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Information Technology
Relaxation and constrained optimization by local processes
Computer Graphics and Image Processing 10:115-125
A general learning theory and its application to schema abstraction
Anderson, J. | Kline, P. | Beasley, C.
In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 13). New York: Academic Press, 277-318
Relational consistency algorithms and their application in finding subgraph and graph isomorphisms
Information Sciences, 19 (3), 229-50.
An investigation of computer coaching for informal learning activities
International Journal of Man-Machine Studies 11:5-24
A cognitive model of planning
Hayes-Roth, B. | Hayes-Roth, F.
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.
Metacognition and cognitive monitoring: A new area for cognitive-developmental inquiry
American Psychologist 34:906-911.
A truth maintenance system
To choose their actions, reasoning programs must be able to make assumptions and subsequently revise their beliefs when discoveries contradict these assumptions. The Truth Maintenance System (TMS) is a problem solver subsystem for performing these functions by recording and maintaining the reasons for program beliefs. Such recorded reasons are useful in constructing explanations of program actions and in guiding the course of action of a problem solver. This paper describes (1) the representations and structure of the TMS, (2) the mechanisms used to revise the current set of beliefs, (3) how dependency-directed backtracking changes the current set of assumptions, (4) techniques for summarizing explanations of beliefs, (5) how to organize problem solvers into "dialectically arguing" modules, (6) how to revise models of the belief systems of others, and (7) methods for embedding control structures in patterns of assumptions. We stress the need of problem solvers to choose between alternative systems of beliefs, and outline a mechanism by which a problem solver can employ rules guiding choices of what to believe, what to want, and what to do.Artificial Intelligence 12(3):231-272