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-reviewOpen access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewHost publication Subtitle
GECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary ComputationOriginal language
EnglishPages from-to (Number of pages)
Pages 185-192Publication milestones
- Published - 2014
Publication status
Published - 2014
Publisher
Association for Computing Machinery, United StatesISBN (Print)
9781450326629Publication IDs
- Scopus: 84905708861
Host publication title
Genetic and Evolutionary Computation ConferenceAbstract
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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