Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Evolutionary learning of communicating agents
Information Sciences—Informatics and Computer Science: An International Journal
Emergent Cooperation for Multiple Agents Using Genetic Programming
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Robustness of robot programs generated by genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Multi-agent learning of heterogeneous robots by evolutionary subsumption
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Integration of genetic programming and reinforcement learning for real robots
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A multiagent cooperative learning algorithm
CSCWD'06 Proceedings of the 10th international conference on Computer supported cooperative work in design III
Multi-agent role allocation: issues, approaches, and multiple perspectives
Autonomous Agents and Multi-Agent Systems
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This paper presents the emergence of the cooperative behavior for multiple robot agents by means of Genetic Programming (GP). For this purpose, we utilize several extended mechanisms of GP, i.e., (1) a co-evolutionary breeding strategy, (2) a controlling strategy of introns, which are non-executed code segments dependent upon the situation, and (3) a subroutine discovery technique. Our experimental domain is an escape problem. We have chosen the actual experimental settings so as to be close to a real world as much as possible. The validness of our approach is discussed with comparative experiments using other methods, i.e., Q-learning and Neural networks, which shows the superiority of GP-based multi-agent learning.