Maciej Swiechowski

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The term general game playing (GGP) refers to a subfield of AI which aims at developing agents able to effectively play many games from a particular class (finite, deterministic). It is also the name of the annual competition proposed by Stanford Logic Group at Stanford University (Stanford, CA, USA), which provides a framework for testing and evaluating(More)
The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new(More)
General game playing (GGP) aims at designing autonomous agents capable of playing any game within a certain genre, without human intervention. GGP agents accept the rules, which are written in the logic-based game definition language (GDL) and unknown to them beforehand, at runtime. The state-of-the-art players use Monte Carlo tree search (MCTS) together(More)
In this paper, we study the problem of applying an inference engine, i.e. a mechanism able to derive answers from a knowledge base, to General Game Playing (GGP). Our focus is on the General Game Playing Competition framework proposed by the Stanford Logic Group. This particular embodiment of a multi-game playing uses the Game Description Language (GDL) for(More)
This paper summarizes the AAIA’17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also(More)
The paper concerns the problem of reaching consensus among agents in group decision making. A popular framework of individual preferences expressed as (fuzzy) preference relations is adopted. The consensus reaching process is assumed to be based on a discussion in the group of agents, which is expected to make the initially expressed preferences closer one(More)