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<Paper uid="W06-1806">
  <Title>Natural Language Understanding using Temporal Action Logic</Title>
  <Section position="7" start_page="0" end_page="0" type="relat">
    <SectionTitle>
6 Related Work
</SectionTitle>
    <Paragraph position="0"> An early and very impressive demonstration of natural language understanding was the SHRDLU system (Winograd, 1971). NL1 improves upon SHRDLU by using modern HPSG grammars instead of CFG grammars and declarative instead of procedural knowledge representation, but still falls short of the complexity of correctly executed dialogues. Though we are confident that our more general system architecture will catch up in the long run.</Paragraph>
    <Paragraph position="1"> More recent work was carried out in the CLARE project (Alshawi et al., 1992) to provide natural language question answering interfaces for databases. The Core Language Engine parses questions into a quasi-logical form, aptly called QLF, that is interpreted and reasoned about in the context of background knowledge. In addition to 34 KRAQ06 the choice of parsing environment and intermediate form, two differences from NL1 are that we explicitly avoid committing to a specific reasoning paradigm while CLARE is based on the logic programming paradigm, and that the scale of the CLARE project is simply vastly larger than ours.</Paragraph>
    <Paragraph position="2"> The idea of using other theorem proving techniques than logic programming to aid natural language understanding has also been explored previously. The work in (Blackburn et al., 1998) uses Dynamic Logic as the semantical representation and a translation to a fragment of first-order logic together with the Bliksem theorem prover as the reasoning mechanism. The feasibility of the set-up is demonstrated by using it to resolve discourse ambiguities. Our approach is similar in the application of an automated theorem prover after a translation step, but our background knowledge is encoded in Temporal Action Logic, which endows the system with the power to perform actions and reason about their effects.</Paragraph>
    <Paragraph position="3"> Other systems, such as the architecture described in (Allen et al., 2001), deal with dialogues in realistic scenarios where human users want to interact with the system as fluently as possible to accomplish a task. Such efforts strive for human-like behaviour while we consider the ultimate goal to be human-level, but possibly very artificial, behaviour and hypothesize that many issues in human dialogues might well be ignored. Our interest lies not in modelling dialogue phenomena, but in using knowledge representation techniques for natural language understanding in a system with a dialogue interface.</Paragraph>
  </Section>
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