An artificial negotiating agent modeling approach embedding dynamic offer generating and cognitive layer

Abstract In this paper, a dynamic offer generating unit and cognitive layer are suggested for artificial agents based negotiation systems. For this purpose, first, adaptive time and behavior dependent tactics are developed taking advantages from time continuity and dynamics aspects (features) integrated in their modeling. Then, a negotiation strategy (bilateral over single issue) based on these two tactics is suggested. Second, a cognitive negotiation model for a negotiator agent is developed using Win–Lose and Win–Win orientations which will be formed based on personality factors. Afterwards, an experimental validation is conducted for testing applicability of time dependent tactics, the effect of offering time, and the effect of cognitive orientations (Win–Lose and Win–Win) on final negotiation outcomes. The results prove the applicability of the suggested time and behavior dependent tactics as well as the proposed cognitive negotiation model.

[1]  Jordi Sabater-Mir,et al.  Reputation-based decisions for logic-based cognitive agents , 2010, Autonomous Agents and Multi-Agent Systems.

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

[3]  Sarit Kraus,et al.  Strategic Negotiation in Multiagent Environments , 2001, Intelligent robots and autonomous agents.

[4]  Jeffrey S. Rosenschein and Gilad Zlotkin Rules of Encounter , 1994 .

[5]  Kurosh Madani,et al.  Negotiation Strategies with Incomplete Information and Social and Cognitive System for Intelligent Human-Agent Interaction , 2010, Smart Information and Knowledge Management.

[6]  Jeff M. Bickerton,et al.  Getting to Yes: Negotiating Agreement without Giving in , 2002 .

[7]  Guido Governatori,et al.  DR-NEGOTIATE - A System for Automated Agent Negotiation with Defeasible Logic-Based Strategies , 2005, EEE.

[8]  Sarit Kraus,et al.  Automated Negotiation and Decision Making in Multiagent Environments , 2001, EASSS.

[9]  L. Thompson Negotiation behavior and outcomes: Empirical evidence and theoretical issues. , 1990 .

[10]  Scott A. Neslin,et al.  The Effects Of Negotiator Preferences, Situational Power, And Negotiator Personality On Outcomes Of Business Negotiations , 1985 .

[11]  R. Friedman,et al.  Bargainer Characteristics in Distributive and Integrative Negotiation , 1998 .

[12]  Chester Louis Karrass Give and Take: The Complete Guide to Negotiating Strategies and Tactics , 1974 .

[13]  G. Northcraft,et al.  Behavioral negotiation theory : a framework for conceptualizing dyadic bargaining , 1989 .

[14]  Dean G. Pruitt,et al.  Mismatching the opponent's offers in negotiation☆ , 1985 .

[15]  Sarit Kraus,et al.  Gender-Sensitive Automated Negotiators , 2007, AAAI.

[16]  Fatos Xhafa,et al.  Computational Intelligence for Technology Enhanced Learning , 2010, Computational Intelligence for Technology Enhanced Learning.

[17]  A. Rubinstein A BARGAINING MODEL WITH INCOMPLETE INFORMATION ABOUT TIME PREFERENCES , 1985 .

[18]  Kurosh Madani,et al.  SISINE: A Negotiation Training Dedicated Multi-Player Role-Playing Platform Using Artificial Intelligence Skills , 2010, Computational Intelligence for Technology Enhanced Learning.

[19]  Nicholas R. Jennings,et al.  A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments , 2003, Artif. Intell..

[20]  Jordi Sabater-Mir,et al.  Integrating Image and Reputation Information in BDI Agents , 2008 .

[21]  J. Rubin,et al.  The social psychology of bargaining and negotiation , 1975 .