Evolutionary Computational Intelligence
for Multi-Agent Simulations
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The growing interest in multi-agent simulations, influenced by the advances in fields like Complexity Science and Artificial Life is related to a modern direction in computational intelligence research. Instead of building isolated artificial intelligence systems from the top-down, this new approach attempts to design systems where a population of agents and the environment interact and adaptation processes take place. This book presents a novel evolutionary platform to tackle the problem of evolving computational intelligence in multi-agent simulations. It consists of an artificial brain mode...
The growing interest in multi-agent simulations, influenced by the advances in fields like Complexity Science and Artificial Life is related to a modern direction in computational intelligence research. Instead of building isolated artificial intelligence systems from the top-down, this new approach attempts to design systems where a population of agents and the environment interact and adaptation processes take place. This book presents a novel evolutionary platform to tackle the problem of evolving computational intelligence in multi-agent simulations. It consists of an artificial brain model, called the gridbrain, a simulation embedded evolutionary algorithm (SEEA) and LabLOVE, a simulation environment. In scenarios that require cooperation, we demonstrate the emergence of synchronization behaviors that would be difficult to achieve under conventional approaches. Kin selection and group selection based approaches are compared. In a scenario where two species are in competition, we demonstrated the emergence of specialization niches without the need for geographical isolation.
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