scholarly journals Representing and Reasoning About the Rules of General Games With Imperfect Information

2014 ◽  
Vol 49 ◽  
pp. 171-206 ◽  
Author(s):  
S. Schiffel ◽  
M. Thielscher
Keyword(s):  
Imperfect Information ◽  
Complete Information ◽  
Game Playing ◽  
Description Language ◽  
General Game Playing ◽  
Game State ◽  
High Level ◽  
Game Description Language ◽  
General Game ◽  
Representation Formalism

A general game player is a system that can play previously unknown games just by being given their rules. For this purpose, the Game Description Language (GDL) has been developed as a high-level knowledge representation formalism to communicate game rules to players. In this paper, we address a fundamental limitation of state-of-the-art methods and systems for General Game Playing, namely, their being confined to deterministic games with complete information about the game state. We develop a simple yet expressive extension of standard GDL that allows for formalising the rules of arbitrary finite, n-player games with randomness and incomplete state knowledge. In the second part of the paper, we address the intricate reasoning challenge for general game-playing systems that comes with the new description language. We develop a full embedding of extended GDL into the Situation Calculus augmented by Scherl and Levesque's knowledge fluent. We formally prove that this provides a sound and complete reasoning method for players' knowledge about game states as well as about the knowledge of the other players.

scholarly journals GDL-III: A Description Language for Epistemic General Game Playing

2017 ◽  
Author(s):  
Michael Thielscher
Keyword(s):  
Imperfect Information ◽  
Formal Semantics ◽  
Experimental Results ◽  
Game Playing ◽  
Description Language ◽  
General Game Playing ◽  
Epistemic Games ◽  
General Game

GDL-III, a description language for general game playing with imperfect information and introspection, supports the specification of epistemic games. These are characterised by rules that depend on the knowledge of players. GDL-III provides a simpler language for representing actions and knowledge than existing formalisms: domain descriptions require neither explicit axioms about the epistemic effects of actions, nor explicit specifications of accessibility relations. We develop a formal semantics for GDL-III and demonstrate that this language, despite its syntactic simplicity, is expressive enough to model the famous Muddy Children domain. We also show that it significantly enhances the expressiveness of its predecessor GDL-II by formally proving that termination of games becomes undecidable, and we present experimental results with a reasoner for GDL-III applied to general epistemic puzzles.

scholarly journals Exploring Monte Carlo Negotiation Search with Nontrivial Agreements

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Elijah Alden Malaby ◽  
John Licato
Keyword(s):  
Monte Carlo ◽  
Research Area ◽  
Description Language ◽  
General Game Playing ◽  
Cooperative Strategies ◽  
Automated Negotiations ◽  
Individual Sequences ◽  
Zero Sum ◽  
Game Description Language ◽  
General Game

The application of automated negotiations to general game playing is a research area with far-reaching implications. Non-zero sum games can be used to model a wide variety of real-world scenarios and automated negotiation provides a framework for more realistically modeling the behavior of agents in these scenarios. A particular recent development in this space is the Monte Carlo Negotiation Search (MCNS) algorithm, which can negotiate to find valuable cooperative strategies for a wide array of games (such as those of the Game Description Language). However, MCNS only proposes agreements corresponding to individual sequences of moves without any higher-level notions of conditional or stateful strategy. Our work attempts to lift this restriction. We present two contributions: extensions to the MCNS algorithm to support more complex agreements and an agreement language for GDL games suitable for use with our algorithm. We also present the results of a preliminary experiment in which we use our algorithm to search for an optimal agreement for the iterated prisoners dilemma. We demonstrate significant improvement of our algorithm over random agreement sampling, although further work is required to more consistently produce optimal agreements.

scholarly journals General Game Playing with Imperfect Information

2019 ◽  
Vol 66 ◽  
pp. 901-935
Author(s):  
Michael Schofield ◽  
Michael Thielscher
Keyword(s):  
Imperfect Information ◽  
Experimental Results ◽  
Controlled Environment ◽  
Game Playing ◽  
General Game Playing ◽  
Additional Challenge ◽  
Information Sets ◽  
Imperfect Information Games ◽  
General Game ◽  
Information Games

General Game Playing is a field which allows the researcher to investigate techniques that might eventually be used in an agent capable of Artificial General Intelligence.  Game playing presents a controlled environment in which to evaluate AI techniques, and so we have seen an increase in interest in this field of research.  Games of imperfect information offer the researcher an additional challenge in terms of complexity over games with perfect information.  In this article, we look at imperfect-information games: their expression, their complexity, and the additional demands of their players.  We consider the problems of working with imperfect information and introduce a technique called HyperPlay, for efficiently sampling very large information sets, and present a formalism together with pseudo code so that others may implement it. We examine the design choices for the technique, show its soundness and completeness then provide some experimental results and demonstrate the use of the technique in a variety of imperfect-information games, revealing its strengths, weaknesses, and its efficiency against randomly generating samples.  Improving the technique, we present HyperPlay-II, capable of correctly valuing information-gathering moves.  Again, we provide some experimental results and demonstrate the use of the new technique revealing its strengths, weaknesses and its limitations.

scholarly journals The International 
General Game Playing Competition

AI Magazine ◽  
2013 ◽  
Vol 34 (2) ◽  
pp. 107 ◽  
Author(s):  
Michael Genesereth ◽  
Yngvi Björnsson
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Game Playing ◽  
Test Bed ◽  
General Game Playing ◽  
Research Challenges ◽  
History Of ◽  
World Class ◽  
General Game

Games have played a prominent role as a test-bed for advancements in the field of Artificial Intelligence ever since its foundation over half a century ago, resulting in highly specialized world-class game-playing systems being developed for various games. The establishment of the International General Game Playing Competition in 2005, however, resulted in a renewed interest in more general problem solving approaches to game playing. In general game playing (GGP) the goal is to create game-playing systems that autonomously learn how to skillfully play a wide variety of games, given only the descriptions of the game rules. In this paper we review the history of the competition, discuss progress made so far, and list outstanding research challenges.

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