An efficient automated negotiation strategy for complex environments

A complex and challenging bilateral negotiation environment for rational autonomous agents is where agents negotiate multi-issue contracts in unknown application domains with unknown opponents under real-time constraints. In this paper we present a negotiation strategy called EMAR for this kind of environment that relies on a combination of Empirical Mode Decomposition ([email protected]?D) and Autoregressive Moving Average ([email protected]?MA). EMAR enables a negotiating agent to acquire an opponent model and to use this model for adjusting its target utility in real-time on the basis of an adaptive concession-making mechanism. Experimental results show that EMAR outperforms best performing agents from the recent Automated Negotiating Agents Competitions (ANAC) in a wide range of application domains. Moreover, an analysis based on empirical game theory is provided that shows the robustness of EMAR in different negotiation contexts.

[1]  S. S. Shen,et al.  Applications of Hilbert–Huang transform to non‐stationary financial time series analysis , 2003 .

[2]  Abhinav Mittal,et al.  A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach , 2012, Eng. Appl. Artif. Intell..

[3]  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.

[4]  Sarit Kraus,et al.  Evaluating practical negotiating agents: Results and analysis of the 2011 international competition , 2013, Artif. Intell..

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

[6]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[7]  Kuldeep Kumar,et al.  Agent-based negotiation and decision making for dynamic supply chain formation , 2009, Eng. Appl. Artif. Intell..

[8]  Luc De Raedt,et al.  Proceedings of the 20th European Conference on Artificial Intelligence , 2012 .

[9]  Sung-Bong Yang,et al.  An efficient multilateral negotiation system for pervasive computing environments , 2008, Eng. Appl. Artif. Intell..

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

[11]  K. Lai,et al.  Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm , 2008 .

[12]  P. Flandrin,et al.  Empirical Mode Decomposition , 2012 .

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

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

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

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

[17]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[18]  Gregory E. Kersten,et al.  Predicting opponent's moves in electronic negotiations using neural networks , 2008, Expert Syst. Appl..

[19]  Gerhard Weiss,et al.  A Novel Strategy for Efficient Negotiation in Complex Environments , 2012, MATES.

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

[21]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[22]  Michael P. Wellman,et al.  Empirical Game-Theoretic Analysis of Chaturanga , 2006 .

[23]  Sarit Kraus,et al.  Genius: negotiation environment for heterogeneous agents , 2009, AAMAS.

[24]  Michael P. Wellman,et al.  Empirical game-theoretic analysis of the TAC Supply Chain game , 2007, AAMAS '07.

[25]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[26]  Francesco M. Donini,et al.  Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces , 2008, Autonomous Agents and Multi-Agent Systems.

[27]  Sandip Sen,et al.  Modeling opponent decision in repeated one-shot negotiations , 2005, AAMAS '05.

[28]  Raymond Y. K. Lau,et al.  Knowledge discovery for adaptive negotiation agents in e-marketplaces , 2008, Decis. Support Syst..

[29]  Chongming Hou.  Predicting agents tactics in automated negotiation , 2004 .

[30]  Sarit Kraus,et al.  The Second Automated Negotiating Agents Competition (ANAC2011) , 2013, Complex Automated Negotiations.

[31]  Katia Sycara,et al.  Literature Review on Multi-attribute Negotiations , 2004 .

[32]  Felix A. Fischer,et al.  An integrated framework for adaptive reasoning about conversation patterns , 2005, AAMAS '05.

[33]  Michael N. Huhns,et al.  Concurrent Multiple-Issue Negotiation for Internet-Based Services , 2006, IEEE Internet Computing.

[34]  Nicholas R. Jennings,et al.  Using Gaussian Processes to Optimise Concession in Complex Negotiations against Unknown Opponents , 2011, IJCAI.