The International General Game Playing Competition

Genesereth, Michael; Björnsson, Yngvi

doi:10.1609/aimag.v34i2.2475

Cited by 69 publications

(70 citation statements)

References 11 publications

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“…The popularity of General Game Playing (GGP) has increased in the last decade, since M. Genesereth et al [8] organised the first GGP competition allowing participants to submit game agents to play in a diverse collection of board games. Sharma et al [25] motivates research in this area by bringing to attention how agents trained without prior knowledge of the game and excelling in specific games, such as TD-Gammon in Backgammon [26] and Blondie24 in Checkers [1], cannot be successfully applied in other scenarios or environments.…”

Section: Relevant Researchmentioning

confidence: 99%

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

Gaina

Liu

Lucas

et al. 2017

Applications of Evolutionary Computation

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Abstract. Monte Carlo Tree Search techniques have generally dominated General Video Game Playing, but recent research has started looking at Evolutionary Algorithms and their potential at matching Tree Search level of play or even outperforming these methods. Online or Rolling Horizon Evolution is one of the options available to evolve sequences of actions for planning in General Video Game Playing, but no research has been done up to date that explores the capabilities of the vanilla version of this algorithm in multiple games. This study aims to critically analyse the different configurations regarding population size and individual length in a set of 20 games from the General Video Game AI corpus. Distinctions are made between deterministic and stochastic games, and the implications of using superior time budgets are studied. Results show that there is scope for the use of these techniques, which in some configurations outperform Monte Carlo Tree Search, and also suggest that further research in these methods could boost their performance.

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Section: Relevant Researchmentioning

confidence: 99%

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

Gaina

Liu

Lucas

et al. 2017

Applications of Evolutionary Computation

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“…The evaluation method constructed as described in sections 3.1 -3.2 takes game state as an input, performs weights calculation according to (3) and returns a single floating point value. Such automatically constructed heuristic can be taken advantage of in several ways by the playing agent.…”

Section: The Use Of the Heuristic Functionmentioning

confidence: 99%

“…Given the game rules written in the so-called GDL (Game Description Language) [2] a playing agent takes various actions towards learning and mastering the game. This includes analysis of the game rules, application of various learning and searching mechanisms, logic-based reasoning methods, efficient knowledge representation and many other techniques [3][4][5]. Integration of all these elements formulates an interesting and challenging research task.…”

Section: Introductionmentioning

confidence: 99%

Generic Heuristic Approach to General Game Playing

Mańdziuk

2012

Lecture Notes in Computer Science

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Abstract. General Game Playing (GGP) is a specially designed environment for creating and testing competitive agents which can play variety of games. The fundamental motivation is to advance the development of various artificial intelligence methods operating together in a previously unknown environment. This approach extrapolates better on real world problems and follows artificial intelligence paradigms better than dedicated single-game optimized solutions. This paper presents a universal method of constructing the heuristic evaluation function for any game playable within the GGP framework. The algorithm embraces distinctive discovery of candidate features to be included in the evaluation function and learning their correlations with actions performed by the players and the game score. Our method integrates well with the UCT algorithm which is currently the state-of-the-art approach in GGP.

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“…Recently, there is increasing interest and progress in AI systems that behave well in a more general range of situations. The evaluation procedures, benchmarks and computations are hence very different, depending on whether we focus on specialised benchmarks [72,38,24,116] or on more general problems [96,31,94]. Despite this trend, AI is still lacking general, well-grounded and universally accepted intelligence measurement tools.…”

Section: Introductionmentioning

confidence: 99%

Towards a Universal Test of Social Intelligence

Cabrera¹

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Under the view of artificial intelligence, an intelligent agent is an autonomous entity which interacts in an environment through observations and actions, trying to achieve one or more goals with the aid of several signals called rewards. The creation of intelligent agents is proliferating during the last decades, and the evaluation of their intelligence is a fundamental issue for their understanding, construction and improvement.Social intelligence is recently obtaining special attention in the creation of intelligent agents due to the current view of human intelligence as highly social. Social intelligence in natural and artificial systems is usually measured by the evaluation of associated traits or tasks that are deemed to represent some facets of social behaviour. The amalgamation of these traits or tasks is then used to configure an operative notion of social intelligence. However, this operative notion does not truly represent what social intelligence is and a definition following this principle will not be precise. Instead, in this thesis we investigate the evaluation of social intelligence in a more formal and general way, by actually considering the evaluee's interaction with other agents.In this thesis we analyse the implications of evaluating social intelligence using a test that evaluates general intelligence. For this purpose, we include other agents into an initially singleagent environment to figure out the issues that appear when evaluating an agent in the context of other agents. From this analysis we obtain useful information for the evaluation of social intelligence.From the lessons learned, we identify the components that should be considered in order to measure social intelligence, and we provide a formal and parametrised definition of social intelligence. This definition calculates an agent's social intelligence as its expected performance in a set of environments with a set of other agents arranged in teams and participating in lineups, with rewards being re-understood appropriately. This is conceived as a tool to define social intelligence testbeds where we can generate several degrees of competitive and cooperative behaviours. We test this definition by experimentally analysing the influence of teams and agent line-ups for several multi-agent systems with variants of Q-learning agents.However, not all testbeds are appropriate for the evaluation of social intelligence. To facilitate the analysis of a social intelligence testbed, we provide some formal property models about social intelligence in order to characterise the testbed and thus assess its suitability. Finally, we use the presented properties to characterise some social games and multi-agent environments, we make a comparison between them and discuss their strengths and weaknesses in order to evaluate social intelligence.ii ResumenBajo la visión de la inteligencia artificial, un agente inteligente es una entidad autónoma la cual interactúa en un entorno a través de observaciones y acciones, tratando de lograr uno o más ...

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The International General Game Playing Competition

Cited by 69 publications

References 11 publications

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

Generic Heuristic Approach to General Game Playing

Towards a Universal Test of Social Intelligence

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