Opportunism and Learning

Authors:
Kristian Hammond;Timothy Converse;Mitchell Marks;Colleen M. Seifert
Affiliations:
The University of Chicago, Department of Computer Science, Artificial Intelligence Laboratory, 1100 East 58th Street, Chicago, IL 60637. AI@TARTARUS.UCHICAGO.EDU;The University of Chicago, Department of Computer Science, Artificial Intelligence Laboratory, 1100 East 58th Street, Chicago, IL 60637. AI@TARTARUS.UCHICAGO.EDU;The University of Chicago, Department of Computer Science, Artificial Intelligence Laboratory, 1100 East 58th Street, Chicago, IL 60637. AI@TARTARUS.UCHICAGO.EDU;Department of Psychology, University of Michigan, 330 Packard Road, Ann Arbor, MI 48104. SEIFERT@UM.CC.UMICH.EDU
Venue:
Machine Learning - Special issue on case-based reasoning
Year:
1993

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Abstract

There is a tension in the world between complexity and simplicity. On one hand, we are faced with a richness of environment and experience that is at times overwhelming. On the other, we seem to be able to cope and even thrive within this complexity through the use of simple scripts, stereotypical judgements, and habitual behaviors. In order to function in the world, we have idealized and simplified it in a way that makes reasoning about it more tractable. As a group and as individuals, human agents search for and create islands of simplicity and stability within a sea of complexity and change.In this article, we will discuss an approach based on the case-based reasoning paradigm that attempts to resolve this tension. This agency approach embraces, rather than avoids, this paradox of the apparent complexity of the world and the overall simplicity of our methods for dealing with it. It accomplishes this by treating the behavior of intelligent agents as an ongoing attempt to discover, create, and maintain the stability that is necessary for the production of actions that satisfy our goals.