We represent properties of actions in a logic programming language that uses both classical negation and negation as failure. The method is applicable to temporal projection problems with incomplete information, as well as to reasoning about the past. It is proved to be sound relative to a semantics of action based on states and transition functions. 1 Introduction This paper extends the work of Eshghi and Kowalski [6], Evans [7] and Apt and Bezem [1] on representing properties of actions in logic programming languages with negation as failure. Our goal is to overcome some of the limitations of the earlier work. The existing formalizations of action in logic programming are adequate for only the simplest kind of temporal reasoning---"temporal projection." In a temporal projection problem, we are given a description of the initial state of the world, and use properties of actions to determine what the world will look like after a series of actions is performed. Moreover, the existing ...

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

Action languages serve for describing changes that are caused by performing actions. We define a new action language C, based on the theory of causal explanation proposed recently by McCain and Turner, and illustrate its expressive power by applying it to a number of examples. The mathematical results presented in the paper relate C to the Baral---Gelfond theory of concurrent actions. Introduction Representing properties of actions has been the subject of many papers and two recent books (Sandewall 1995), (Shanahan 1997). One direction of work makes use of "action languages," such as A (Gelfond & Lifschitz 1993) and its dialects. An action language serves for describing the effects of actions on fluents. The meaning of a set of propositions in an action language can be represented by a "transition diagram." In this paper we define a new action language C, based on the theory of causal explanation proposed in (McCain & Turner 1997) and extended in (Lifschitz 1997a). The main i...

We present a formalization of the blocks world on the basis of the situation calculus and circumscription, and investigate its mathematical properties. The main theorem describes the effect of the circumscription which solves the frame problem in the presence of ramifications. The theorem is quite general, in the sense that it is applicable not only to the blocks world, but to a whole class of domains involving situations and actions. Its statement does not mention anything specific for the domain of blocks. Instead, it lists assumptions about purely formal, mostly syntactic, properties of the axiom set. 1 INTRODUCTION In this paper we present a formalization of the blocks world on the basis of the situation calculus and circumscription, and investigate its mathematical properties. The formalization is not particularly original; it is based on the approach of [ Baker, 1989 ] 1 , and is in some ways similar to the formulation from Section 6 of [ Baker and Ginsberg, 1989 ] . One diffe...

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vii Chapter 1 Introduction 1 1.1 Background : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Research Approach and Significance : : : : : : : : : : : : : : : : : : : : 1 1.3 Overview of Contributions : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.4 Organization : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 Chapter 2 Motivation and Background 4 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.2 Reasoning About Actions : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.3 Temporal Formalisms for Reasoning About Actions : : : : : : : : : : : : 5 2.3.1 Situation Calculus : : : : : : : : : : : : : : : : : : : : : : : : : 5 2.3.2 Other Formalisms : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.4 Problems in Reasoning About Actions : : : : : : : : : : : : : : : : : : : 6 2.4.1 The Frame, Qualification and Ramification Problems : : : : : : : 6 2.4.2 Different Aspects of Reasoning About Actions : : : : :...

. There has been a growing interest in AI in formal, qualitative models of various mental attitudes, starting with the well-researched concepts of knowledge and belief, and continuing with more exotic notions such as goals and intentions. In this initial survey we take stock of the work carried out so far in this field, including the motivation for this line of research, the mental attitudes considered, those not, the (primarily logical) tools used, and some of the outstanding problems. 1 Introduction In this paper we review work on formal, qualitative, explicit models of mental attitudes, as studied in recent years in AI. By `mental attitudes' we mean notions such as knowledge, belief, desires, goals, and so on -- those notions, usually attributed to humans, which are generally associated with people's mental life. We use the term `mental state' to denote the collection of all mental attitudes of an agent. Why does AI care about theories of mental state? To start with, to the extent ...

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Logical AI involves representing knowledge of an agent’s world, its goals and the current situation by sentences in logic. The agent decides what to do by inferring that a certain action or course of action is appropriate to achieve the goals. We characterize briefly a large number of concepts that have arisen in research in logical AI. Reaching human-level AI requires programs that deal with the common sense informatic situation. Human-level logical AI requires extensions to the way logic is used in formalizing branches of mathematics and physical science. It also seems to require extensions to the logics themselves, both in the formalism for expressing knowledge and the reasoning used to reach conclusions. A large number of concepts need to be studied to achieve logical AI of human level. This article presents candidates. The references, though numerous, to articles concerning these concepts are still insufficient, and I’ll be grateful for more, especially for papers available on the web. This article is available in several forms via

When we reason about change over time, causation provides an implicit preference: we prefer sequences of world states in which one world state leads causally to the next, rather than sequences in which one world state follows another at random and without causal connections. In this paper, we explore the crucial role that causation plays in our intuitions about temporal reasoning. We examine previous approaches to general temporal reasoning, and their shortcomings, in light of this analysis. We present a new system for causal reasoning , motivated action theory, which builds upon causation as a crucial preference criterion. Motivated action theory solves a broad class of temporal reasoning problems, including the traditional problems of both forward and backward reasoning, and additionally provides a basis for a new theory of explanation. Correspondence to 545 Technology Square Room 811, Cambridge MA 02138; (617) 253-2663; las@ai.mit.edu L. A. Stein and L. Morgenstern Motivated Act...