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Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 276 pages, kõrgus x laius: 235x155 mm, kaal: 5738 g, 21 Illustrations, color; 37 Illustrations, black and white; XXI, 276 p. 58 illus., 21 illus. in color., 1 Hardback
  • Sari: Springer Theses
  • Ilmumisaeg: 08-Feb-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319282425
  • ISBN-13: 9783319282428
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Exploring the Strategy Space of Negotiating Agents: A Framework for Bidding, Learning and Accepting in Automated Negotiation 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 276 pages, kõrgus x laius: 235x155 mm, kaal: 5738 g, 21 Illustrations, color; 37 Illustrations, black and white; XXI, 276 p. 58 illus., 21 illus. in color., 1 Hardback
  • Sari: Springer Theses
  • Ilmumisaeg: 08-Feb-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319282425
  • ISBN-13: 9783319282428
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319282428.html
Märksõnad:

This book reports on an outstanding thesis that has significantly advanced the state-of-the-art in the area of automated negotiation. It gives new practical and theoretical insights into the design and evaluation of automated negotiators. It describes an innovative negotiating agent framework that enables systematic exploration of the space of possible negotiation strategies by recombining different agent components. Using this framework, new and effective ways are formulated for an agent to learn, bid, and accept during a negotiation. The findings have been evaluated in four annual instantiations of the International Automated Negotiating Agents Competition (ANAC), the results of which are also outlined here. The book also describes several methodologies for evaluating and comparing negotiation strategies and components, with a special emphasis on performance and accuracy measures.

1 Introduction 1(14)
1.1 Negotiation
1(1)
1.2 An Automated Negotiator
2(2)
1.2.1 Generic Negotiation Strategies
3(1)
1.3 Bidding, Learning, and Accepting
4(1)
1.4 Research Questions
5(2)
1.4.1 Designing a Component-Based Automated Negotiation Framework
6(1)
1.4.2 Analyzing the Negotiating Strategy Components
6(1)
1.5 Thesis Scope
7(1)
1.6 Dissertation Outline
8(3)
1.6.1 The Fundamentals
9(1)
1.6.2 The BOA Architecture
9(1)
1.6.3 Analyzing the Components of an Automated Negotiator
10(1)
1.6.4 Putting the Pieces Together
10(1)
1.7 Contributions
11(1)
References
12(3)
2 Background 15(38)
2.1 Introduction
15(1)
2.2 Terminology
16(7)
2.2.1 Negotiation Domain
16(1)
2.2.2 Negotiation Protocol
17(1)
2.2.3 Preference Profiles
18(3)
2.2.4 Outcome Spaces
21(2)
2.3 Negotiating Strategies
23(10)
2.3.1 Architecture of Negotiation Strategies
24(1)
2.3.2 Negotiation Strategy Space Exploration
25(2)
2.3.3 Bidding Strategies
27(2)
2.3.4 Acceptance Strategies
29(2)
2.3.5 Opponent Models
31(2)
2.4 Evaluation Methodologies
33(10)
2.4.1 Environments for Evaluating Negotiating Agents
33(3)
2.4.2 Negotiating Agent Competitions
36(1)
2.4.3 Evaluating Performance of Negotiation Strategies
37(2)
2.4.4 Evaluating Learning Methods
39(4)
References
43(10)
3 A Component-Based Architecture to Explore the Space of Negotiation Strategies 53(18)
3.1 Introduction
53(2)
3.2 The BOA Agent Architecture
55(5)
3.2.1 The BOA Agent
55(2)
3.2.2 Employing the BOA Architecture
57(3)
3.3 Decoupling Existing Agents
60(7)
3.3.1 Identifying the Components
60(3)
3.3.2 Testing Equivalence of BOA Agents
63(4)
3.4 Conclusion
67(1)
References
67(4)
4 Effective Acceptance Conditions 71(20)
4.1 Introduction
71(2)
4.2 Acceptance Conditions in Negotiation
73(3)
4.2.1 A Formal Model of Accepting
73(1)
4.2.2 Acceptance Conditions
74(1)
4.2.3 Existing Acceptance Conditions
75(1)
4.3 Combined Acceptance Conditions
76(2)
4.4 Experiments
78(9)
4.4.1 Detailed Experimental Setup
78(2)
4.4.2 Hypotheses and Experimental Results
80(7)
4.5 Conclusion
87(1)
References
88(3)
5 Accepting Optimally with Incomplete Information 91(20)
5.1 Introduction
91(1)
5.2 Decision Making in Negotiation Under Uncertainty
92(4)
5.2.1 Stochastic Behavior in Negotiation
93(1)
5.2.2 Optimal Stopping in Negotiation
93(3)
5.3 Accepting Random Offers
96(7)
5.3.1 Uniformly Random Behavior
96(2)
5.3.2 Non-Uniform Random Behavior
98(1)
5.3.3 Experiments
99(2)
5.3.4 When Optimal Stopping Is Most Effective
101(2)
5.4 Time Dependent Offers
103(5)
5.4.1 Uniformly Unpredictable Offers
103(1)
5.4.2 Arbitrarily Unpredictable Offers
104(2)
5.4.3 Experiments
106(2)
5.5 Conclusion
108(1)
References
108(3)
6 Measuring the Performance of Online Opponent Models 111(18)
6.1 Introduction
111(1)
6.2 Evaluating Opponent Models
112(3)
6.2.1 Influence of the Agent's Strategy
113(1)
6.2.2 Influence of the Opponent's Strategy
113(1)
6.2.3 Influence of the Negotiation Scenario
114(1)
6.3 Measuring the Performance of Opponent Models
115(2)
6.3.1 Negotiation Strategies of the Agents
115(1)
6.3.2 Negotiation Strategies of the Opponents
115(1)
6.3.3 Negotiation Scenarios
116(1)
6.3.4 Quality Measures for Opponent Models
116(1)
6.4 Experiments
117(2)
6.4.1 Experimental Setup
117(1)
6.4.2 Opponent Models
118(1)
6.5 Results
119(6)
6.5.1 Overall Performance of Opponent Models
119(2)
6.5.2 Influence of the Negotiation Setting
121(1)
6.5.3 Influence of the Agent's Strategy
122(1)
6.5.4 Influence of the Opponent's Strategy
123(1)
6.5.5 Influence of the Negotiation Scenario
124(1)
6.6 Conclusion
125(1)
References
126(3)
7 Predicting the Performance of Opponent Models 129(18)
7.1 Introduction
129(2)
7.2 Measuring the Quality of Opponent Models
131(7)
7.2.1 Preliminaries
131(1)
7.2.2 Selection of Opponent Models
132(1)
7.2.3 Selection of Accuracy Measures
133(2)
7.2.4 Quantifying the Estimation Accuracy
135(2)
7.2.5 Quantifying the Accuracy/Performance Relationship
137(1)
7.3 Experimental Analysis
138(6)
7.3.1 Evaluating the Estimation Accuracy of Opponent Models
138(4)
7.3.2 Evaluating the Accuracy Versus Performance Relationship
142(1)
7.3.3 Evaluating the Usefulness of Accuracy Measures
143(1)
7.4 Conclusion
144(1)
References
145(2)
8 A Quantitative Concession-Based Classification Method of Bidding Strategies 147(20)
8.1 Introduction
147(2)
8.2 Concession Making in Negotiation
149(1)
8.3 Concession Rate
150(5)
8.3.1 An Example
151(1)
8.3.2 Formal Definition
152(2)
8.3.3 Classifying the Agents According to Their Concession Rates
154(1)
8.4 Experiments
155(9)
8.4.1 Experimental Setup
155(1)
8.4.2 Experimental Results for ANAC 2010
156(3)
8.4.3 Experimental Results for ANAC 2011
159(5)
8.5 Conclusion and Discussion
164(1)
References
165(2)
9 Optimal Non-adaptive Concession Strategies 167(14)
9.1 Introduction
167(1)
9.2 An Example
168(1)
9.3 Making Non-adaptive Concessions
169(2)
9.4 Conceding and Accepting
171(2)
9.5 Making Optimal Offers
173(5)
9.6 Experiments
178(1)
9.7 Conclusion
179(1)
References
180(1)
10 Putting the Pieces Together 181(14)
10.1 Introduction
181(2)
10.2 Measuring the Contribution of Strategy Components
183(1)
10.3 Experiments
184(2)
10.4 Component Contribution
186(6)
10.4.1 The Influence of the Opponent
188(2)
10.4.2 Interaction Effects
190(1)
10.4.3 Combining the Best Components
191(1)
10.5 Conclusion
192(1)
References
193(2)
11 Conclusion 195(20)
11.1 Contributions
195(3)
11.2 Answers to Our Research Questions
198(1)
11.3 Outlook and Challenges
199(12)
11.3.1 The BOA Architecture
199(1)
11.3.2 Bidding
200(2)
11.3.3 Opponent Modeling
202(2)
11.3.4 Accepting
204(1)
11.3.5 The Automated Negotiating Agents Competition
204(2)
11.3.6 Robustness of Negotiation Strategies
206(2)
11.3.7 Negotiation Setting
208(1)
11.3.8 Application to Human Negotiations
209(2)
References
211(4)
Appendix A: GENIUS: An Environment to Support the Design of Generic Automated Negotiators 215(8)
Appendix B: The Automated Negotiating Agents Competition (ANAC) 223(18)
Appendix C: ANAC 2010 241(8)
Appendix D: ANAC 2011 249(10)
Appendix E: ANAC 2012 259(6)
Appendix F: ANAC 2013 265(6)
Summary 271