Combining the Practical Syllogism and Planning in Dialogue

We present a dialogue model which relates discourse relations and intentions by reasoning about pragmatic capabilities of dialogue particpants. For that purpose, planning approaches for discourse and application domain are employed. In this way, a computationally tractable version of the pratical syllogism is devised. 1 Rational Dialogues Our goal is to build dialogue systems for rational interaction. Users can interact with the system in a given (ideally open) domain by conducting spoken dialogues. In principle, it should be possible to augment them by other forms of multi-modal interaction like gestures or the selection of items from a menu on a screen. Interactions are called “rational” because we want to apply rationality principles (at the knowledge representation level) to optimally select appropriate communicative actions. We assume that the satisfaction of user goals within the thematic framework of a particular application domain is to be achieved with the help of a dialogue system proper in cooperation with a technical application which we also call the “domain problem solver”. Such a technical application can be an information or reservation system, a system for controlling certain devices, etc. For dialogue modelling, we will follow a planbased approach which has its roots in natural language processing and Artificial Intelligence. It provides the means to conduct taskor goaloriented dialogues which are focussed on accomplishing concrete tasks as mentioned in the introduction. We claim that only a general planning approach enables cooperative response behaviour (pragmatic adequateness, overanswering) and the ability for negotiation. For the reasoning part, i.e. knowledge representation and inference for the interpretation of dialogue as well as for planning to satisfy user goals in the application domain, we insist on a clear commitment to a computational logic framework, in particular description logics. Of course, humans act incoherently and even inconsistently, and common sense reasoning can only to a certain extent be understood in terms of logic, but we are convinced that a coherent and consistent rational reconstruction is the best we can do about it. Such a constructive perspective has the advantage of enabling us to begin with a well understood framework for knowledge representation and reasoning upon which we can attempt to build rule systems for still idealized, but more realistic patterns of argumentation in specific domains. We believe that there is a potential to succeed in a variety of prevailingly instrumentalized contexts as it is the case with technical applications or in forensic argumentation. Taking these claims serious, obviously a variety of complex issues must be addressed within the framework of our dialogue system, as, e.g., intention recognition, cooperativeness, grounding, or sharing plans – to name just a few important ones. For that, we refer to other publications of our group, e.g., (Görz et al., 2002; Bücher et al., 2002; Ludwig et al., 2002). In this paper, we are adressing only one specific problem: 2 Choices in Rational Dialogues In a series of papers, Asher and Lascarides introduced Segmented Discourse Representation Theory (SDRT) as an extension to Kamp’s DRT and used it to model dialogue by combining compositional semantics, discourse structure and information about the participant’s intentional states. The reason for their approach is to overcome inferential problems encountered in AI approaches to plan recognition in dialogue systems (Asher and Lascarides, 1997). A discourse is represented in the form of a SDRS (Segmented Discourse Representation Structure) which is a recursive structure of DRSs, i.e. semantic representations of linguistic expressions on the clause level, connected by rhetorical relations. Examples for such relations are explanation, contrast, or continuation. For discourse analysis, the authors propose to construct two different SDRSs, one for each discourse participant, in order to take their different cognitive states – we prefer to talk about epistemic states – into account. As a formal means for reasoning from the epistemic states of participants to what they say and vice versa, Asher and Lascarides introduce a version of Aristotle’s Practical Syllogism: it “states that normally, people intend to do things that they believe help them achieve their goals”1. Under the rationality assumption for dialogues we propose for the multi-agent framework and the applications we work on within it, we will use a tighter, i.e. monotonic version of the Practical Syllogism by dropping “normally”: If (a) participant B wants ψ and believes ¬ψ, and (b) B believes he can infer ψ from his knowledge base augmented by φ, and moreover φ isB’s choice for achieving ψ, then (c) B intends φ. There are two reasons for tightening up the Practical Syllogism into a monotonic version. The first is a technical, albeit quite important one: As already mentioned, in order to achieve a running system we committed ourselves to a particular computational logic framework, description logics, where the decidability – and furthermore an (Asher and Lascarides, 1997), p. 16 of the preprint version efficient implementation – of the inference problem is the foremost issue. Non-monotonicity in general would mean to lose decidability. The second reason is that in any specific application situation in fact the choice of an appropriate action is monotonic – either it is available or not. If not, a normality assumption is of little help. This decision can be modelled by a Reiter-style default rule, but it is a priori with respect to the application timepoint of the Practical Syllogism: Either counterevidence to the assumption that the chosen action is applicable exists in the actual situation, or it doesn’t. So we have two distinct phases: First we have to check for counterevidence; if it exists, the choice cannot be executed and the Practical Syllogism is not applicable. If there is no counterevidence, we perform a monotonic derivation. Technically, the first step is represented in Abdallah’s FIL calculus (Abdallah, 1995); it allows to express a certain situation description (model) with alternative model extensions where within each extended model we can infer monotonically. The monotonic version of Practical Syllogism now reads as follows: (a) (WB(ψ) ∧ BB(¬ψ) ∧ (b) BB((φ→ ψ) ∧ choiceB(φ, ψ))