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Overcoming Deception in Evolution of Cognitive Behaviors

  • Joel Lehman
    ,
  • Risto Miikkulainen
  • University of Texas
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Open access

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Host publication Subtitle

GECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Original language

English

Pages from-to (Number of pages)

Pages 185-192

Publication milestones

  • Published - 2014

Publication status

Published - 2014

Publisher

Association for Computing Machinery, United States
9781450326629

Publication IDs

  • Scopus: 84905708861

Host publication title

Genetic and Evolutionary Computation Conference

Abstract

When scaling neuroevolution to complex behaviors, cognitive capabilities such as learning, communication, and memory become increasingly important. However, successfully evolving such cognitive abilities remains difficult. This paper argues that a main cause for such difficulty is deception, i.e. evolution converges to a behavior unrelated to the desired solution. More specifically, cognitive behaviors often require accumulating neural structure that provides no immediate fitness benefit, and evolution often thus converges to non-cognitive solutions. To investigate this hypothesis, a common evolutionary robotics T-Maze domain is adapted in three separate ways to require agents to communicate, remember, and learn. Indicative of deception, evolution driven by objective-based fitness often converges upon simple non- cognitive behaviors. In contrast, evolution driven to explore novel behaviors, i.e. novelty search, often evolves the desired cognitive behaviors. The conclusion is that open-ended methods of evolution may better recognize and reward the stepping stones that are necessary for cognitive behavior to emerge.

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