recent years, much research has been aimed at the study of episodic reminding and its relationship to the process of understanding in both man and machines. Because of the ill defined nature of the functionality of episodic remindings in the understanding process, however, little progress has been made in uncovering the nature of the memory organization that supports these remindings or how they are used to help in understanding. This paper will present a functional view of episodic reminding from the perspective of planning rather than understanding. Because of the clearer functionality of planning, a more straightforward view of the use of these remindings and the memory organization that supports them is possible. This paper will discuss three situations in which episodic memories can be used in planning: problem anticipation, plan construction and plan repair. The memory organization that each of these uses implies will also be discussed. This view of memory will be presented in terms of a case-based planner, CHEF, which makes extensive use of episodic remindings in constructing new plans.

This paper describes POLYA, a computer program that writes geometry proofs. POLYA actively collects features from a geometry diagram on the basis of which it recognizes and applies knowledge from known examples. We present a vocabulary of visual targets, results, and actions to support incremental parsing of diagrams. W e also show how scripts can be used to organize visual actions into useful sequences. W e show how those sequences can be used to parse diagrams and instantiate proofs. Finally, we show how scripts represent the implicit spatial knowledge conveyed by examples.

This symposium highlights what Cognitive Science has gained from recent research in cognitive modelling. The symposium brings together several research teams that share the methodological approach of refining computational models through empirical studies of human problem solvers. The models include the A C M E constraint satisfaction network model, the S M E model of analogy, the CASCADE process model of problem solving, and Case-Based Reasoning models. These projects represent a diverse set of approaches to computational modelling, and embody very different architectures for cognition. While each approach has contributed unique findings, these models also share a unifying set of assumptions about how cognitive constraints structure problem solving processes. By presenting these diverse approaches to cognitive modeling within a single session, we plan to promote discussion of the principles underlying all such models, and highlight the progress that can be made by combining constraints from computation and cognition.

Our project concerns the definition of a content theory of action appropriate for agents that act in a multi-agent environment and its implementation in a multi-agent system. Such a theory has to explain what agents know and how they use this knowledge; it has to identify what resources are available to the agents when they must decide on an action; it has to allow agents to reason and engage in concrete activity in their domain. More important for our research, and in contrast with numerous works in Distributed Artificial Intelligence, such a vocabulary must provide a basis for agents to decide and learn when, how or with whom they should cooperate. In this paper we suggest a vocabulary of interactions for intelligent agents. Our vocabulary attempts to do justice to the situated character of action with respect to the disparate but related dimensions of physicality, sociality and experience.

Solving the multiple goals problem has been a major issue in Artificial Intelligence models of planning (Sussman, 1975; Sacerdoti, 1975; Wilensky, 1978; Wilensky, 1980; Wilensky, 1983; Carbonell, 1979); however, most models have assumed that the best plan for a set of goals to be satisfied in coivj unction will zirise from a simple combination of the best individual plans for each goal. However, human planners seem to possess an ability to look at a set of goals, and charMterize them as a whole, instezui of as a collection of individual goals (Hayes-Roth and Hayes-Roth, 1979). In this paper, we introduce the notion of indexing complex multiple-goal plans in terms of the interactions between the goals that they satisfy. W e present the vocabulary requirements for representing the causality behii^ goal interactions, the general planning strategies used to resolve these interactions, and the specific plans based on these more general resolution strategies that are instantiated in the actual planning problem.

Classical plan preconditions implicitly play a dual role, both documenting the facts necessary for a plan to be sound and listing the conditions under which it should be used. As the closed-world assumption is relaxed these two roles begin to diverge, particularly when attempts are made to use plans in situations other than those for which they were originally constructed. Rosenschein and Kaelbling exploit one aspect of the divergence by suggesting that some logiceil preconditions can be considered in the design phase of building an agent, but "compiled away" so that the agent need not explicitly consider them [Rosenschein and Kaelbling, 1986]. W e suggest an alternative view whereby an agent can explicitly reason and learn about which conditions are the best cues for employing standard plans, and discuss the idea in the context of the Runner project.

In earlier work, we proposed a memory model that would facilitate the detection of opportunities to satisfy suspended goals. In this opportunistic memory model, suspended goals are indexed under feature sets that are predictive of the presence of the opportunity, and which are likely to be encountered in the normal course of future activity. The functional benefit of such encoding depends crucially on the particular vocabulary of features used, the costs of their detection, and the overlap of features relevant to the pursuit of different goals. In this paper we investigate the feature vocabulary implied by recent work on visual search [Treisman, 1985, Tsotsos, 1990], and its use in indexing goals suspended due to the lack of a particular object.

Over the past few years there has been a growing interest in the notion of using causal explanations in both learning ([Dejong and Mooney, 1986] [Mitchell et a/., 1986]) and planning ([Hammond, 1989], [Hammond, 1987] and [Simmonsand Davis, 1987]). The study of how complex causal explanations can be used in learning has turned into something of a cottage industry in AI; however, little attention has paid to how explanations may be constructed. In this paper, we will examine some of the current proposals concerning the process of explanation, augment them with a few ideas of our own, and suggest a new, more strategic level of knowledge about explanations that can be used to guide the explanation process. In particular, we are interested in the problems involved with integrating rule-based methods of explanation construction with memory-based approaches.

This paper explores the relationship between human activity and remindings. W e argue that the type of activity in which a person is engaged influences the kinds of features that trigger remindings, and, conversely, that remindings can change situated behavior over time. W e argue further that the types of indices used in memory are not uniform, but depend upon the nature of the task that a person is engaged in as well as his history of interaction with the world.

Recent work in planning has rejected the assumption of a closed, stable world, and the associated paradigm of exhaustive preplanning, which encounters serious problems trying to plan in a world where that assumption does not hold. Several alternative strategies have been proposed, responding to these new problems in a variety of ways. W e review this spectrum, finding the various approaches in part incompatible but not bereft of some common themes and complementary strengths. W e suggest factors in the application domain which should influence the appropriate mix, and describe the T R U C K E R project to illustrate some of the problems and benefits in implementing such a mix.