Man – Machine Interface
نویسنده
چکیده
Agents trained by learning techniques provide a powerful approximation of state spaces in games that are too large for naive approaches. In the study Genetic Algorithms and Manual Interface was implemented and used to train agents for the board game LUDO. The state space of LUDO is generalized to a small set and encoded to suit the different techniques. The impact of variables and tactics applied in training are determined. Agents based on the techniques performed satisfactory against a baseline finite agent, and a Genetic Algorithm based agent performed satisfactory against competitors from the course. Better state space representations will improve the success of learning based agents.
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