Using Game Description Language for mediated dispute resolution

Mediation is a process in which two parties agree to resolve their dispute by negotiating over alternative solutions presented by a mediator. In order to construct such solutions, the mediator brings more information and knowledge, and, if possible, resources to the negotiation table. In order to do so, the mediator faces the challenge of determining which information is relevant to the current problem, given a vast database of knowledge. The contribution of this paper is the automated mediation machinery to resolve this issue. We define the concept of a Mediation Problem and show how it can be described in Game Description Language (GDL). Furthermore, we present an algorithm that allows the mediator to efficiently determine which information is relevant to the problem and collect this information from the negotiating agents. We show with several experiments that this algorithm is much more efficient than the naive solution that simply takes all available knowledge into account.

[1]  Janet L. Kolodner,et al.  The MEDIATOR: Analysis of an Early Case-Based Problem Solver , 1989, Cogn. Sci..

[2]  Trevor J. M. Bench-Capon Persuasion in Practical Argument Using Value-based Argumentation Frameworks , 2003, J. Log. Comput..

[3]  Jonathan Wilkenfeld,et al.  THE ROLE OF MEDIATION IN CONFLICT MANAGEMENT : CONDITIONS FOR SUCCESSFUL RESOLUTION , 1999 .

[4]  John von Neumann,et al.  1. On the Theory of Games of Strategy , 1959 .

[5]  Simeon Simoff,et al.  Negotiating Intelligently , 2006, SGAI Conf..

[6]  Donald E. Knuth,et al.  The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm , 1981, ICALP.

[7]  Wietske Visser,et al.  Interest-based Preference Reasoning , 2011, ICAART.

[8]  Michael Thielscher,et al.  A General Game Description Language for Incomplete Information Games , 2010, AAAI.

[9]  Michael Thielscher,et al.  General Game Playing , 2015 .

[10]  Sarit Kraus,et al.  Bridging the Gap: Face-to-Face Negotiations with an Automated Mediator , 2011, IEEE Intelligent Systems.

[11]  Carles Sierra,et al.  Information-Based Agency , 2007, IJCAI.

[12]  Nicholas R. Jennings,et al.  Agents That Reason and Negotiate by Arguing , 1998, J. Log. Comput..

[13]  John Zeleznikow,et al.  Developing Negotiation Decision Support Systems that Support Mediators: A Case Study of the Family_Winner System , 2005, Artificial Intelligence and Law.

[14]  T. van Amelsvoort Bridging the Gap , 2014, Tijdschrift voor psychiatrie.

[15]  John K. Debenham Bargaining with information , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[16]  Michael R. Genesereth,et al.  General Game Playing: Overview of the AAAI Competition , 2005, AI Mag..

[17]  Lin Padgham,et al.  A BDI agent programming language with failure handling, declarative goals, and planning , 2011, Autonomous Agents and Multi-Agent Systems.

[18]  M. Dresher THEORY OF GAMES OF STRATEGY , 1956 .

[19]  Letizia Tanca,et al.  What you Always Wanted to Know About Datalog (And Never Dared to Ask) , 1989, IEEE Trans. Knowl. Data Eng..

[20]  Frank Dignum,et al.  A formal analysis of interest-based negotiation , 2009, Annals of Mathematics and Artificial Intelligence.

[21]  Ramón López de Mántaras,et al.  CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation , 2011, ICCBR.

[22]  John K. Debenham,et al.  Curious Negotiator , 2002, CIA.

[23]  Jørn K. Rognes,et al.  Knowing You : Own Orientation and Information about the Opponent ’ s Orientation in Negotiation , 2003 .

[24]  Sarit Kraus,et al.  AutoMed: an automated mediator for multi-issue bilateral negotiations , 2012, Autonomous Agents and Multi-Agent Systems.

[25]  Dongmo Zhang,et al.  Using GDL to Represent Domain Knowledge for Automated Negotiations , 2016, AAMAS Workshops.

[26]  Csaba Szepesvári,et al.  Bandit Based Monte-Carlo Planning , 2006, ECML.

[27]  K. Sycara Problem Restructuring in Negotiation , 1991 .