Shallow decision-making analysis in General Video Game Playing

The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hyper heuristic based or knowledge based algorithms. So far the metrics used to evaluate the performance of agents have been win ratio, game score and length of games. In this paper we provide a wider set of metrics and a comparison method for evaluating and comparing agents. The metrics and the comparison method give shallow introspection into the agent's decision making process and they can be applied to any agent regardless of its algorithmic nature. In this work, the metrics and the comparison method are used to measure the impact of the terms that compose a tree policy of an MCTS based agent, comparing with several baseline agents. The results clearly show how promising such general approach is and how it can be useful to understand the behaviour of an AI agent, in particular, how the comparison with baseline agents can help understanding the shape of the agent decision landscape. The presented metrics and comparison method represent a step toward to more descriptive ways of logging and analysing agent's behaviours

Self-adaptive MCTS for General Video Game Playing

Monte-carlo tree search (mcts) has shown particular success in general game playing (ggp) and general video game playing (gvgp) and many enhancements and variants have been developed. Recently, an on-line adaptive parameter tuning mechanism for mcts agents has been proposed that almost achieves the same performance as off-line tuning in ggp.in this paper we apply the same approach to gvgp and use the popular general video game ai (gvgai) framework, in which the time allowed to make a decision is only 40 ms. We design three self-adaptive mcts (sa-mcts) agents that optimize on-line the parameters of a standard non-self-adaptive mcts agent of gvgai. The three agents select the parameter values using naïve monte-carlo, an evolutionary algorithm and an n-tuple bandit evolutionary algorithm respectively, and are tested on 20 single-player games of gvgai.the sa-mcts agents achieve more robust results on the tested games. With the same time setting, they perform similarly to the baseline standard mcts agent in the games for which the baseline agent performs well, and significantly improve the win rate in the games for which the baseline agent performs poorly. As validation, we also test the performance of non-self-adaptive mcts instances that use the most sampled parameter settings during the on-line tuning of each of the three sa-mcts agents for each game. Results show that these parameter settings improve the win rate on the games wait for breakfast and escape by 4 times and 150 times, respectively

Evolving UCT alternatives for general video game playing

We use genetic programming to evolve alternatives to the UCB1 heuristic used in the standard UCB formulation of Monte Carlo Tree Search. The fitness function is the performance of MCTS based on the evolved equation on playing particular games from the General Video Game AI framework. Thus, the evolutionary process aims to create MCTS variants that perform well on particular games; such variants could later be chosen among by a hyper-heuristic game-playing agent. The evolved solutions could also be analyzed to understand the games better. Our results show that the heuristic used for node selection matters greatly to performance, and the vast majority of heuristics perform very badly; furthermore, we can evolve heuristics that perform comparably to UCB1 in several games. The evolved heuristics differ greatly between games.peer-reviewe