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.
Original language | English |
---|---|
Title of host publication | Genetic and Evolutionary Computation Conference : GECCO '14 Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation |
Publisher | Association for Computing Machinery |
Publication date | 2014 |
Pages | 185-192 |
ISBN (Print) | 9781450326629 |
DOIs | |
Publication status | Published - 2014 |
Keywords
- $$evolution
- $$modeling
- cognition
- diversity maintenance
- evolutionary robotics