"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.

Proc. Eleventh Intl. Joint Conference on Artificial Intelligence (pp. 731-737). Detroit, MI: IJCAII.

Proc. Eleventh Intl. Joint Conference on Artificial Intelligence (pp. 681-687). Detroit, MI: IJCAII.

"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.

Proc. Tenth Intl. Joint Conference on Artificial Intelligence (pp. 334-337). Milan, Italy: IJCAII.

Machine Learning, 1, 11–46.

Available for a fee. Manuscript available at Carnegie Mellon University https://kilthub.cmu.edu/articles/A_world-championship-level_Othello_program/6602903. 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 implemenation of a program based on this analysis and state-of-the-art AI gameplaying techniques; and (3) an evaluation of the program's performance through games played against other programs and comparisons with expert human play. Artificial Intelligence, 19, 279-320.

"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.