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Computer-based tutoring/coaching systems have the promise of enhancing the educational value of gaming environments by guiding a student's discovery learning. This paper provides an in-depth view of (i) the philosophy behind such systems, (ii) the kinds of diagnostic modeling strategies required to infer a student's shortcomings from observing his behavior and (iii) the range of explicit tutorial strategies needed for directing the Tutor to say the right thing at the right time. Examples of these issues are drawn for a computer-based coaching system for a simple game-How the West was Won. Our intention in writing this paper is to make explicit the vast amounts of tutorial knowledge required to construct a coaching system that is robust, friendly and intelligent enough to survive in home or classroom use. During the past three years, we have witnessed how subtle the computer-based coaching problem really is.

This paper presents a cognitive model of the planning process. The model generalizes the theoretical architecture of the Hearsay-II system. Thus, it assumes that planning comprises the activities of a variety of cognitive “specialists.” Each specialist can suggest certain kinds of decisions for incorporation into the plan in progress. These include decisions about: (a) how to approach the planning problem; (b) what knowledge bears on the problem; (c) what kinds of actions to try to plan; (d) what specific actions to plan; and (e) how to allocate cognitive resources during planning. Within each of these categories, different specialists suggest decisions at different levels of abstraction. The activities of the various specialists are not coordinated in any systematic way. Instead, the specialists operate opportunistically, suggesting decisions whenever promising opportunities arise. The paper presents a detailed account of the model and illustrates its assumptions with a “thinking aloud” protocol. It also describes the performance of a computer simulation of the model. The paper contrasts the proposed model with successive refinement models and attempts to resolve apparent differences between the two points of view. Cognitive Science 3:275-310.

This chapter focuses on ACT system that embodies the extremely powerful thesis that a single set of learning processes underlies the whole gamut of human learning—from children learning their first language by hearing examples of adult speech to adults learning to program a computer by reading textbook instructions. The computer simulation is called ACT. The ACT theory describes its application to research on abstraction of schemas. In ACT, knowledge is divided into two categories: declarative and procedural. The declarative knowledge is represented in a propositional network similar to semantic network representations.

An algorithm based on state space search is introduced for computing the minimax value of game trees. The new algorithm SSS∗ is shown to be more efficient than α-ß in the sense that SSS∗ never evaluates a node that α-ß can ignore. Moreover, for practical distributions of tip node values, SSS∗ can expect to do strictly better than α-ß in terms of average number of nodes explored. In order to be more informed than α-ß, SSS∗ sinks paths in parallel across the full breadth of the game tree. The penalty for maintaining these alternate search paths is a large increase in storage requirement relative to α-ß.

The determination of subgraph and graph isomorphisms is an important application for the algebraic manipulation of networks of binary constraints. Simplified and streamlined arc consistency and tree search algorithms are introduced, and experimental results show substantial reduction in timings compared with previous algorithms for determining isomorphisms. Several path consistency algorithms, including a new one, have been timed experimentally on isomorphism problems, and found not to be cost effective despite their theoretical appeal. The importance of this result is enhanced by the absence of previously published experimentation with path consistency. A theoretical study of the new path consistency algorithm provides insight into the experimental results.

Human programmers seem to know a lot about programming. This suggests a way to try to build automatic programming systems: encode this knowledge in some machine-usable form. In order to test the viability of this approach, knowledge about elementary symbolic programming has been codified into a set of about four hundred detailed rules, and a system, called PECOS, has been built for applying these rules to the task of implementing abstract algorithms. The implementation techniques covered by the rules include the representation of mappings as tables, sets of pairs, property list markings, and inverted mappings, as well as several techniques for enumerating the elements of a collection. The generality of the rules is suggested by the variety of domains in which PECOS has successfully implemented abstract algorithms, including simple symbolic programming, sorting, graph theory, and even simple number theory.

The distributive computation of constrained optimization problems by networks of locally interconnected simple processors is examined. A general method for designing such networks is described. The design includes the network of interconnections among the participating processors, as well as the iterative computation to be performed by each processor. The application of these results to relaxation techniques in the processing of visual information is discussed and exemplified.

We discuss the problem of incorporating into a heuristic theorem prover a decision procedure for a fragment of the logic. An obvious goal when incorporating such a procedure is to reduce the search space explored by the heuristic component of the system, as would be achieved by eliminating from the system's data base some explicitly stated axioms. For example, if a decision procedure for linear inequalities is added, one would hope to eliminate the explicit consideration of the transitivity axioms. However, the decision procedure must then be used in all the ways the eliminated axioms might have been. The difficulty of achieving this degree of integration is more dependent upon the complexity of the heuristic component than upon that of the decision procedure. The view of the decision procedure as a black box is frequently destroyed by the need pass large amounts of search strategic information back and forth between the two components.

The ability to understand Natural Language has long been a goal of Artificial Intelligence Research, and it is still far from being solved, however, in the early 1970's the AI research techniques reached a point whereby certain applications became feasible for the first time. Since that time, several systems such as PLANES[1], LIFER[2], and ROBOT[3,4,5] have been built that have demonstrated that the current state of the art is sufficient for quite good natural language data base query. The implementation of the R0301 system has been geared for high performance and instaliaoility in accual real world environments. As such, it offers the AI research community some insight into the difficulties encountered when putting the current: AI technology in the hands of people in the real world. This paper discusses the unexpected linguistic and semantic difficulties encountered in the 12 commercial applications to which ROBOT has been applied during the last year and a half.