This paper contains a proposal of application of rule induction for generating agent strategy. This method of learning is tested on a predator-prey domain, in which predator agents learn how to capture preys. We assume that proposed learning mechanism will be beneficial in all domains, in which agents can determine direct results of their actions. Experimental results show that the learning process is fast. Multi-agent communication aspect is also taken into account. We can show that in specific conditions transferring learned rules gives profits to the learning agents. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Śniezyński, B. (2009). Agent strategy generation by rule induction in predator-prey problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5545 LNCS, pp. 895–903). https://doi.org/10.1007/978-3-642-01973-9_99
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