Strategy Representation and Reasoning for Incomplete Information Concurrent Games in the Situation Calculus

Strategy representation and reasoning for incomplete information concurrent games has recently received much attention in multi-agent system and AI communities. However, most of the logical frameworks are based on concrete game models, lack the abilities to reason about strategies explicitly or specify strategies procedurally, and ignore the issue of coordination within a coalition. In this paper, by a simple extension of a variant of multiagent epistemic situation calculus with a strategy sort, we develop a general framework for strategy representation and reasoning for incomplete information concurrent games. Based on Golog, we propose a strategy programming language which can be conveniently used to specify collective strategies of coalitions at different granularities. We present a formalization of joint abilities of coalitions under commitments to strategy programs. Different kinds of individual strategic abilities can be distinguished in our framework. Both strategic abilities in ATL and joint abilities of Ghaderi et al. can be considered as joint abilities under special programs in our framework.We illustrate our work with a variant of Levesque's Squirrels World.

[1]  Ariel Rubinstein,et al.  A Course in Game Theory , 1995 .

[2]  Hector J. Levesque,et al.  Towards a logical theory of coordination and joint ability , 2007, AAMAS '07.

[3]  A. Rubinstein,et al.  A Course in Game Theory , 1995 .

[4]  Dongmo Zhang,et al.  Representing and Reasoning about Game Strategies , 2015, J. Philos. Log..

[5]  James P. Delgrande,et al.  Representing von Neumann–Morgenstern Games in the Situation Calculus , 2004, Annals of Mathematics and Artificial Intelligence.

[6]  Hector J. Levesque,et al.  Incorporating Action Models into the Situation Calculus , 2014, Johan van Benthem on Logic and Information Dynamics.

[7]  Thomas Lukasiewicz,et al.  Team Programming in Golog under Partial Observability , 2007, IJCAI.

[8]  Giuseppe De Giacomo,et al.  Situation Calculus Based Programs for Representing and Reasoning about Game Structures , 2010, KR.

[9]  Michael Wooldridge,et al.  Cooperation, Knowledge, and Time: Alternating-time Temporal Epistemic Logic and its Applications , 2003, Stud Logica.

[10]  Samir Guglani Knowledge , 2016, The Lancet.

[11]  Hector J. Levesque,et al.  Ability and Knowing How in the Situation Calculus , 2000, Stud Logica.

[12]  Hector J. Levesque,et al.  GOLOG: A Logic Programming Language for Dynamic Domains , 1997, J. Log. Program..

[13]  Johan van Benthem,et al.  Reasoning about Strategies , 2013, Computation, Logic, Games, and Quantum Foundations.

[14]  Wojciech Jamroga,et al.  Constructive knowledge: what agents can achieve under imperfect information , 2007, J. Appl. Non Class. Logics.

[15]  Ramaswamy Ramanujam,et al.  Dynamic Logic on Games with Structured Strategies , 2008, KR.

[16]  Giuseppe De Giacomo,et al.  Bounded Epistemic Situation Calculus Theories , 2013, IJCAI.

[17]  Stephan Schiffel,et al.  Representing and Reasoning About the Rules of General Games With Imperfect Information , 2014, J. Artif. Intell. Res..

[18]  Alex M. Andrew,et al.  Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems , 2002 .

[19]  Michael Wooldridge,et al.  Alternating-time temporal logic with explicit strategies , 2007, TARK '07.

[20]  Wojciech Jamroga,et al.  Agents that Know How to Play , 2004, Fundam. Informaticae.

[21]  Thomas A. Henzinger,et al.  Alternating-time temporal logic , 1997, Proceedings 38th Annual Symposium on Foundations of Computer Science.

[22]  Hector J. Levesque,et al.  ConGolog, a concurrent programming language based on the situation calculus , 2000, Artif. Intell..

[23]  Jan van Eijck,et al.  PDL as a Multi-Agent Strategy Logic , 2013, TARK.