Negotiating with Unknown Opponents Toward Multi-lateral Agreement in Real-Time Domains

Automated negotiation has been gained a mass of attention mainly because of its broad application potential in many fields. This work studies a prominent class of automated negotiations – multi-lateral multi-issue negotiations under real-time constraints, where the negotiation agents are given no prior information about their opponents’ preferences over the negotiation outcome space. A novel negotiation approach is proposed that enables an agent to obtain efficient agreements in this challenging multi-lateral negotiations. The proposed approach achieves that goal by, (1) employing sparse pseudo-input Gaussian processes (SPGPs) to model opponents, (2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and (3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation.

[1]  Gerhard Weiss,et al.  Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains , 2013, IJCAI.

[2]  Gerhard Weiss,et al.  An Efficient and Adaptive Approach to Negotiation in Complex Environments , 2012, ECAI.

[3]  Ryszard Kowalczyk,et al.  Predicting partner's behaviour in agent negotiation , 2006, AAMAS '06.

[4]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[5]  Nicholas R. Jennings,et al.  Learning on opponent's preferences to make effective multi-issue negotiation trade-offs , 2004, ICEC '04.

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

[7]  Gerhard Weiss,et al.  An approach to complex agent-based negotiations via effectively modeling unknown opponents , 2015, Expert Syst. Appl..

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

[9]  Gerhard Weiss,et al.  Optimizing complex automated negotiation using sparse pseudo-input gaussian processes , 2013, AAMAS.

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

[11]  Nicholas R. Jennings,et al.  Negotiating Concurrently with Unknown Opponents in omplex, Real-Time Domains , 2012, ECAI.

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

[13]  Ho-fung Leung,et al.  ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[14]  Koen V. Hindriks,et al.  Acceptance Conditions in Automated Negotiation , 2013, Complex Automated Negotiations.

[15]  Zili Zhang,et al.  Toward Efficient Agreements in Real-Time Multilateral Agent-Based Negotiations , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[16]  Gerhard Weiss,et al.  Evaluating Practical Automated Negotiation Based on Spatial Evolutionary Game Theory , 2014, KI.

[17]  Ho-fung Leung,et al.  An efficient and robust negotiating strategy in bilateral negotiations over multiple items , 2014, Eng. Appl. Artif. Intell..

[18]  Gerhard Weiss,et al.  An Intelligent Agent for Bilateral Negotiation with Unknown Opponents in Continuous-Time Domains , 2014, TAAS.

[19]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.