Abstract
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.
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Technically speaking, GDL does allow you to define utility over non-terminal states, but these utility values do not really have any meaning, as in the end the utility of the terminal state is the only thing that ‘counts’.
GDL defines more relations symbols, but we will not discuss them here because they are not relevant for this paper.
References
Baydin AG, López de Mántaras R, Simoff S, Sierra C (2011) CBR with commonsense reasoning and structure mapping: an application to mediation. In: Ram A, Wiratunga N (eds) Case-based reasoning research and development: 19th international conference on case-based reasoning, ICCBR 2011, London, 12–15 Sept 2011. Proceedings, Springer, Berlin, Heidelberg, pp 378–392. https://doi.org/10.1007/978-3-642-23291-6_28
Bellucci E, Zeleznikow J (2005) Developing negotiation decision support systems that support mediators: case study of the Family_Winner system. Artif Intell Law 13(2):233–271
Bench-Capon TJM (2003) Persuasion in practical argument using value-based argumentation frameworks. J Logic Comput 13(3):429–448
Ceri S, Gottlob G, Tanca L (1989) What you always wanted to know about datalog (and never dared to ask). IEEE Trans Knowl Data Eng 1(1):146–166. https://doi.org/10.1109/69.43410
Chalamish M, Kraus S (2012) AutoMed: an automated mediator for multi-issue bilateral negotiations. Auton Agents Multi Agent Syst 24:536–564. https://doi.org/10.1007/s10458-010-9165-y
Debenham J (2004) Bargaining with information. In: Jennings NR, Sierra C, Sonenberg L, Tambe M (eds) Proceedings third international conference on autonomous agents and multi agent systems AAMAS-2004. ACM Press, New York, pp 664–671
Debenham JK, Simoff S (2006) Negotiating intelligently. In: Bramer M, Coenen F, Tuson A (eds) Proceedings 26th international conference on innovative techniques and applications of artificial intelligence, Cambridge, pp 159–172
Genesereth M, Love N, Pell B (2005) General game playing: overview of the AAAI competition. AI Mag 26(2):62–72
Genesereth MR, Thielscher M (2014) General game playing. Synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool Publishers, San Rafael. https://doi.org/10.2200/S00564ED1V01Y201311AIM024
Jonge Dd, Zhang D (2016) Using gdl to represent domain knowledge for automated negotiations. In: Osman N, Sierra C (eds) Autonomous agents and multiagent systems: aamas 2016 workshops, visionary papers, Singapore, 9–10 May 2016. Revised selected papers, Springer, Cham, pp 134–153
Knuth DE, Moore RW (1975) An analysis of alpha–beta pruning. Artif Intell 6(4):293–326. https://doi.org/10.1016/0004-3702(75)90019-3. http://www.sciencedirect.com/science/article/pii/0004370275900193
Kocsis L, Szepesvári C (2006) Bandit based Monte-Carlo planning. In: Proceedings of the 17th European conference on machine learning, Springer, Berlin, ECML’06, pp 282–293. https://doi.org/10.1007/11871842_29
Kolodner JL, Simpson RL (1989) The mediator: analysis of an early case-based problem solver. Cognit Sci 13(4):507–549
Lin R, Gev Y, Kraus S (2011) Bridging the gap: face-to-face negotiations with automated mediator. IEEE Intell Syst 26(6):40–47
Love N, Genesereth M, Hinrichs T (2006) General game playing: game description language specification. Tech. Rep. LG-2006-01, Stanford University, Stanford. http://logic.stanford.edu/reports/LG-2006-01.pdf
von Neumann J (1959) On the theory of games of strategy. In: Tucker A, Luce R (eds) Contrib Theory Games. Princeton University Press, Princeton, pp 13–42
Parsons S, Sierra C, Jennings N (1998) Agents that reason and negotiate by arguing. J Logic Comput 8(3):261–292
Rahwan I, Pasquier P, Sonenberg L, Dignum F (2009) A formal analysis of interest-based negotiation. Ann Math Artif Intell 55(3–4):253–276
Sardina S, Padgham L (2011) A BDI agent programming language with failure handling, declarative goals, and planning. Auton Agents Multi Agent Syst 23(1):18–70. https://doi.org/10.1007/s10458-010-9130-9
Schei V, Rognes JK (2003) Knowing me, knowing you: own orientation and information about the opponent’s orientation in negotiation. Int J Confl Manag 14(1):43–60
Sierra C, Debenham J (2007) Information-based agency. In: Proceedings of twentieth international joint conference on artificial intelligence IJCAI-07, Hyderabad, pp 1513—1518
Simoff SJ, Debenham J (2002) Curious negotiator. In: Klusch M, Ossowski S, Shehory O (eds) Proceedings of the int. conference on cooperative information agents, CIA-2002, Springer, Heidelberg
Sycara KP (1991) Problem restructuring in negotiation. Manag Sci 37(10):1248–1268
Thielscher M (2010) A general game description language for incomplete information games. In: Fox M, Poole D (eds) Proceedings of the twenty-fourth AAAI conference on artificial intelligence, AAAI 2010, Atlanta, 11–15 July 2010, AAAI Press. http://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/view/1727
Visser W, Hindriks KV, Jonker CM (2011) Interest-based preference reasoning. In: Proceedings of international conference on agents and artificial intelligence ICAART2011, pp 79–88
Wilkenfeld J, Kraus S, Santmire TE, Frain CK (2004) The role of mediation in conflict management: Conditions for successful resolution. In: et al ZM (ed) Multiple paths to knowledge in international relations, Lexington Books
Acknowledgements
This work was sponsored by Endeavour Research Fellowship 4577_2015 awarded by the Australian Department of Education.
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Appendix A
Appendix A
In this section, we present the results obtained with a number of other games. All results in this section were averaged over 100 repetitions (see Tables 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15).
1.1 Experimental results connect-4
1.2 Experimental results breakthrough
1.3 Experimental results free-for-all
1.4 Experimental results Qyshinsu
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de Jonge, D., Trescak, T., Sierra, C. et al. Using Game Description Language for mediated dispute resolution. AI & Soc 34, 767–784 (2019). https://doi.org/10.1007/s00146-017-0790-8
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DOI: https://doi.org/10.1007/s00146-017-0790-8