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<Paper uid="W00-1407">
  <Title>A Strategy for Generating Evaluative Arguments ..</Title>
  <Section position="2" start_page="47" end_page="47" type="metho">
    <SectionTitle>
1 Guidelines from Argumentation Theory
</SectionTitle>
    <Paragraph position="0"> An argumentation strategy specifies what content should be included in the argument and how it should be arranged. This comprises several decisions: what represents supporting (or opposing) evidence for the main claim, where to position the main claim of the argument; what supporting (or opposing) evidence to include andhow to order it, and.how to order supp6rfing and opposing evidence with respect to each other.</Paragraph>
    <Paragraph position="1"> Argumentation theory has developed guidelines specifying how these decisions can be effectively made (see (Mayberry and Golden 1996; Miller and Levine 1996; Corbett and Connors 1999; McGuire 1968) for details; see also (Marcu 1996) for an alternative discussion of some of the same guidelines).</Paragraph>
    <Paragraph position="2"> (a) What represents supporting (or opposing) evidence for a claim - Guidelines for this decision vary depending on the argument type.</Paragraph>
    <Paragraph position="3"> Limiting our analysis to evaluative arguments, argumentation theory indicates that supporting and opposing evidence should be identified according to a model of the reader's values and preferences. For instance, the risk involved in a game can be used as evidence for why your reader should like the game, only if the reader likes risky situations.</Paragraph>
    <Paragraph position="4"> (b) Positioning the main claim - Claims are often presented up front, usually for the sake of clarity. Placing the claim early helps readers follow the line of reasoning. However, delaying the claim until the end of the argument can be effective, particularly when readers are likely to find the claim objectionable or emotionally shattering.</Paragraph>
    <Paragraph position="5">  (c) Selecting supporting (and opposing) evidence - Often an argument cannot mention all  the available evidence, usually for the sake of brevity. Only strong evidence should, be presented in detail, whereas weak evidence should be either briefly mentioned or omitted entirely.</Paragraph>
    <Paragraph position="6"> (d) Arranging/Ordering~supporiing evicleiTce Typically the strongest support should be presented first, in order to get at least provisional agreement from the reader early on. If at all possible, at least one very effective piece of supporting evidence should be saved for the end of the argument, inorder to leave the reader with a final impression of the argument's strength. This guideline proposed in (Mayberry and Golden 1996) is a compromise between the climax and the anti-climax approaches discussed in (McGuire 1968).</Paragraph>
    <Paragraph position="7"> (e) Addressing and ordering the counterarguments (opposing evidence) - There ........ * .~ar~ . ~three,.~options. ~ .for, :Ihis~ :.ateeision: not ~to .... mention any counterarguments, to acknowledge them without directly refuting them, to acknowledge them and directly refuting them.</Paragraph>
    <Paragraph position="8"> Weak counterarguments may be omitted.</Paragraph>
    <Paragraph position="9"> Stronger counterarguments should be briefly acknowledged, because that shows the reader that you are aware of the issue's complexity; and it also contributes to the impression that you are reasonable and broad-minded. You may need to refute a counterargument once you have acknowledged it, if the reader agrees with a position substantially different from yours.</Paragraph>
    <Paragraph position="10"> Counterarguments should be ordered to minimize their effectiveness: strong ones should  be placed in the middle, weak ones upfront and at the end.</Paragraph>
    <Paragraph position="11"> (09 Ordering supporting and opposing evidence - A preferred ordering between supporting and  opposing evidence appears to depend on whether the reader is aware of the opposing evidence. If so, the preferred ordering is opposing before supporting, and the reverse otherwise.</Paragraph>
    <Paragraph position="12"> Although these guidelines provide useful information on the types of content to include in an evaluative argument and how to arrange it, the design of a computational argumentative strategy based on these guidelines requires that the concepts mentioned in the guidelines be formalized in a coherent computational framework. This includes: explicitly representing the reader's values and preferences (used in guideline a); operationally defining the term &amp;quot;objectionable claim v (used in guideline b) through a measure of the discrepancy between the readerrs-initial positionand-the argument's main claim2; providing a measure of evidence strength (needed in guidelines c, d, and e); and</Paragraph>
  </Section>
  <Section position="3" start_page="47" end_page="48" type="metho">
    <SectionTitle>
3 An operational definition for &amp;quot;emotionally
</SectionTitle>
    <Paragraph position="0"> shattering&amp;quot; is outside the scope of this paper.</Paragraph>
    <Paragraph position="1">  representing whether the reader is or is not aware of certain facts (needed in guideline tO.</Paragraph>
  </Section>
  <Section position="4" start_page="48" end_page="49" type="metho">
    <SectionTitle>
2 From Guidelines to the Argumentation
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="48" end_page="48" type="sub_section">
      <SectionTitle>
Strategy
</SectionTitle>
      <Paragraph position="0"> We assume that the reader's values and preferences are represented as an additive multiattribute value function (AMVF), a conceptualization based on multiattribute utility theory (MAUT)(Clemen 1996). Besides being widely used in decision theory (where they were originally developed), conceptualizations based on MAUT have recently become a common choice in the field of user modelling (Jameson, Schafer et al. 1995). Similar models are also used in Psychology, in the study of consumer behaviour (Solomon 1998).</Paragraph>
    </Section>
    <Section position="2" start_page="48" end_page="49" type="sub_section">
      <SectionTitle>
2.1 Background on AMVF
</SectionTitle>
      <Paragraph position="0"> An AMVF is a model of a person's values and preferences with respect to entities in a certain class. It comprises a value tree and a set of component value functions, one for each attribute of the entity. A value tree is a decomposition of the value of an entity into a hierarchy of aspects of the entity 3, in which the leaves correspond to the entity primitive a~ributes (see Figure 1 for a simple value tree in the real estate domain). The arcs of the tree are weighted to represent the importance of the value of an objective in contributing to the value 3 In decision theory these aspects are called objectives. For consistency with previous work, we will follow this terminology in the remainder of the paper.</Paragraph>
      <Paragraph position="1"> of its parent in the tree (e.g., in Figure 1 location is more than twice as important as size in determining the value of a house). Note that the sum of the weights at each level is equal to 1. A component value function for an attribute expresses the preferability of each attribute value as a number in the \[0,1\] interval. For instance, in Figure 1, neighborhood n2 has preferability 0.3, and a distance-from-park of 1 mile has preferability (1 - (1/5&amp;quot; 1))=0.8.</Paragraph>
      <Paragraph position="2"> Formally, an AMVF predicts the value v(e) of an entity e as follows:  v(e) = v(xl ..... x,) = Y~w, v/x9, where - (x/ ..... x,,) is the vector of attribute values for an entity e - Vattribute i, v, is the component value function, which maps the least preferable x, to 0, the most preferable to I, and the other x, to values in \[0,1\] - w, is the weight for attribute i, with 0_&lt; w, _&lt;1 and Zw, =1 - w, is equal to the product of all the weights  from the root of the value tree to the attribute i A function vo(e) can also be defined for each objective. When applied to an entity, this * - function &amp;quot;returns ~the value of the entity with respect to that objective. For instance, assuming the value tree shown in Figure 1, we have: v,. ........ (e) = = (0.4 * V~,,h~orhooa (e)) + (0.6 * vl~,~,_/,~,,,_r~rk (e)) Thus, given someone's AMVF, it is possible to compute how valuable an entity is to that  individual. Furthermore, it is possible to compute how valuable any objective (i.e., any aspect of that entity) is for that person. All of these values are expressed as a number in the interval \[0, i \].</Paragraph>
    </Section>
    <Section position="3" start_page="49" end_page="49" type="sub_section">
      <SectionTitle>
2.2 Computational Definition of Concepts
Mentioned in Guidelines
</SectionTitle>
      <Paragraph position="0"> Presenting an evaluative argument is an attempt to persuade the reader that a value judgment applies to a subject. The value judgement, also called the argumentative intent, can either be positive (in favour of the subject), or negative (against the subject) 4. The subject can be a single entity (e.g., &amp;quot;This book is very good&amp;quot;), the difference between two entities (e.g., &amp;quot;City-a is somewhat better than city-b'), or any other form of comparison among entities in a set (e.g., &amp;quot;This city is the best in North America&amp;quot;).</Paragraph>
      <Paragraph position="1"> Guideline (a) - Given the reader's AMVF, it is straightforward to establish what represent supporting or opposing evidence for an argument with a given argumentative intent and a given subject. In fact, if the argumentative intent is positive, objectives for which the subject has positive value can be used as supporting evidence, whereas objectives for which the subject has a negative value can be used as opposing evidence (the opposite holds when the argumentative intent is negative). The value of different subjects is measured as follows. If the subject is a single entity e, the value of the subject for an objective o is vo(e), and it is positive when it is greater than 0.5, the midpoint of \[0,1\] (negative otherwise). In contrast, if the subject is a comparison between two entities (e.g., v(ed &gt; v(e_,)), the value of the subject for an objective o is \[vo(e9 - Vo(e,)\], and it is positive when it is greater than 0 (negative otherwise).</Paragraph>
      <Paragraph position="2"> Guidelines (b) - Since argumentative intent is a value judgment, we canreasonab\[y assume that instead of being simply positive or negative, it may be specified more precisely as a number in the interval \[0,1\] (or as a specification that can be normalized in this interval), Then, the term</Paragraph>
    </Section>
  </Section>
  <Section position="5" start_page="49" end_page="52" type="metho">
    <SectionTitle>
4 Arguments can also be neutral. However, in this
</SectionTitle>
    <Paragraph position="0"> paper we do not discuss arguments with a neutral argumentative intent.</Paragraph>
    <Paragraph position="1"> &amp;quot;objectionable claim&amp;quot; can be operationally defined. If we introduce a measure-ofdiscrepancy(MD) as the absolute value of the difference between the argumentative intent and the reader's expected value of the subject before the argument is presented (based on her AMVF), a claim becomes more and more &amp;quot;objectionable!' for a reader as MD moves from 0 to 1.</Paragraph>
    <Paragraph position="2"> ,~,. ,:,_.~.uidelin~;,(c) ~(d), (e). ~,:~The,,~strength oPS the .... evidence in support of (or opposition to) the main argument claim is critical in selecting and organizing the argument content. To define a measure of the strength of support (or opposition), we adopt and extend previous work on explaining decision theoretic advice based on an AMVF. (Klein 1994) presents explanation strategies (not based on argumentation theory) to justify the preference of one alternative from a pair. In these strategies, the compellingness of an objective measures the objective's strength in determining the overall value difference between the two alternatives, other things being equal.</Paragraph>
    <Paragraph position="3"> And an objective is notably-compelling? (i.e., worth mentioning) if it is an outlier in a population of objectives with respect to compeilingness. The formal definitions are:</Paragraph>
    <Paragraph position="5"> - o is an objective, a/and a2 are alternatives, refo is an ancestor of o in the value tree - w(o, refo) is the product of the weights of all the links from o to refo - vo is the component value function for leaf objectives (i.e., attributes), and it is the recursive evaluation over children(o) for nonleaf objectives notably-compelling?(o, opop. al, a2, refo) \[ compellingness(o, al, a2, refo) \[ &gt;px+ko'x, where - o, al, a2 and refo are defined as in the previous Def; opop is an objective population (e.g., siblings(o)), and I opopl &gt;2 - pe opop; xeX = \[compellingness(p, al, a_~, refo) l - gx is the mean of X, ~x is the standard  deviation and k is a user-defined constant We have defined similar measures for arguing the value of a single entity and we named them s-compellingness and s-notably-compelling?.  An objective can be s-compelling either because of its strength or because of its weakness in contributing to the value of an alternative. So, if m~ measures how much the value of an objective contributes to the overall value difference of an alternative from the worst possible case 5 and m2 measures how much the value of an objective contributes to the overall value difference of the is either a single entity or a pair of entities in the domain of interest. Root can be any objective in the value tree for the evaluation (e.g., the overall value of a house, its location, its amenities). ArgInt is the argumentative intent of the argument, a number in \[0,1 \]. The constant k, part of the definitions of notably-compelling? and snotably-compelling?, determines the degree of :, .,,alternative ,from., th~_b~st:,possible:~ease,:.~e-: :,~ eoneisenessofithe;argument,,, The~Express-Value define s-compellingness as the greatest of the two quantities m~ and m2. Following the terminology introduced in the two previous Equations we have:</Paragraph>
    <Paragraph position="7"> We give to s-notably-compelling? a definition analogous to the one for notably-compelling?</Paragraph>
    <Paragraph position="9"> whether the reader is or is not aware of certain facts. We assume this information is represented separately.</Paragraph>
    <Section position="1" start_page="50" end_page="50" type="sub_section">
      <SectionTitle>
2.3 The Argumentation Strategy
</SectionTitle>
      <Paragraph position="0"> We have applied the formal definitions described in the previous section to develop the argumentative strategy shown in Figure 2. The strategy is designed for generating honest and balanced arguments, which present an evaluation of the subject equivalent to the one you would expect the reader to hold according to her model of preferences (i.e., the argumentative intent is equal to the expected value, so MD=0) 6. We now examine the strategy in detail, after introducing necessary, terminology. The subject  whose argumentative intent was-greater (or lower) than the expected value, could also be defined in our framework. However, this strategy should boost the evaluation of supporting evidence and include only weak counterarguments, or hide them overall (the opposite if the target value was lower than the expected value) function, used at the end of the strategy, indicates that the objective applied to the subject must be realized in natural language with a certain argumentative intent.</Paragraph>
      <Paragraph position="1"> In the first part of the strategy, depending on the nature of the subject, an appropriate measure of evidence strength is assigned, along with the appropriate predicate that determines whether a piece of evidence is worth mentioning. After that, only evidence that is worth mentioning is assigned as supporting or opposing evidence by comparing its value to the argument intent. In the second part, ordering constraints from argumentation theory are applied 7. Notice that we assume a predicate Aware that is true when the user is aware of a certain fact, false otherwise. Finally, in the third part of the strategy, the argument claim is expressed in natural language. The opposing evidence (i.e., ContrastingSubObjectives), that must be considered, but not in detail, is also expressed in natural language. In contrast, supporting evidence is presented in detail, by recursively calling the strategy on each supporting piece of evidence.</Paragraph>
    </Section>
    <Section position="2" start_page="50" end_page="52" type="sub_section">
      <SectionTitle>
2.4 Implementation and Application
</SectionTitle>
      <Paragraph position="0"> The argumentation strategy has been implemented as a set of plan operators. Using these operators the Longbow discourse planner (Young and Moore 1994) selects and arranges the content of the argument. We have applied our strategy in a system that serves as a real-estate personal assistant (Carenini 2000a). The system presents information about houses available on the market in graphical format. The user explores this information by means of interactive techniques, and can request a natural 7 The steps in the strategy are marked with the guideline they are based on.</Paragraph>
      <Paragraph position="1">  language evaluation of any house just by dragging the graphical representation of the house to a query button. The evaluative arguments generated by the system are concise, properly arranged and tailored to the user's preferences s. For sample arguments generated by our strategy see (Carenini 2000b) in this proceedings.</Paragraph>
      <Paragraph position="2"> (Elzer, Chu-Carroli et al. 1994; Chu-Carroll and Carberry 1998) studied the generation of evaluative arguments in the context of collaborative planning dialogues. Although they also adopt a qualitative measure of evidence strength, when an evaluation is needed this measure is mapped into numerical values so that preferences can be compared and combined ..... ...= :- ....... :,, ~- .-: :-;.~ ~,xnore:.:e:ffeeti~ely:,Rl~ve.~tC/,~ittr,,respeet =-~tO:our</Paragraph>
    </Section>
  </Section>
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