Advances in AgentBased Complex Automated Negotiations Studies in Computational Intelligence

Complex Automated Negotiations have been widely studied and are becoming an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. In general, automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependency between issues, representation of utility, negotiation protocol, negotiation form (bilateral or multi-party), time constraints, etc. Software agents can support automation or simulation of such complex negotiations on the behalf of their owners, and can provide them with adequate bargaining strategies. In many multi-issue bargaining settings, negotiation becomes more than a zero-sum game, so bargaining agents have an incentive to cooperate in order to achieve efficient win-win agreements. Also, in a complex negotiation, there could be multiple issues that are interdependent. Thus, agent’s utility will become more complex than simple utility functions. Further, negotiation forms and protocols could be different between bilateral situations and multi-party situations. To realize such a complex automated negotiati on, we have to incorporate advanced Artificial Intelligence technologies includes search, CSP, graphical utility models, Bays nets, auctions, utility graphs, predicting and learning methods. Applications could include e-commerce tools, decisionmaking support tools, negotiation support tools, collaboration tools, etc. These issues are explored by researchers from different communities in Autonomous Agents and Multi-Agent systems. They are, for instance, being studied in agent negotiation, multi-issue negotiations, auctions, mechanism design, electronic commerce, voting, secure protocols, matchmaking & brokering, argumentation, and co-operation mechanisms. This book is also edited from some aspects of negotiation researches including theoretical mechanism design of trading based on auctions, allocation mechanism based on negotiation among multi-agent, case-study and analysis of automated negotiations, data engineering issues in negotiations, and so on. The Prediction of Partners’ Behaviors in Self-interested Agents 9 Introduction 9 Negotiation Using Complex Utility Space and Problem 9 Complex Utility Space 9 Existing Biding-Based Protocol 9 Issues on Scalability and Privacy 9 Representative-Based Protocol 9 Revealed Area for Agent 9 Representative-Based Protocol for Multi-issue Negotiation 9 Threshold Adjusting Mechanism 9 The Effects of Revealed Area Based Selection Method 9 Experiments 9 Setting of Experiments 9 Experimental Results 9 Related Work 9 Conclusions 9 References 9 Sequential Auctions for Common Value Objects with Budget Constrained Bidders 29 Introduction 29 Complete Information Setting 29 Second-Price Auctions 29 First-Price Auctions 29 Incomplete Information Setting 29 Equilibrium Bids for Incomplete Information 29 Second Price Rules 29 First Price Rules 29 Related Work 29 Conclusions and Future Work 29 References 29 A Comparative Study of Argumentation- and Proposal-Based Negotiation 46 Introduction 46 The Testbed 46 Three Approaches to the Game 46 Using a Mediator 46 Proposal- and Argumentation-Based Negotiating Agents 46 Summary 46 Evaluation 46 Evaluation Criteria 46 Experimental Setup 46 Evaluation of Experimental Results 46 Summary 46 Conclusion and Future Work 46 References 46 Appendix: Details of Testbed 46 Production 46 Allocation 46 Generating Possible Worlds 46 Evaluating Possible Worlds – the Utility Function 46 The Blind Leading the Blind: A Third-Party Model for Bilateral Multi-issue Negotiations under Incomplete Information 67 Introduction 67 Preliminaries 67 Negotiation Environment 67 Desired Properties 67 Single-Issue Example 67 Framework 67 Constraining the Solution Space 67 Instantiation of the Framework 67 Arbitration Module 67 Protocol Module 67 Example 67 Conclusion 67 References 67 Using Clustering Techniques to Improve Fuzzy Constraint Based Automated Purchase Negotiations 95 Introduction 95 Modeling Agents’ Preferences 95 Multi-Attribute Decision Problems 95 Negotiation Framework 95 Agent’s Domain Knowledge 95 Negotiation Dialogue 95 Decision Mechanisms 95 Operational Semantics 95 Discussion 95 Applying Clustering 95 Experimental Evaluation 95 Empirical Settings 95 Empirical Results 95 Conclusions 95 References 95 Assess Your Opponent: A Bayesian Process for Preference Observation in Multi-attribute Negotiations 124 Introduction 124 Related Work 124 Negotiation Contract 124 Negotiation Protocol 124 Reasoning Model 124 Common Characteristics 124 Agent’s Utility Function 124 Knowledge Classification 124 Iterated Concessions Protocol 124 Strategy 124 Static Strategies 124 Adaptive Strategy 124 Evaluation 124 Evaluation Metrics 124 Experimental Study 124 Conclusion 124 Appendix: Calculation of the Pareto Optimal Frontier 124 References 124 Designing Risk-Averse Bidding Strategies in Sequential Auctions for Transportation Orders 143 Introduction 143 Goals and Organisation of This Paper 143 Modeling Utility Functions under Risk 143 The Importance of Risk Adversity in Decision Making: An Example 143 Bidding in Sequential Auctions with Complementarities 143 Optimal Bidding Policy for Sequential 2nd Price (Vickrey) Auctions 143 Optimal Bidding Policy for Sequential 1st Price Auctions: Numerical Solutions 143 Heuristic Addressing Multiple Copies 143 Experimental Analysis 143 Experimental Analysis of Distribution of Risk Profiles of Winning Bidders 143 Complex Bundle Preferences: Effect of Auction Orders 143 Experimental Analysis of Market Efficiency and Auctioneer Revenue 143 Conclusions and Further Work 143 References 143 CPN-Based State Analysis and Prediction for Multi-agent Scheduling and Planning 165 Introduction 165 Petri Nets and Colored Petri Nets 165 Petri Nets 165 Colored Petri Nets 165 Mathematical Formats of CPNs 165 State Analysis and Prediction of CPNs 165 Transition Checking Function 165 State Prediction Function 165 State Checking Function 165 Success Possibility Analysis 165 Improved CPN Models for Possibility Analysis 165 State Possibility Evaluation 165 Applications of CPN-Based State Analysis and Predictions in Multi-agent Scheduling and Planning 165 Agent-Based Scheduling and Planning Systems 165 Using CPNs to Model Scheduling and Planning Problems 165 CPN Based State Analysis and Predictions 165 Example 165 Conclusions 165 References 165 Adaptive Commitment Management Strategy Profiles for Concurrent Negotiations 181 Introduction 181 Concurrent Negotiation 181 Coordinating Concurrent Negotiations 181 Protocol 181 Decommitment 181 Commitment Management 181 CMSs for One Resource 181 Adaptive CMS Profiles 181 Empirical Results 181 Objectives 181 Experimental Settings 181 PerformanceMeasure 181 Results and Observations 181 Conclusion 181 References 181 Analyses of Task Allocation Based on Credit Constraints 200 Introduction 200 Preliminaries 200 Model 200 Initial Payment 200 Contract 200 Protocol 200 Experiments 200 Discussion 200 Conclusions 200 References 200 Front Matter....Pages - The Prediction of Partners’ Behaviors in Self-interested Agents....Pages 1-20 Sequential Auctions for Common Value Objects with Budget Constrained Bidders....Pages 21-37 A Comparative Study of Argumentation- and Proposal-Based Negotiation....Pages 39-59 The Blind Leading the Blind: A Third-Party Model for Bilateral Multi-issue Negotiations under Incomplete Information....Pages 61-88 Using Clustering Techniques to Improve Fuzzy Constraint Based Automated Purchase Negotiations....Pages 89-117 Assess Your Opponent: A Bayesian Process for Preference Observation in Multi-attribute Negotiations....Pages 119-137 Designing Risk-Averse Bidding Strategies in Sequential Auctions for Transportation Orders....Pages 139-160 CPN-Based State Analysis and Prediction for Multi-agent Scheduling and Planning....Pages 161-176 Adaptive Commitment Management Strategy Profiles for Concurrent Negotiations....Pages 177-195 Analyses of Task Allocation Based on Credit Constraints....Pages 197-214 Erratum: The Prediction of Partners’ Behaviors in Self-interested Agents....Pages E1-E1 Back Matter....Pages -