Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming and emergent intelligence
Advances in genetic programming
Emergence: From Chaos to Order
Emergence: From Chaos to Order
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Evolution of coordination in reactive multiagent systems
Evolution of coordination in reactive multiagent systems
Redundant representations in evolutionary computation
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Network Simulations for Relationality Design - An Approach Toward Complex Systems
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Relationality Design toward Enriched Communications
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
An Evolutionary Solution for Cooperative and Competitive Mobile Agents
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
XML-based genetic programming framework: design philosophy, implementation, and applications
Artificial Life and Robotics
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We present the result of our work on the use of strongly typed genetic programming with exception handling capabilities for the evolution of surrounding behavior of agents situated in an inherently cooperative environment. The predators-prey pursuit problem is used to verify our hypothesis that relatively complex surrounding behavior may emerge from simple, implicit, locally defined, and therefore--scalable interactions between the predator agents. Proposing two different communication mechanisms ((i) simple, basic mechanism of implicit interaction, and (ii) explicit communications among the predator agents) we present a comparative analysis of the implications of these communication mechanisms on evolution, generality and robustness of the emerged surrounding behavior. We demonstrate that relatively complex-surrounding behavior emerges even from implicit, proximity-defined interactions among the agents. Although the basic model offers the benefits of simplicity and scalability, compared to the enhanced model of explicit communications among the agents, it features increased computational effort and inferior generality and robustness of agents' emergent surrounding behavior when the team of predator agents is evolved in noiseless environment and then tested in noisy and uncertain environment. Evolution in noisy environment virtually equalizes the robustness and generality characteristics of both models. For both models however the increase of noise levels during the evolution is associated with evolving solutions, which are more robust to noise but less general to new, unknown initial situations.