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Socionics pp 155-175 | Cite as

On the Organisation of Agent Experience: Scaling Up Social Cognition

  • Michael Rovatsos
  • Kai Paetow
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3413)

Abstract

This paper introduces “micro-scalability” as a novel design objective for social reasoning architectures operating in open multiagent systems. Micro-scalability is based on the idea that social reasoning algorithms should be devised in a way that allows for social complexity reduction, and that this can be achieved by operationalising principles of interactionist sociology. We first present a formal model of InFFrA agents called m 2 InFFrA that utilises two cornerstones of micro-scalability, the principles of social abstraction and transient social optimality. Then, we exemplify the usefulness of these concepts by presenting experimental results with a novel opponent classification heuristic AdHoc that has been developed using the InFFrA social reasoning architecture. These results prove that micro-scalability deserves further investigation as a useful aspect of socionic research.

Keywords

Social Cognition Multiagent System Markov Decision Process Agent Experience Opponent Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Michael Rovatsos
    • 1
  • Kai Paetow
    • 2
  1. 1.School of InformaticsThe University of EdinburghEdinburghUnited Kingdom
  2. 2.Department of Technology AssessmentTechnical University of HamburgHamburgGermany

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