Opponent selection in a multi-agent gaming environment under resource constraints
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The RLGame is a strategy game of two players that is continuously developed since 2001. It was created in order to conduct research at the field of Reinforcement Learning. In this dissertation we suggest a number of scenarios, where the agents (avatars) that are competing in the RLGame could be studied under the presence of resource constraints. The goal of these scenarios is to investigate whether there are indications of behavioral adaptation when the rules of the game essentially change and therefore could lead to explicit or implicit opponent selection. A number of scenarios are theoretically described and some of them are implemented using Eclipse, a Java integrated development environment. These implementations provided some indications that the existence of some resource constraints might affect the behavior of the synthetic agents and at the same time set the base for future research on the RLGame in order for a more dynamic approach to be implemented and the behavioral adaptation of the agents to be investigated in depth.