Supporting the Design of General Automated Negotiators

The design of automated negotiators has been the focus of abundant research in recent years. However, due to difficulties involved in creating generalized agents that can negotiate in several domains and against human counterparts, many automated negotiators are domain specific and their behavior cannot be generalized for other domains. Some of these difficulties arise from the differences inherent within the domains, the need to understand and learn negotiators’ diverse preferences concerning issues of the domain and the different strategies negotiators can undertake. In this paper we present a system that enables alleviation of the difficulties in the design process of general automated negotiators termed Genius, a General Environment for Negotiation with Intelligent multi-purpose Usage Simulation. With the constant introduction of new domains, e-commerce and other applications, which require automated negotiations, generic automated negotiators encompass many benefits and advantages over agents that are designed for a specific domain. Based on experiments conducted with automated agents designed by human subjects using Genius we provide both quantitative and qualitative results to illustrate its efficacy. Our results show the advantages and underlying benefits of using Genius for designing general automated negotiators.

[1]  Chunyan Miao,et al.  Economically Inspired Self-healing Model for Multi-Agent Systems , 2007 .

[2]  Laurent Deveaux,et al.  Bargaining on an Internet Agent-based Market: Behavioral vs. Optimizing Agents , 2001, Electron. Commer. Res..

[3]  Peter Henderson,et al.  Comparison of Some Negotiation Algorithms Using a Tournament-Based Approach , 2002, Agent Technologies, Infrastructures, Tools, and Applications for E-Services.

[4]  James K. Sebenius,et al.  Thinking Coalitionally: Party Arithmetic, Process Opportunism, and Strategic Sequencing , 1992 .

[5]  A. Rubinstein.  Perfect Equilibrium in a Bargaining Model , 1982 .

[6]  Sarit Kraus,et al.  Resolving crises through automated bilateral negotiations , 2008, Artif. Intell..

[7]  Koen V. Hindriks,et al.  Opponent modelling in automated multi-issue negotiation using Bayesian learning , 2008, AAMAS.

[8]  Sarit Kraus,et al.  Understanding how people design trading agents over time , 2008, AAMAS.

[9]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Catholijn M. Jonker,et al.  An agent architecture for multi-attribute negotiation using incomplete preference information , 2007, Autonomous Agents and Multi-Agent Systems.

[11]  Michael P. Wellman,et al.  Autonomous bidding agents - strategies and lessons from the trading agent competition , 2007 .

[12]  Catholijn M. Jonker,et al.  An Agent Architecture for Multi-Attribute Negotiation , 2001, IJCAI.

[13]  Matthias Klusch,et al.  Cooperative Information Agents XII, 12th International Workshop, CIA 2008, Prague, Czech Republic, September 10-12, 2008. Proceedings , 2008, CIA.

[14]  Gregory E. Kersten,et al.  WWW-based negotiation support: design, implementation, and use , 1999, Decis. Support Syst..

[15]  Sarit Kraus,et al.  The Hostage Crisis Simulation , 1992 .

[16]  Katia P. Sycara,et al.  Bayesian learning in negotiation , 1998, Int. J. Hum. Comput. Stud..

[17]  H. Raiffa.  The art and science of negotiation , 1983 .

[18]  Nicholas R. Jennings,et al.  Negotiation decision functions for autonomous agents , 1998, Robotics Auton. Syst..

[19]  Koen V. Hindriks,et al.  Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation , 2008, AMEC/TADA.

[20]  Dorin Militaru,et al.  Negotiation in Multi-Agent Environments , 2011 .

[21]  Jeffrey S. Rosenschein,et al.  Rules of Encounter - Designing Conventions for Automated Negotiation among Computers , 1994 .

[22]  Cuihong Li,et al.  A review of research literature on bilateral negotiations , 2003 .

[23]  A Levenstein.  Art and science of management: negotiation vs. confrontation. , 1984, Nursing management.

[24]  Daniel P. Loucks,et al.  Computer-Assisted Negotiations of Water Resources Conflicts , 1998 .

[25]  K. Hindriks,et al.  Negotiation Dynamics: Analysis, Concession Tactics, and Outcomes , 2007, 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07).

[26]  Avi Pfeffer,et al.  Modeling how humans reason about others with partial information , 2008, AAMAS.

[27]  Nicholas R. Jennings,et al.  Determining successful negotiation strategies: an evolutionary approach , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[28]  Koen V. Hindriks,et al.  Towards an Open Negotiation Architecture for Heterogeneous Agents , 2008, CIA.

[29]  John B. Kidd,et al.  Decisions with Multiple Objectives—Preferences and Value Tradeoffs , 1977 .

[30]  Nicholas R. Jennings,et al.  A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents , 2005, Artificial Intelligence Review.

[31]  Nicholas R. Jennings,et al.  Using similarity criteria to make issue trade-offs in automated negotiations , 2002, Artif. Intell..

[32]  Michael Wooldridge,et al.  A Classification Scheme for Negotiation in Electronic Commerce , 2001 .

[33]  Max H. Bazerman,et al.  Negotiator Rationality and Negotiator Cognition: The Interactive Roles of Prescriptive and Descriptive Research , 1991 .

[34]  Victor Lesser,et al.  Multi-dimensional, multistep negotiation for task allocation in a cooperative system , 2005 .

[35]  Sarit Kraus,et al.  DESIGNING AND BUILDING A NEGOTIATING AUTOMATED AGENT , 1995, Comput. Intell..

[36]  Sarit Kraus,et al.  Negotiating with bounded rational agents in environments with incomplete information using an automated agent , 2008, Artif. Intell..

[37]  Alan H. Karp,et al.  A game tree strategy for automated negotiation , 2004, EC '04.