing 
at most a secondary role. 
6 CONCLUSION 
The ability to interact with people in an easy and natural 
manner is the promise natural language interfaces hold 
for computer systems. To realize this promise, systems 
need to acquire and use various kinds of information 
about the people with whom they are interacting. That 
is, they need models of their users. 
Sophisticated user models can serve many important 
functions in natural language systems: they can be used 
to tailor the interaction to an individual user, to increase 
the system's cooperativeness, and to correct or even 
prevent misconceptions by the user. This paper has 
made several general points about the role of user 
models in question answering systems. 
• What constitutes a user model is a matter of some 
debate. The view taken in this paper is that a user 
model is an explicit source of knowledge containing 
the beliefs and assumptions the system holds about 
the user. 
• User models must hold many diverse types of infor- 
mation. Natural language systems need to know 
about the user's goals and plans, capabilities, atti- 
tudes, and beliefs. 
• User models can be classified along various dimen- 
sions. In general terms, these dimensions character- 
ize the agents being modeled, how the model changes 
with time, and how it is used. 
• The acquisition of information about the user is a 
central problem that must be faced. The process can 
be explicit, implicit, or a mixture of the two. The 
techniques used for acquisition depend on the kind of 
information. 
• Environmental issues, such as how the model will be 
used, place added constraints on the type of user 
model that may be employed in a particular imple- 
mentation. 
To date, most of the work involving the kind of user 
models discussed in this paper is in an early research 
stage. This research typically focuses on just one aspect 
of the overall user modeling problem, such as plan 
recognition or modeling multiple agents. There is still a 
great deal of research to be done in these individual 
areas. Goal recognition and modeling is central to many 
AI problems and has not yet been adequately handled in 
any real systems. Many of the ways that a user model 
can improve natural language interaction have not yet 
been explored. In the context of generation systems, for 
example, no existing systems use their knowledge of the 
user as a factor in the lexical choice problem. 
Addressing individual problems in user modeling and 
looking at particular applications where a user model 
can help have been appropriate research strategies in 
early investigations. Ultimately, however, user model- 
ing must be addressed from a more global point of view. 
A rich, interactive system will need to model many 
things about many human agents. This information can 
form a central knowledge base for reasoning about 
agents in many contexts. 
The notion of a central user modeling facility has 
motivated work on a general user modeling system or 
general user modeling module (Finin and Drager 1986, 
Kass 1987a, Kass and Finin 1987c). A general user 
modeling system would provide an environment for 
building systems that used a user model, including 
various facilities for maintaining and updating user 
models. A general user modeling module is an indepen- 
dent component of a larger system that provides infor- 
mation about the user to other modules, much like a 
data base or knowledge base. The interface to the 
general user modeling module is well-defined, enabling 
it to be used in a variety of systems with little or no 
customization. 
Future work in user modeling for natural language 
systems should focus in two directions: establishing 
how user models should be used in systems that com- 
municate in natural language, and determining how user 
models can be built more effectively. Many authors 
have emphasized the need for user models in certain 
contexts, or have demonstrated that the availability of 
user model information can improve the behavior of a 
system. This work needs to be extended to identify 
what information applications will expect a user model 
to have, how that information should be provided to the 
application, and when the information needs to be 
available. Answers to these questions will help define 
the services that a user modeling component must 
provide. 
The second focus of research should be on building 
user models. This work could progress in two ways. 
First, the task of explicitly building user models (such as 
building stereotypes) could be made easier. Research in 
this area seems to parallel efforts to find better ways to 
acquire knowledge for knowledge bases from experts. 
However, if general user modeling modules that can 
Computational Linguistics, Volume 14, Number 3, September 1988 19 
Kass and Finin Modeling the User in Natural Language Systems 
function in diverse systems are to be built, the focus 
must be placed on the second approach: implicit user 
model acquisition. In this regard, a user modeling 
module could be general either with respect to the 
underlying domain or to the type of interaction. At this 
time, domain generality seems both a useful and prac- 
tical goal. The work described in Kass (1987a) and Kass 
and Finin (1987c) is a beginning in this area, presenting 
a set of domain general user model acquisition rules for 
cooperative consultation situations. 
User modeling is not an easy task. Effective user 
modeling requires sophisticated knowledge representa- 
tion, acquisition, and reasoning abilities--no wonder 
user modeling is such a new field. On the other hand, 
advances in any of these areas should provide immedi- 
ate benefits to user modeling. Thus progress in some of 
the fundamental areas of AI can result in progress in 
user modeling as well. 
ACKNOWLEDGEMENTS 
This work was partially supported by grant ARMY/DAAG-29-84-K- 
0061 from the Army Research Office, grant DARPA/ONR-N00014- 
85-K-0807 from DARPA, and a grant from the Digital Equipment 
Corporation. 
NOTES 
Authors' current addresses: 
Robert Kass, Center for Machine Intelligence, 2001 Commonwealth 
Blvd., Ann Arbor, MI 48105; 
Tim Finin, Unisys Paoli Research Center, P.O. Box 517, Paoli, PA 
19301. 
1. The use of such "bug libraries" has proven very successful in 
student modeling for intelligent tutoring systems. (Brown and 
Burton 1978, Sleeman 1982, Johnson and Soloway 1984) are 
examples of just a few intelligent tutoring systems that profitably 
employ this idea. 
2. There is an unfortunate conflict in terminology here. Sparck Jones 
uses the term "agent" in the sense of an individual who performs 
a task for another. Thus for Sparck-Jones the agent is the actual 
individual interacting with the system. Hence in our terminology 
the system may have agent models for both Sparck-Jones's 
"agent" and "patient," with the model for the individual Sparck- 
Jones calls the "agent" actually being a user model. 
3. Both the overlay and perturbation models were developed in 
work on intelligent tutoring systems. The overlay model was first 
defined by Carr and Goldstein (1977) and used in their Wumpus 
Advisor (WUSOR) user model, although Carbonell (1970) used an 
overlay technique in the SCHOLAR program, considered to be 
the first of the intelligent tutoring systems. A perturbation model 
was used by Brown and Burton in representing bugs students had 
in learning multicolumn subtraction (Brown and Burton 1978) and 
has since been used by many others. See Sleeman and Brown 
(1982) for a collection of seminal papers on intelligent tutoring 
sy,ltems, or Kass (1987b) for a look at user modeling for intelligent 
tutoring systems. 
4. This is how acceptance attitudes were implemented in VIE-DPM. 
A wider range of values for the acceptance attitudes, such as a 
four-valued logic or numeric weights, could easily be used in- 
stead. 
5. Although it is conceivable that each interaction with an individual 
user might refine the generic model of all users in some way. Thus 
such a user model would converge on the "average user" after 
many sessions. 
6. The terms used in a user's statements also provide information 
about beliefs of the user, but not as much as one might hope. At 
first glance, it seems that if the user makes use of a word, he has 
knowledge about the concept to which that word refers. Most of 
the time this is true. However, people will sometimes use a term 
that they really don't understand, simply because others have 
used it. Inferences based simply on the use of terms should be 
made with care (or with a low level of trust). 
7. A very clever system might even be able to incorporate questions 
from the user modeling module into questions from the applica- 
tion in an attempt to meet two needs simultaneously. 
8. The first three issues are suggested by Sridharan in Sleeman et al 
(1985). 

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Kass and Finin Modeling the User in Natural Language Systems 
