Abstract
This paper presents the first stage of research into a multi-agent complex environment, called “FlatLand” aiming at emerging complex and adaptive obstacle-avoidance and targetachievement behaviors by use of a variety of learning mechanisms. The presentation includes a detailed description of the FlatLand simulated world, the learning mechanisms used as well as an efficient method for comparing the mechanisms’ performance, robustness and required computational effort.
Original language | English |
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Title of host publication | 11th IEEE Mediterranean Conference on Control and Automation (MED'03) |
Number of pages | 6 |
Publication status | Published - 1 Jun 2003 |
Keywords
- back-propagation
- genetic algorithms
- machine learning
- multi-agent
- simulated worlds