Natural language generation (NLG) is first and foremost a reasoning task. In this reasoning, a system plans a communicative act that will signal key facts about the domain to the hearer. In generating action descriptions, this reasoning draws on characterizations both of the causal properties of the domain and the states of knowledge of the participants in the conversation. This dissertation shows how such characterizations can be specified declaratively and accessed efficiently in NLG. The heart of this dissertation is a study of logical statements about knowledge and action in modal logic. By investigating the proof-theory of modal logic from a logic programming point of view, I show how many kinds of modal statements can be seen as straightforward instructions for computationally manageable search, just as Prolog clauses can. These modal statements provide sufficient expressive resources for an NLG system to represent the effects of actions in the world or to model an addressee whose knowledge in some respects exceeds and in other respects falls short of its own. To illustrate the use of such statements, I describe how the SPUD sentence planner exploits a modal knowledge base to

Abstract: In this overview paper, I will attempt to identify and describe some of the common threads that tie together work in reasoning about knowledge in such diverse fields as philosophy, economics, linguistics, artificial intelligence, and theoretical computer sciencce. I will briefly discuss some of the more recent work, particularly in computer science, and suggest some lines for future research.

The knowledge required for action is generally indexical rather than objective. For example, a robot that knows the relative position of an object is generally able to go and pick it up; he need not know its absolute position. Agents may have very incomplete knowledge of their situation in terms of what objective facts hold and still be able to achieve their goals. This paper presents a formal theory of knowledge and action, embodied in a modal logic, that handles the distinction between indexical and objective knowledge and allows a proper specification of the knowledge prerequisites and effects of action. Several kinds of robotics situations involving indexical knowledge are formalized within the framework; these examples show how actions can be specified so as to avoid making excessive requirements upon the knowledge of agents. 1 Introduction 1.1 Motivation Designing autonomous robots or other kinds of agents that interact in sophisticated ways with their environment is hard; yo...

In their attempt to model and reason about the beliefs of agents, artificial intelligence (AI) researchers have borrowed from two different philosophical tradi-tions regarding the folk psychology of belief. In one tradition, belief is a relation between an agent and a proposition, that is, a propositional attitude. Formal analyses of propositional attitudes are often given in terms of a possible-worlds semantics. In the other tradition, belief is a relation between an agent and a sen-tence that expresses a proposition (the sentential approach). The arguments for and against these approaches are complicated, confusing, and often obscure and unintelligible (at least to this author). Nevertheless strong supporters exist for both sides, not only in the philosophical arena (where one would expect it), but also in AI. In the latter field, some proponents of posslble-worlds analysis have attempted to remedy what appears to be its biggest drawback, namely the assumption that an agent believes all the logical consequences of his or her beliefs. Drawing on initial work by Levesque, Fagin and Halpern define a logic of 9eneral awareness that superimposes elements of the sentential approach on a possible-worlds framework. The result, they claim, is an appropriate model for resource-limited believers. We argue that this is a bad idea: it ends up being equivalent to a more com-plicated version of the sentential approach. In concluding we cannot refrain from adding to the debate about the utility of possible-worlds analyses of belief.

The paper investigates several approaches to user modeling in natural-language dialog systems. First, reasons are pointed out why user modeling has become so important in the last few years, and definitions are proposed for the notions of 'user model ' and 'user modeling component'. Then, techniques for constructing user models in the course of a dialog are presented and recent proposals for representing a wide range of assumptions about a user's beliefs and goals in a system's knowledge base are surveyed. Examples for the application of user models in systems developed to date are presented, and some social implications are discussed. Finally, unsolved problems like coping with collective beliefs or resource-limited processes are investigated, and prospects for application-oriented research are outlined. 1.

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The goal of this paper is to present a new family of formal systems, so called multilanguage systems (ML-systems), which allow the use of multiple distinct first order languages and inference rules whose premises and consequences need not belong to the same language. ML-systems are argued to formalize naturally and elegantly notions like belief, knowledge and, more in general, various forms of propositional attitudes. Some instances of ML-systems are defined and proved equivalent to the modal logic K and some of Konolige's logics for belief.

As part of an effort to define a unified formal semantics for beliefs, desires and action, this paper sketches a model theory for the axiological aspects of agent theory: hedonic states, likes, goals and values. Particular attention is paid to modelling the intensity of likes. The main intuition underlying the model theory is that the axiological aspects of agent theory can be modelled through computational generalisations of physical dynamics. Computational analogues of force, mass and potential are offered. Introduction An important part of agent theory appears to be the notion of desires. Several formulations of agent theory have adopted beliefs, desires and intentions as a set of basic notions (the so-called BDI models). However, to our knowledge, so far relatively little has been said explicitly in the AI literature about a theory of desires (Cohen and Levesque, 1985 and in press, Moore, 1985a; Kiss, 1988, Shoham, 1989). This paper takes some initial steps towards the explicit f...

We investigate how belief change in cooperative dialogues can be handled within a modal logic of action, belief, and intention. We first review the main approaches of the literature, and point out some of their shortcomings. We then propose a new framework for belief change. Our basic notion is that of a contextual topic: we suppose that we can associate a set of topics with every agent, speech act, and formula. This allows us to talk about an agent's competence, belief adoption, and belief preservation. Based on these principles we analyse the agents ' belief states after a speech act. We illustrate our theory by a running example. 1

Commonsense causal discourse requires a language with which to express varying degrees of causal connectedness. This paper presents a commonsense language for reasoning about action and causation whose semantics is expressed by way of counterfactuals. Causal relations are analyzed along several dimensions including notions of resource consumption, degree of responsibility, instrumentality, and degree of causal contribution. Grounding the semantics in a level of counterfactual reasoning is shown to play an important role in constraining the set of allowable event descriptions instantiating reports expressed by any of the relations in the language. These ideas are also applied to a causal analysis of rational action: by adopting an explanatory stance, one can characterize action through descriptions that refer to causal connections between mental states and actions. Such a causal analysis resolves some well-known difficulties in correctly ascribing agency and intentionality. Finally, an ...