"In this article we take a step towards providing an analysis of the Soar architecture as a basis for general intelligence. Included are discussions of the basic assumptions underlying the development of Soar, a description of Soar cast in terms of the theoretical idea of multiple levels of description, an example of Soar performing multi-column subtraction, and three analyses of Soar: its natural tasks, the sources of its power, and its scope and limits." Artificial Intelligence, 47, 289-325.

"Chunking, a simple experience-based learning mechanism, is Soar's only learning mechanism. Chunking creates new items of information, called chunks, based on the results of problem-solving and stores them in the knowledge base. These chunks are accessed and used in appropriate later situations to avoid the problem-solving required to determine them. It is already well-established that chunking improves performance in Soar when viewed in terms of the subproblems required and the number of steps within a subproblem. However, despite the reduction in number of steps, sometimes there may be a severe degradation in the total run time. This problem arises due to expensive chunks, i.e., chunks that require a large amount of effort in accessing them from the knowledge base. They pose a major problem for Soar, since in their presence, no guarantees can be given about Soar's performance.In this article, we establish that expensive chunks exist and analyze their causes. We use this analysis to propose a solution for expensive chunks. The solution is based on the notion of restricting the expressiveness of the representational language to guarantee that the chunks formed will require only a limited amount of accessing effort. We analyze the tradeoffs involved in restricting expressiveness and present some empirical evidence to support our analysis."Machine Learning, 5, 299-348.

Abstraction has proven to be a powerful tool for controlling the combinatorics of a problemsolving search. It is also of critical importance for learning systems. In this article we present, and evaluate experimentally, a general abstraction method -- impasse-driven abstraction - which is able to provide necessary assistance to both problem solving and learning. It reduces the amount of time required to solve problems, and the time required to learn new rules. In addition, it results in the acquisition of rules that are more general than would have otherwise been learned.

Chunking, an experience based-learning mechanism, improves Soar's performance a great deal when viewed in terms of the number of subproblems required and the number of steps within a subproblem. This high-level view of the impact of chunking on performance is based on an deal computational model, which says that the time per step is constant. However, if the chunks created by chunking are expensive, then they consume a large amount of processing in the match, i.e, indexing the knowledge-base, distorting Soar*s constant time-per-stcp model. In these situations, the gain in number of steps does not reflect an improvement in performance; in fact there may be degradation in the total run time of the system. Such chunks form a major problem for the system, since absolutely 10 guarantees can be given about its behavior. I "his article presents a solution to the problem of expensive chunks. The solution is based on the notion of restricting the expressiveness of Soar's representational language to guarantee that chunks formed will require only a limited amount of matching effort. We analyze the tradeoffs involved in restricting expressiveness and present some empirical evidence to support our analysis.

The system presented here shows how Soar, an architecture for general problem solving and learning, can acquire general search-control knowledge from outside guidance. The guidance can be either direct advice about what the system should do, or a problem that illustrates a relevant idea. The system makes use of the guidance by first formulating an appropriate goal for itself. In the process of achieving this goal, it learns general search-control chunks. In the case of learning from direct advice, the goal is to verify that the advice is correct. The verification allows the system to obtain general conditions of applicability of the advice, and to protect itself from erroneous advice. The system learns from illustrative problems by setting the goal of solving the problem provided. It can then transfer the lessons it learns along the way to its original problem. This transfer constitutes a rudimentary form of analogy.

"The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlying structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them. In this article we present SOAR, an implemented proposal for such an architecture. We describe its organizational principles, the system as currently implemented, and demonstrations of its capabilities." Artificial Intelligence, 33(1):1-64.

In this article we describe an approach to the construction of a general learning mechanism based on chunking in Soar. Chunking is a learning mechanism that acquires rules from goal-based experience. Soar is a general problem-solving architecture with a rule-based memory. In previous work we have demonstrated how the combination of chunking and Soar could acquire search-control knowledge (strategy acquisition) and operator implementation rules in both search-based puzzle tasks and knowledge-based expert-systems tasks. In this work we examine the anatomy of chunking in Soar and provide a new demonstration of its learning capabilities involving the acquisition and use of macro-operators.

Othello is a recent addition to the collection of games that have been examined within artificial intelligence. Advances have been rapid, yielding programs that have reached the level of world-championship play. This article describes the current champion Othello program, iago. The work described here includes: (1) a task analysis of Othello; (2) the implementation of a program based on this analysis and state-of-the-art AI game-playing techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play.

"Practice, and the performance improvement that it engenders, has long been a major topic in psychology. In this paper, both experimental and theoretical approaches are employed in an investigation of the mechanisms underlying this improvement On the experimental side, it is argued that a single law, the power law of practice, adequately describes all of the practice data. On the theoretical side, a model of practice rooted in modern cognitive psychology, the chunking theory of learning, is formulated. The paper consists of (1) the presentation of a set of empirical practice curves; (2) mathematical investigations into the nature of power law functions; (3) evaluations of the ability of three different classes of functions to adequately model the empirical curves; (4) a discussion of the existing models of practice; (5) a presentation of the chunking theory of learning." In J. R. Anderson (Ed.). Cognitive Skills and their Acquisition (pp. 1-55). Hillsdale, NJ: Erlbaum.