Automated Agents that Proficiently Negotiate with People: Can We Keep People out of the Evaluation Loop

Research on automated negotiators has flourished in recent years. Among the important issues considered is how these automated negotiators can proficiently negotiate with people. To validate this, many experimentations with people are required. Nonetheless, conducting experiments with people is timely and costly, making the evaluation of these automated negotiators a very difficult process. Moreover, each revision of the agent’s strategies requires to gather an additional set of people for the experiments. In this paper we investigate the use of Peer Designed Agents (PDAs) – computer agents developed by human subjects – as a method for evaluating automated negotiators. We have examined the negotiation results and its dynamics in extensive simulations with more than 300 human negotiators and more than 50 PDAs in two distinct negotiation environments. Results show that computer agents perform better than PDAs in the same negotiation contexts in which they perform better than people, and that on average, they exhibit the same measure of generosity towards their negotiation partners. Thus, we found that using the method of peer designed negotiators embodies the promise of relieving some of the need for people when evaluating automated negotiators.

[1]  Theo Offerman,et al.  Cooperation in an Overlapping Generations Experiment , 1999, Games Econ. Behav..

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

[3]  Sarit Kraus,et al.  Modeling Agents through Bounded Rationality Theories , 2009, IJCAI.

[4]  Laura Boteler,et al.  Virtual Reality Skills Training for Health Care Professionals in Alcohol Screening and Brief Intervention , 2009, The Journal of the American Board of Family Medicine.

[5]  Shou-De Lin,et al.  Designing the Market Game for a Trading Agent Competition , 2001, IEEE Internet Comput..

[6]  Ya'akov Gal,et al.  Learning Social Preferences in Games , 2004, AAAI.

[7]  Sarit Kraus,et al.  Programming agents as a means of capturing self-strategy , 2008, AAMAS.

[8]  Koen V. Hindriks,et al.  Analysis of Negotiation Dynamics , 2007, CIA.

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

[10]  Ya'akov Gal,et al.  Adapting to agents' personalities in negotiation , 2005, AAMAS '05.

[11]  Sarit Kraus,et al.  The influence of social dependencies on decision-making: initial investigations with a new game , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[12]  Shou-De Lin,et al.  A trading agent competition , 2000 .

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

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

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

[16]  Sarit Kraus,et al.  Efficient agents for cliff-edge environments with a large set of decision options , 2006, AAMAS '06.

[17]  Reinhard Selten,et al.  How to play (3×3)-games.: A strategy method experiment , 2003, Games Econ. Behav..

[18]  Sarit Kraus,et al.  Facing the challenge of human-agent negotiations via effective general opponent modeling , 2009, AAMAS.

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

[20]  Felix A. Fischer,et al.  Cooperative Information Agents XI , 2008 .

[21]  David R. Traum,et al.  Multi-party, Multi-issue, Multi-strategy Negotiation for Multi-modal Virtual Agents , 2008, IVA.

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

[23]  Claudio Bartolini,et al.  AutONA: a system for automated multiple 1-1 negotiation , 2003, EC '03.

[24]  R. Selten,et al.  Duopoly Strategies Programmed by Experienced Players , 1997 .

[25]  Dale E. Olsen Interview and Interrogation Training using a Computer-Simulated Subject , 1997 .

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

[27]  Sarit Kraus,et al.  Can automated agents proficiently negotiate with humans? , 2010, CACM.

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

[29]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[30]  R. McKelvey,et al.  An experimental study of the centipede game , 1992 .

[31]  A. Tversky,et al.  The framing of decisions and the psychology of choice. , 1981, Science.