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Revising the Trade-off between the Number of Agents and Agent Intelligence

  • Marcus Komann
  • Dietmar Fey
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

Emergent agents are a promising approach to handle complex systems. Agent intelligence is thereby either defined by the number of states and the state transition function or the length of their steering programs. Evolution has shown to be successful in creating desired behaviors for such agents. Genetic algorithms have been used to find agents with fixed numbers of states and genetic programming is able to balance between the steering program length and the costs for longer programs. This paper extends previous work by further discussing the relationship between either using more agents with less intelligence or using fewer agents with higher intelligence. Therefore, the Creatures’ Exploration Problem with a complex input set is solved by evolving emergent agents. It shows that neither a sole increase in intelligence nor amount is the best solution. Instead, a cautious balance creates best results.

Keywords

State Machine Genetic Programming Cellular Automaton Agent Intelligence Visit Rate 
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 2010

Authors and Affiliations

  • Marcus Komann
    • 1
  • Dietmar Fey
    • 2
  1. 1.Friedrich-Schiller-University JenaJenaGermany
  2. 2.University of Erlangen-NürnbergErlangenGermany

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