Search

For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game. We propose adapting such methods for Rolling Horizon Evolutionary Algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent's win rate. On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach. We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.

Research in Artificial Intelligence has shown that machines can be programmed to perform as well as, or even better than humans in specific tasks, such as playing Chess, recognizing images, or driving cars. However, humans still have the advantage of being able to switch between all these tasks autonomously, while a machine needs to be reprogrammed for each new task. The field of Artificial General Intelligence aims at creating a program that is able to perform heterogeneous tasks in different environments autonomously, similarly to humans. The ability to plan and make decisions is one of the core capabilities expected of such a program. My research presents various improvements for a strategy that searches for the best actions to perform in an unknown environment, using board games and video games as test domains. The research shows that the performance of an agent that plays many unknown (video) games can be improved by reasoning on the game rules in a computationally efficient way, using game-independent information to guide the search, and adapting and diversifying the search for each new game being played.

General Game Playing (GGP) aims at creating computer programs able to play any arbitrary game at an expert level given only its rules. The lack of game-specific knowledge and the necessity of learning a strategy online have made Monte-Carlo Tree Search (MCTS) a suitable method to tackle the challenges of GGP. An efficient search-control mechanism can substantially increase the performance of MCTS. The RAVE strategy and its more recent variant, GRAVE, have been proposed for this reason. In this paper we further investigate the use of GRAVE for GGP and compare its performance with the more established RAVE strategy and with a new variant, called HRAVE, that uses more global information. Experiments show that for some games GRAVE and HRAVE perform better than RAVE, with GRAVE being the most promising one overall.

A purpose of General Video Game Playing (GVGP) is to create agents capable of playing many different real-time video games. Instead of using a fixed general strategy, a challenging aspect is devising strategies that adapt the search to each video game being played. Recent work showed that on-line parameter tuning can be used to adapt Monte-Carlo Tree Search (MCTS) in real-time. This paper extends prior work on Self-adaptive Monte-Carlo Tree Search (SA-MCTS) by further testing one of the previously proposed on-line parameter tuning strategies, based on the N-Tuple Bandit Evolutionary Algorithm (NTBEA). Results show that, both for a simple and a more advanced MCTS agent, on-line parameter tuning has impact on performance only for a few GVGP games. Moreover, an informed strategy as NTBEA shows a significant performance increase only in one case. In a real-time domain as GVGP, advanced parameter tuning does not seem very promising. Randomizing pre-selected parameters for each simulation appears to be a robust approach.

General game playing (ggp) programs need a game description language (gdl) reasoner to be able to interpret the game rules and search for the best actions to play in the game. One method for interpreting the game rules consists of translating the gdl game description into an alternative representation that the player can use to reason more efficiently on the game. The propositional network (propnet) is an example of such method. The use of propnets in ggp has become popular due to the fact that propnets can speed up the reasoning process by several orders of magnitude compared to custom-made or prolog-based gdl reasoners, improving the quality of the search for the best actions. This paper analyzes the performance of a propnet-based reasoner and evaluates four different optimizations for the propnet structure that can help further increase its reasoning speed in terms of visited game states per second.

General Video Game Playing (GVGP) is a field of Artificial Intelligence where agents play a variety of real-time video games that are unknown in advance. This limits the use of domain-specific heuristics. Monte-Carlo Tree Search (MCTS) is a search technique for game playing that does not rely on domain-specific knowledge. This paper discusses eight enhancements for MCTS in GVGP; Progressive History, N-Gram Selection Technique, Tree Reuse, Breadth-First Tree Initialization, Loss Avoidance, Novelty-Based Pruning, Knowledge-Based Evaluations, and Deterministic Game Detection. Some of these are known from existing literature, and are either extended or introduced in the context of GVGP, and some are novel enhancements for MCTS. Most enhancements are shown to provide statistically significant increases in win percentages when applied individually. When combined, they increase the average win percentage over sixty different games from 31.0% to 48.4% in comparison to a vanilla MCTS implementation, approaching a level that is competitive with the best agents of the GVG-AI competition in 2015.