This thesis is a contribution to the General Game Playing domain (GGP), a problematic of Artificial Intelligence (AI) aiming at developing autonomous agents that are able to play at a large variety of games called General Games. GGP is different from search algorithms allowing to play with a good level at specific games and opens the possibility to evaluate the efficiency of AI methods without prior knowledge form experts. An important aspect of our work lies on the utilization of an implicit game tree representation as a set of logic rules, an explicit representation being too large to be stored in memory. In this context, we have proposed an efficient method of rule instantiation allowing the computation of a logic circuit. A parallelization of the circuit evaluation allowed us to significantly accelerate the game tree exploration. We have proposed an adaptation of Monte-Carlo Tree Search for GGP and a method using RAVE (Rapid Action Value Estimation) in the beginning of the exploration when few estimations are available.