David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Mind and Society 4 (2):179-195 (2005)
This paper is concerned with adaptive learning and coordination processes. Implementing agent-based modeling techniques (Learning Classifier Systems, LCS), we focus on the twofold impact of cognitive and environmental complexity on learning and coordination. Within this framework, we introduce the notion of Adaptive Learning Agent with Rule-based Memory (ALARM), which is a particular class of Artificial Adaptive Agent (AAA, Holland and Miller 1991). We show that equilibrium is approached to a high degree, but never perfectly reached. We also demonstrate that memorization and learning capacities depend upon the relative discordance between the cognitive complexity of agents' mental models and the degree of stability of the environment
|Keywords||Coordination Adaptive learning Classifier systems Computational techniques|
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