From Research to Practice: Automated Negotiations with People

The development of proficient automated agents has flourished in recent years, yet making the agents interact with people has still received little attention. This is mainly due to the unpredictable nature of people and their negotiation behavior, though complexity and costs attached to experimentation with people, starting from the design and ending with the evaluation process, is also a factor. Even so, succeeding in designing proficient automated agents remains an important objective. In recent years, we have invested much effort in facilitating the design and evaluation of automated agents interacting with people, making it more accessible to researchers. We have created two distinct environments for bargaining agents, as well as proposing a novel approach for evaluating agents. These are key factors for making automated agents become a reality rather than remain theoretical.

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

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

[3]  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..

[4]  Sarit Kraus,et al.  Supporting the Design of General Automated Negotiators , 2010 .

[5]  R. Luce,et al.  Individual Choice Behavior: A Theoretical Analysis. , 1960 .

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

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

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

[9]  Matthew P. Wand,et al.  Kernel Smoothing , 1995 .

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

[11]  Randall Davis,et al.  Negotiation as a Metaphor for Distributed Problem Solving , 1988, Artificial Intelligence.

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

[13]  Colin Camerer Behavioral Game Theory , 1990 .

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

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

[16]  R. Duncan Luce,et al.  Individual Choice Behavior: A Theoretical Analysis , 1979 .

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

[18]  Ya'akov Gal,et al.  Facilitating the Evaluation of Automated Negotiators using Peer Designed Agents , 2010, AAAI.

[19]  American Economic Association Progress in Behavioral Game Theory , 2003 .

[20]  Colin Camerer Behavioral Game Theory: Experiments in Strategic Interaction , 2003 .

[21]  Stuart M. Shieber,et al.  Agent decision-making in open mixed networks , 2010, Artif. Intell..

[22]  John S. J. Hsu,et al.  Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers , 1999 .

[23]  Ya'akov Gal,et al.  An Adaptive Agent for Negotiating with People in Different Cultures , 2011, TIST.

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