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This book contains the proceedings of the 28 th edition of the annual Benelux Conference on Artificial Intelligence (BNAIC 2016). BNAIC 2016 was jointly organized by the University of Amsterdam and the Vrije Universiteit Amsterdam, under the auspices of the Benelux Association for Artificial Intelligence (BNVKI) and the Dutch Research School for Information and Knowledge Systems (SIKS).Held yearly, the objective of BNAIC is to promote and disseminate recent research developments in Artificial Intelligence within Belgium, Luxembourg and the Netherlands. However, it does not exclude contributions from countries outside the Benelux. As in previous years, BNAIC 2016 welcomed four types of contributions, namely A) regular papers, B) compressed contributions, C) demonstration abstracts, and D) thesis abstracts.We received 93 submissions, consisting of 24 regular papers, 47 short papers, 11 demonstration abstracts and 11 thesis abstracts. After a thorough review phase by the Program Committee, the conference chairs made the final acceptance decisions. The overall acceptance rate was 88% (63% for regular papers, 100% for compressed contributions and demonstration abstracts, and 91% for thesis abstracts).In addition to the regular research presentations, posters and demonstrations, we were happy to include several other elements in the program of BNAIC 2016, among which keynote presentations by Marc Cavazza (University of Kent), Frank van Harmelen (Vrije Universiteit Amsterdam), Hado van Hasselt (Google DeepMind), and Manuela Veloso (Carnegie Mellon University), a Research meets Business session, a panel discussion on Social Robots, with contributions by Elly Konijn (Vrije

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 40ms. 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.

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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.