Heuristics for using CP-nets in utility-based negotiation without knowing utilities

CP-nets have proven to be an effective representation for capturing preferences. However, their use in automated negotiation is not straightforward because, typically, preferences in CP-nets are partially ordered and negotiating agents are required to compare any two outcomes based on a request and an offer in order to negotiate effectively. If agents know how to generate total orders from their CP-nets, they can make this comparison. This paper proposes heuristics that enable the use of CP-nets in utility-based negotiations by generating total orderings. To validate this approach, the paper compares the performance of CP-nets with our heuristics with the performance of UCP-nets that are equipped with complete preference orderings. Our results show that we can achieve comparable performance in terms of the outcome utility. More importantly, one of our proposed heuristics can achieve this performance with significantly smaller number of interactions compared to UCP-nets.

[1]  Onn Shehory,et al.  Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets , 2006 .

[2]  Koen V. Hindriks,et al.  Heuristic-Based Approaches for CP-Nets in Negotiation , 2013, Complex Automated Negotiations.

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

[4]  Nicholas R. Jennings,et al.  A Service-Oriented Negotiation Model between Autonomous Agents , 1997, MAAMAW.

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

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

[7]  Ryszard Kowalczyk,et al.  An Efficient Procedure for Collective Decision-making with CP-nets , 2010, ECAI.

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

[9]  N. R. Jennings,et al.  To appear in: Int Journal of Group Decision and Negotiation GDN2000 Keynote Paper Automated Negotiation: Prospects, Methods and Challenges , 2022 .

[10]  Sarit Kraus,et al.  AutoMed: an automated mediator for bilateral negotiations under time constraints , 2007, AAMAS '07.

[11]  Sarit Kraus,et al.  The First Automated Negotiating Agents Competition (ANAC 2010) , 2012, New Trends in Agent-Based Complex Automated Negotiations.

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

[13]  Pinar Yolum,et al.  Reasoning and Negotiating with Complex Preferences Using CP-Nets , 2008, AMEC/TADA.

[14]  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.

[15]  Koen V. Hindriks,et al.  The first automated negotiating agents competition (ANAC 2010) , 2016 .

[16]  K. B. Venable,et al.  CP-networks: semantics, complexity, approximations and extensions , 2002 .

[17]  Carmel Domshlak,et al.  Hard and soft constraints for reasoning about qualitative conditional preferences , 2006, J. Heuristics.

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

[19]  G. N. Purohit,et al.  Page Ranking Algorithms for Web Mining , 2011 .

[20]  Nicholas R. Jennings,et al.  Using similarity criteria to make issue trade-offs in automated negotiations , 2002, Artif. Intell..

[21]  Jacques L. Koko,et al.  The Art and Science of Negotiation , 2009 .

[22]  Craig Boutilier,et al.  CP-nets: a tool for represent-ing and reasoning with conditional ceteris paribus state-ments , 2004 .

[23]  Toby Walsh,et al.  mCP Nets: Representing and Reasoning with Preferences of Multiple Agents , 2004, AAAI.

[24]  Koen V. Hindriks,et al.  Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation , 2008, AMEC/TADA.

[25]  Han La Poutré,et al.  Automated Interactive Sales Processes , 2011, IEEE Intelligent Systems.

[26]  Sarit Kraus,et al.  GENIUS: AN INTEGRATED ENVIRONMENT FOR SUPPORTING THE DESIGN OF GENERIC AUTOMATED NEGOTIATORS , 2012, Comput. Intell..

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

[28]  Dhananjay K. Gode,et al.  Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality , 1993, Journal of Political Economy.

[29]  Ronen I. Brafman,et al.  UCP-Networks: A Directed Graphical Representation of Conditional Utilities , 2001, UAI.

[30]  Avi Pfeffer,et al.  Simultaneously modeling humans' preferences and their beliefs about others' preferences , 2008, AAMAS.

[31]  Pinar Yolum,et al.  Effective negotiation with partial preference information , 2010, AAMAS.

[32]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[33]  Rory A. Fisher,et al.  Statistical methods and scientific inference. , 1957 .

[34]  R. A. Fisher,et al.  Statistical methods and scientific inference. , 1957 .

[35]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[36]  Catholijn M. Jonker,et al.  The Fifth Automated Negotiating Agents Competition (ANAC 2014) , 2016, ANAC@AAMAS.

[37]  Takayuki Ito,et al.  New Trends in Agent-Based Complex Automated Negotiations , 2011, Studies in Computational Intelligence.

[38]  Peter C. Fishburn,et al.  Utility theory for decision making , 1970 .

[39]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[40]  Nicholas R. Jennings,et al.  On Efficient Procedures for Multi-issue Negotiation , 2006, TADA/AMEC.

[41]  Pinar Yolum,et al.  Learning opponent’s preferences for effective negotiation: an approach based on concept learning , 2010, Autonomous Agents and Multi-Agent Systems.