A multi-demand negotiation model based on fuzzy rules elicited via psychological experiments

This paper proposes a multi-demand negotiation model that takes the effect of human users’ psychological characteristics into consideration. Specifically, in our model each negotiating agent's preference over its demands can be changed, according to human users’ attitudes to risk, patience and regret, during the course of a negotiation. And the change of preference structures is determined by fuzzy logic rules, which are elicited through our psychological experiments. The applicability of our model is illustrated by using our model to solve a problem of political negotiation between two countries. Moreover, we do lots of theoretical and empirical analyses to reveal some insights into our model. In addition, to compare our model with existing ones, we make a survey on fuzzy logic based negotiation, and discuss the similarities and differences between our negotiation model and various consensus models.

[1]  Michael Wagner,et al.  A Comparative Study on Vector Similarity Methods for Offer Generation in Multi-attribute Negotiation , 2015, Australasian Conference on Artificial Intelligence.

[2]  Francisco Chiclana,et al.  Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information , 2016, Knowl. Based Syst..

[3]  Tanmoy Chakraborty,et al.  A behavioral study of bargaining in social networks , 2010, EC '10.

[4]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[5]  Takayuki Ito,et al.  A Dependency-Based Automated Negotiation Mechanism for a Hypergraph Utility Model , 2015, 2015 IIAI 4th International Congress on Advanced Applied Informatics.

[6]  Etienne E. Kerre,et al.  Defuzzification: criteria and classification , 1999, Fuzzy Sets Syst..

[7]  Sarit Kraus,et al.  Can automated agents proficiently negotiate with humans? , 2010, CACM.

[8]  Feng Li,et al.  Application of strategic fuzzy games to wage increase negotiation and decision problems , 2012, Expert Syst. Appl..

[9]  Dongmo Zhang,et al.  A Logical Multidemand Bargaining Model with Integrity Constraints , 2016, Int. J. Intell. Syst..

[10]  Ya'akov Gal,et al.  A cultural sensitive agent for human-computer negotiation , 2012, AAMAS.

[11]  Yejun Xu,et al.  A consensus model for hesitant fuzzy preference relations and its application in water allocation management , 2017, Appl. Soft Comput..

[12]  Xudong Luo,et al.  Offer Evaluation and Trade-Off Making in Automated Negotiation Based on Intuitionistic Fuzzy Constraints , 2016, PRIMA.

[13]  Nir Vulkan,et al.  Entrepreneurs’ negotiation behavior , 2015 .

[14]  B. Schwartz,et al.  Maximizing Versus Satisficing : Happiness Is a Matter of Choice , 2002 .

[15]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[16]  Asuman E. Ozdaglar,et al.  Opinion Dynamics and Learning in Social Networks , 2010, Dyn. Games Appl..

[17]  Jemal H. Abawajy,et al.  Strategies for agent-based negotiation in e-trade , 2012 .

[18]  Francesco Costantino,et al.  Multistage bilateral bargaining model with incomplete information—A fuzzy approach , 2009 .

[19]  Gregory B. Northcraft,et al.  Unlocking integrative potential: Expressed emotional ambivalence and negotiation outcomes , 2015 .

[20]  Fred S. Roberts,et al.  Applied Combinatorics, Second Edition , 2009 .

[21]  Yan Zhang,et al.  An Ordinal Bargaining Solution with Fixed-Point Property , 2008, J. Artif. Intell. Res..

[22]  Nicholas R. Jennings,et al.  An Overview of the Results and Insights from the Third Automated Negotiating Agents Competition (ANAC2012) , 2014, Novel Insights in Agent-based Complex Automated Negotiation.

[23]  Shaohua Tang,et al.  A description logic-based policy compliance checker for trust negotiation , 2016, Peer Peer Netw. Appl..

[24]  Nick Bassiliades,et al.  A System for Automated Agent Negotiation with Defeasible Logic-Based Strategies - Preliminary Report , 2004, RuleML.

[25]  Dirk Helbing,et al.  Bargaining over waiting time in ultimatum game experiments. , 2012, Social science research.

[26]  Michael Luck,et al.  Negotiation strategy for continuous long-term tasks in a grid environment , 2015, Autonomous Agents and Multi-Agent Systems.

[27]  M. Degroot Reaching a Consensus , 1974 .

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

[29]  Dongmo Zhang,et al.  A Sequential Model for Reasoning about Bargaining in Logic Programs , 2013, LPNMR.

[30]  Changyong Liang,et al.  A trust induced recommendation mechanism for reaching consensus in group decision making , 2017, Knowl. Based Syst..

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

[32]  Nicholas R. Jennings,et al.  A Fuzzy-Logic Based Bidding Strategy for Autonomous Agents in Continuous Double Auctions , 2003, IEEE Trans. Knowl. Data Eng..

[33]  Enrique Herrera-Viedma,et al.  Consensus reaching model in the complex and dynamic MAGDM problem , 2016, Knowl. Based Syst..

[34]  Javier Carbó,et al.  Reaching agreements through fuzzy counter-offers , 2003 .

[35]  M. Yan A Fuzzy Logic Enhanced Bargaining Model for Business Pricing Decision Support in Joint Venture Projects , 2011 .

[36]  Michael Luck,et al.  Adjustable Fuzzy Inference for Adaptive Grid Resource Negotiation , 2015, Next Frontier in Agent-Based Complex Automated Negotiation.

[37]  Dongmo Zhang,et al.  A logic-based axiomatic model of bargaining , 2010, Artif. Intell..

[38]  Dzenana Donko,et al.  Temporal dynamics of changes in group user's preferences in recommender systems , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[39]  Gergana Y. Nenkov,et al.  A short form of the Maximization Scale: Factor structure, reliability and validity studies , 2008, Judgment and Decision Making.

[40]  Anikó Ekárt,et al.  Bi-directional double auction for financial market simulation , 2013, AAMAS.

[41]  Stathes Hadjiefthymiades,et al.  An adaptive fuzzy logic system for automated negotiations , 2015, Fuzzy Sets Syst..

[42]  Xudong Luo,et al.  Adaptive Conceding Strategies for Negotiating Agents Based on Interval Type-2 Fuzzy Logic , 2016, KSEM.

[43]  N. H. Timm Applied Multivariate Analysis , 2002 .

[44]  B. Schwartz,et al.  Maximizing versus satisficing: happiness is a matter of choice , 2002 .

[45]  Hong-li Wang,et al.  Logic for Automated Negotiation in E-Business , 2008, 2008 ISECS International Colloquium on Computing, Communication, Control, and Management.

[46]  Ho-fung Leung,et al.  CUHKAgent: An Adaptive Negotiation Strategy for Bilateral Negotiations over Multiple Items , 2014, Novel Insights in Agent-based Complex Automated Negotiation.

[47]  K. Kirby,et al.  Delay-discounting probabilistic rewards: Rates decrease as amounts increase , 1996, Psychonomic bulletin & review.

[48]  Stathes Hadjiefthymiades,et al.  Automatic Fuzzy rules generation for the deadline calculation of a seller agent , 2009, 2009 International Symposium on Autonomous Decentralized Systems.

[49]  Faouzi Masmoudi,et al.  A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains , 2017, J. Intell. Manuf..

[50]  S. Lauermann Dynamic Matching and Bargaining Games: A General Approach , 2013 .

[51]  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 .

[52]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[53]  Enrique Herrera-Viedma,et al.  Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors , 2016, Decis. Support Syst..

[54]  Bo An,et al.  Bilateral bargaining with one-sided uncertain reserve prices , 2013, Autonomous Agents and Multi-Agent Systems.

[55]  Enrique Herrera-Viedma,et al.  Fuzzy decision making and consensus: Challenges , 2015, J. Intell. Fuzzy Syst..

[56]  José M. Molina López,et al.  Reaching Agreements through Fuzzy Counter-Offers , 2003, ICWE.

[57]  Donelson R. Forsyth,et al.  Interdependent Construal of Self and the Endorsement of Conflict Resolution Strategies in Interpersonal, Intergroup, and International Disputes , 2002 .

[58]  W. Maddux,et al.  Cultural Variance in the Interpersonal Effects of Anger in Negotiations , 2010, Psychological science.

[59]  Koen V. Hindriks,et al.  A Survey of Opponent Modeling Techniques in Automated Negotiation , 2016, AAMAS.

[60]  Lin Li,et al.  An agent-based fuzzy constraint-directed negotiation model for solving supply chain planning and scheduling problems , 2016, Appl. Soft Comput..

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

[62]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[63]  Nejat Anbarci,et al.  Robustness of intermediate agreements and bargaining solutions , 2013, Games Econ. Behav..

[64]  R. Fisher,et al.  Getting to Yes: Negotiating Agreement Without Giving in , 1981 .

[65]  Francisco Chiclana,et al.  Multiplicative consistency of intuitionistic reciprocal preference relations and its application to missing values estimation and consensus building , 2014, Knowl. Based Syst..

[66]  Xudong Luo,et al.  Automated negotiation for e-commerce decision making: A goal deliberated agent architecture for multi-strategy selection , 2015, Decis. Support Syst..

[67]  Enrique Herrera-Viedma,et al.  A visual interaction consensus model for social network group decision making with trust propagation , 2017, Knowl. Based Syst..

[68]  Xin Liu,et al.  Modeling Users' Dynamic Preference for Personalized Recommendation , 2015, IJCAI.

[69]  Xudong Luo,et al.  The Task Model of Court Investigation in a Multi-agent System of Argumentation in Court , 2013, LORI.

[70]  Stathes Hadjiefthymiades,et al.  A Fuzzy Logic System for Bargaining in Information Markets , 2012, TIST.

[71]  Chi-Bin Cheng,et al.  Buyer-supplier negotiation by fuzzy logic based agents , 2005, Third International Conference on Information Technology and Applications (ICITA'05).

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

[73]  Ya'akov Gal,et al.  Training with automated agents improves people's behavior in negotiation and coordination tasks , 2014, Decis. Support Syst..

[74]  Xudong Luo,et al.  Games Played by Networked Players , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[75]  Francisco Herrera,et al.  Managing consensus based on leadership in opinion dynamics , 2017, Inf. Sci..

[76]  Ho-fung Leung,et al.  A one-shot bargaining strategy for dealing with multifarious opponents , 2013, Applied Intelligence.

[77]  Catholijn M. Jonker,et al.  From problems to protocols: Towards a negotiation handbook , 2013, Decis. Support Syst..

[78]  Mark Fey,et al.  Uncertainty and Incentives in Crisis Bargaining: Game-Free Analysis of International Conflict , 2011 .

[79]  Jun Ma,et al.  Designing a Successful Bidding Strategy Using Fuzzy Sets and Agent Attitudes , 2010 .

[80]  A. Roth,et al.  Risk aversion in bargaining: An experimental study , 1988 .

[81]  Christopher S. Tang,et al.  Using Nash bargaining to design project management contracts under cost uncertainty , 2013 .

[82]  Pawel Sobkowicz,et al.  Opinion dynamics model based on cognitive biases , 2017, J. Artif. Soc. Soc. Simul..

[83]  Ya'akov Gal,et al.  Human-Computer Negotiation in Three-Player Market Settings , 2014, ECAI.

[84]  Nicholas R. Jennings,et al.  Acquiring user tradeoff strategies and preferences for negotiating agents: A default-then-adjust method , 2006, Int. J. Hum. Comput. Stud..

[85]  Ronald R. Yager,et al.  Uninorm aggregation operators , 1996, Fuzzy Sets Syst..

[86]  Jonathan Gratch,et al.  An Effective Conversation Tactic for Creating Value over Repeated Negotiations , 2015, AAMAS.

[87]  Sarit Kraus,et al.  Principles of Automated Negotiation , 2014 .

[88]  Antony S R Manstead,et al.  Supplication and appeasement in conflict and negotiation: The interpersonal effects of disappointment, worry, guilt, and regret. , 2006, Journal of personality and social psychology.

[89]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[90]  Reza Kerachian,et al.  A fuzzy game theoretic approach for groundwater resources management: Application of Rubinstein Bargaining Theory , 2010 .

[91]  Xiao-yu Ma,et al.  A method considering and adjusting individual consistency and group consensus for group decision making with incomplete linguistic preference relations , 2017, Appl. Soft Comput..

[92]  Lynn Vavreck,et al.  The Political Costs of Crisis Bargaining: Presidential Rhetoric and the Role of Party , 2011 .

[93]  Yu Sun,et al.  Fuzzy Logic to Support Bilateral Agent Negotiation in E-commerce , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[94]  Jing Xiao,et al.  Managing consensus and weights in iterative multiple-attribute group decision making , 2016, Appl. Soft Comput..

[95]  Ursula Hess,et al.  The Effect of the Negotiator's Social Power as a Function of the Counterpart's Emotional Reactions in a Computer Mediated Negotiation , 2013 .

[96]  Stathes Hadjiefthymiades,et al.  Buyer agent decision process based on automatic fuzzy rules generation methods , 2010, International Conference on Fuzzy Systems.

[97]  Jie Lu,et al.  A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions , 2017, IEEE Transactions on Fuzzy Systems.

[98]  Jeffrey S. Rosenschein,et al.  Mechanisms for Automated Negotiation in State Oriented Domains , 1996, J. Artif. Intell. Res..

[99]  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.

[100]  Hai Jin,et al.  A Logic Predicate Based Automated Trust Negotiation Model , 2007, 2007 Second International Conference on Communications and Networking in China.

[101]  Xudong Luo,et al.  A negotiation-based model for policy generation , 2017 .

[102]  Harri Ehtamo,et al.  Constraint proposal method for computing Pareto solutions in multi-party negotiations , 2001, Eur. J. Oper. Res..

[103]  Chien-Chang Hsu,et al.  An intelligent negotiation strategy prediction system , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[104]  Wenjun Ma,et al.  A fuzzy logic based bargaining model in discrete domains: Axiom, elicitation and property , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[105]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[106]  Stathes Hadjiefthymiades,et al.  Implicit Deadline Calculation for Seller Agent Bargaining in Information Marketplaces , 2008, 2008 International Conference on Complex, Intelligent and Software Intensive Systems.

[107]  Mohammed S. Karim,et al.  Fuzzy Driven Multi-issue Agent Negotiation on Electronic Marketplace , 2012, ACITY.

[108]  Federico Bergenti,et al.  An analytic study of opinion dynamics in multi-agent systems , 2017, Comput. Math. Appl..

[109]  Francisco Chiclana,et al.  Visual information feedback mechanism and attitudinal prioritisation method for group decision making with triangular fuzzy complementary preference relations , 2014, Inf. Sci..

[110]  Xudong Luo,et al.  Reward and Penalty Functions in Automated Negotiation , 2016, Int. J. Intell. Syst..

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

[112]  K. Robert Lai,et al.  Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling , 2016, Eng. Appl. Artif. Intell..

[113]  Fred Charles Political negotiation as a process of modifying utilities , 2016 .

[114]  C. D. De Dreu,et al.  The interpersonal effects of anger and happiness in negotiations. , 2004, Journal of personality and social psychology.

[115]  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.

[116]  Guido Hertel,et al.  A meta-analysis on gender differences in negotiation outcomes and their moderators. , 2015, Psychological bulletin.

[117]  Chunyan Miao,et al.  A TWO‐STAGE WIN–WIN MULTIATTRIBUTE NEGOTIATION MODEL: OPTIMIZATION AND THEN CONCESSION , 2013, Comput. Intell..

[118]  Akira Okada Coalitional bargaining games with random proposers: Theory and application , 2011, Games Econ. Behav..

[119]  Kwang Mong Sim,et al.  BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[120]  J. Nash THE BARGAINING PROBLEM , 1950, Classics in Game Theory.

[121]  Michael Wooldridge,et al.  Does Game Theory Work? , 2012, IEEE Intelligent Systems.

[122]  Tim Menzies Evaluation Issues with Critical Success Metrics , 1998 .

[123]  Minjie Zhang,et al.  A Dynamic, Optimal Approach for Multi-Issue Negotiation Under Time Constraints , 2014, Novel Insights in Agent-based Complex Automated Negotiation.

[124]  Jin Q. Jeon,et al.  A New Measure for Heated Negotiation in the IPO Syndicate , 2015 .

[125]  Alison Ledgerwood,et al.  Identity Rivalry and the Group Endowment Effect , 2005 .

[126]  Marjan Kuchaki Rafsanjani,et al.  Erratum to: A supplier offer modification approach based on fuzzy systems for automated negotiation in e-commerce , 2016, Inf. Syst. Frontiers.

[127]  Yucheng Dong,et al.  Consensus building in multiperson decision making with heterogeneous preference representation structures: A perspective based on prospect theory , 2015, Appl. Soft Comput..

[128]  Ya'akov Gal,et al.  An Agent Design for Repeated Negotiation and Information Revelation with People , 2013, AAAI.

[129]  Quoc Bao Vo,et al.  From axiomatic to strategic models of bargaining with logical beliefs and goals , 2012, AAMAS.

[130]  Jian Lin,et al.  Ratio-based similarity analysis and consensus building for group decision making with interval reciprocal preference relations , 2016, Appl. Soft Comput..

[131]  Chunyan Miao,et al.  KEMNAD: A KNOWLEDGE ENGINEERING METHODOLOGY FOR NEGOTIATING AGENT DEVELOPMENT , 2012, Comput. Intell..