Academia.eduAcademia.edu

Outline

Title
Abstract
Objectives
Introduction to Agreement Technologies
Conclusions & Discussion
Negotiation Protocol
Plan Negotiation Protocol
Agree Negotiation Protocol
Discussion
Studying the Impact of Intra-Team Agenda
Studying the Impact of a I
Results
Introduction
Intra-Team Protocol
Conclusions
Future Work
References
All Topics
Computer Science
Artificial Intelligence

Complex negotiations in multi-agent systems

Profile image of Victor Sanchez-AnguixVictor Sanchez-Anguix

2013, PhD Thesis

Abstract

Multi-agent systems (MAS) are distributed systems where autonomous entities called agents, either human or software, pursue their own goals. The MAS paradigm has been proposed as the appropriate modeling approach for the deployment of applications like electronic commerce, multi-robot systems, security applications, and so forth. In the MAS community, the vision of open multi-agent system, where heterogeneous agents can enter and leave the system dynamically, has gained strength as a potentially interesting modeling paradigm due to its conceptual relation with technologies like world wide web, grid computing, and virtual organizations. Given the heterogeneity and agent's self-interest, conflict is a candidate phenomenon to arise in multi-agent systems. In the last few years, the term agreement technologies has been used to address all the mechanisms that, directly or indirectly, promote the resolution of conflicts in computational systems like multi-agent systems. Among agreement technologies, automated negotiation is proposed as one key mechanism in conflict resolution due to its analogous use in human conflict resolution. Automated negotiation consists of an automated exchange of proposals carried out by software agents on behalf of their users. The final goal is the achievement of an agreement with all the involved parts. Despite being studied by scholars in Artificial Intelligence for several years, several problems have not been addressed by the scientific community yet. The main objective of this thesis is proposing negotiation models for complex scenarios where the complexity may stem from (i) limited computational capabilities or (ii) the necessity to accommodate the preferences of multiple individuales. In the first part of the thesis we propose a bilateral negotiation model for the problem of negotiation in Ambient Intelligence (AmI), a domain with a special emphasis on computational efficiency due to the limited capability of AmI devices. In the second part of the thesis we propose several negotiation models for agent-based negotiation teams. A negotiation team is a group of individuals that acts together as single negotiation party due to its common interests in the negotiation at hand. The complexity of negotiation teams resides in the fact that despite having common interests, intra-team conflict is also present. As far as we are concerned, the topic of negotiation teams in MAS is introduced with this thesis.

Related papers

A Negotiation Platform for Cooperating Multi-agent Systems
H. Altay Güvenir

Concurrent Engineering, 1993

Distributed artificial intelligence attempts to integrate and coordinate the activities of multiple, intelligent problem solvers that come together to solve complex tasks in domains such as design, medical diagnosis, business management, and so on Due to the different goals, knowledge, and viewpoint of the agents, conflicts might arise at any phase of the problem-solving process. Managing diverse knowledge requires well-organized models of conflict resolution. In this paper, a system for cooperating intelligent agents which openly supports multiagent conflict detection and resolution is described. The system is based on the insights, first, that each agent has its own conflict knowledge which is separated from its domain-level knowledge; and, second, that each agent has its own conflict management knowledge which is not accessible to or known by others. Furthermore, there are no globally-known conflict-resolution strategies. Each agent involved in a conflict chooses a resolution scheme according to its self interest. The problem-solving environment allows a new problem solver to be added, or an existing one to be removed, without requiring any modification of the rest of the system, and therefore achieves open information system semantics Keywords: distributed artificial intelligence, conflict detection and resolution, conflict knowledge, open information system.

View PDFchevron_right
Tasks for agent-based negotiation teams: Analysis, review, and challenges
Vicente Julian, Victor Sanchez-Anguix, Vicent Botti

Engineering Applications of Artificial Intelligence, 2013

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities; (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains; (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, married couple, etc.). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.

View PDFchevron_right
A New Hierarchical Negotiation Scheme on top of a Multi-Agent Architecture
Ahmed Sameh

Proceeding

The increasing demand for distributed entities to interact in complex domains necessitates the need for cooperation, coordination and negotiation to reach agreement. Examples of such systems are: the flow of work and information through cooperating companies (virtual enterprises), international air traffic controllers, the coordination of logistic processes in shipping companies, the use of flexible transport systems in industrial manufacturing and assembly, and the operation of multi-guidance and control systems. This paper describes the design of a model that uses a hierarchy of negotiation protocols to solve conflicts in any of the INTERRAP layers. Two testbeds have been developed to test the proposed hierarchical model: a Desktop Configuration and a Travel Planning Domains. The testbeds proved the effectivness of the model in solving conflicts among several agents in all the INTERRAP layers. These two domains have been chosen because they introduce various types of conflictswhich could be solved using various negotiation protocols in the model.

View PDFchevron_right
Negotiation among autonomous computational agents: principles, analysis and challenges
Michael Wooldridge

Artificial Intelligence Review, 2008

Automated negotiation systems with software agents representing individuals or organizations and capable of reaching agreements through negotiation are becoming increasingly important and pervasive. Examples, to mention a few, include the industrial trend toward agent-based supply chain management, the business trend toward virtual enterprises, and the pivotal role that electronic commerce is increasingly assuming in many organizations. Artificial intelligence (AI) researchers have paid a great deal of attention to automated negotiation over the past decade and a number of prominent models have been proposed in the literature. These models exhibit fairly different features, make use of a diverse range of concepts, and show performance characteristics that vary significantly depending on the negotiation context. As a consequence, assessing and relating individual research contributions is a difficult task. Currently, there is a need to build a framework to define and characterize the essential features that are necessary to conduct automated negotiation and to compare the usage of key concepts in different publications. Furthermore, the development of such a framework can be an important step to identify the core elements of autonomous negotiating agents, to provide a coherent set of concepts related to automated negotiation, to assess progress in the field, and to highlight new research directions. Accordingly, this paper introduces a generic framework for automated negotiation. It describes, in detail, the components of the framework, assesses the sophistication of the majority of work in the AI literature on these components, and discusses a number of prominent models of negotiation. This paper also highlights some of the major challenges for future automated negotiation research.

View PDFchevron_right
A conceptual framework for modeling automated negotiations in multiagent systems
Mohammad Reza Shirazi

Negotiation …, 2008

In fully automated e-negotiation all involved parties are software agents, so negotiation takes place in a multiagent system between software agents that have been developed as a computer system for automating tasks in a specific application domain. A multiagent system is a ...

View PDFchevron_right
Negotiation and Argumentation in Multi-Agent Systems: Fundamentals, Theories, Systems and Applications
helder coelho

2018

In this chapter we elaborate on the specification and exploitation of a software agent-based Electronic Institution, embedding both a Normative Environment and a Computational Trust service which, when used in tandem, lead to an enhanced process of selection of potential business partners and enable automatic contract negotiation. We introduce a formalization of a Normative Environment, which accommodates the establishment of e-contracts and provides a contract monitoring facility. Contracts are established and enacted by contractual partners previously selected through an automatic negotiation process. While monitoring contracts at run-time, the Normative Environment makes use of a reporting mechanism to inform relevant contract enactment events to interested parties. In our work, we benefit from this reporting function by exploiting it as an input to a computational trust mechanism. We also present the built-in Computational Trust service, a key element of the Electronic Instituti...

View PDFchevron_right
Principles of Automated Negotiation
Sarit Kraus

2009

With an increasing number of applications in the context of multi-agent systems, automated negotiation is a rapidly growing area. Written by top researchers in the field, this state-of-the-art treatment of the subject explores key issues involved in the design of negotiating agents, covering strategic, heuristic, and axiomatic approaches. The authors discuss the potential benefits of automated negotiation as well as the unique challenges it poses for computer scientists and for researchers in artificial intelligence. They also consider possible applications and give readers a feel for the types of domain where automated negotiation is already being deployed. This book is ideal for graduate students and researchers in computer science who are interested in multi-agent systems. It will also appeal to negotiation researchers from disciplines such as management and business studies, psychology, and economics.

View PDFchevron_right
A Negotiation Formal Model for Cooperating Agents
Mohamed Jmaiel

Abstract The work presented in this paper constitutes a part of a global approach aiming at defining the theoretical foundations of a platform for designing and implementing Multi-Agents Systems. This paper is primarily centered around the study of the negotiation as a technique needed to support the cooperative activity within this kind of systems. Our contribution is mainly about the definition of formal models for negotiation and negotiating agent.

View PDFchevron_right
A negotiation model for autonomous computational agents: Formal description and empirical evaluation
N. Mamede

2002

Autonomous agents are being used in an increasing number of applications. The agents operate in complex environments and, over time, conflicts inevitably occur among them. Negotiation is the predominant process for resolving conflicts. This paper presents a generic negotiation model for autonomous agents that handles multi-party, multi-issue and single or repeated rounds. The model is based on computationally tractable assumptions and accounts for a tight integration of the individual capability of planning and the social capability of negotiation. This paper also describes an experiment conducted to evaluate the model in different types of situations. The experimental results confirmed a number of well-documented conclusions about human negotiation.

View PDFchevron_right
A model of automated negotiation based on agents profiles
Serban Radu

Scalable Computing: Practice and Experience, 2013

In this paper we present a model of self-interested agents acting in an open environment, which captures the most relevant elements of agents' behaviour related to negotiation with other agents. The agent behaviour is mainly motivated by the gain they may obtain while fulfilling their goals and negotiating. Sometimes, the agents are also motivated by the necessity to cooperate with other agents for achieving their goals. The key element in the agent behaviour is their capability to develop a set of negotiation profiles: the preference profile, the partner cooperation profile and the group-of-partners negotiation profile; these profiles help the agents to conduct their negotiation.

View PDFchevron_right

References (171)

  1. 3 Scientific Publications Next, all of the publications describing the results of this thesis work are listed.
  2. 3.1 Publications in SCI Journals • V. Sanchez-Anguix, V. Julian, V. Botti and A. Garcia-Fornes. Reaching Unanimous Agreements within Agent-based Negotiation Teams with Linear and Monotonic Utility Functions. IEEE Transactions on Systems, Man and Cybernetics -Part B. Volume 42(3), pages 778-792, 2012. Impact Factor 2.699 (Q1 Computer Science, Artificial Intelligence).
  3. • V. Sanchez-Anguix, S. Valero, V. Julian, V. Botti and A. Garcia-Fornes. Evolutionary-aided negotiation model for bilateral bargaining in Ambient In- telligence domains with complex utility functions. Information Sciences, In Press, 2011. Impact Factor 3.291 (Q1 Computer Science, Informa- tion Systems).
  4. • V. Sanchez-Anguix, V. Julian, V. Botti and A. Garcia-Fornes. Studying the Impact of Negotiation Environments on Negotiation Teams' Performance. Information Sciences, In Press, 2012. Impact Factor 2.833 (Q1 Com- puter Science, Information Systems)
  5. • V. Sanchez-Anguix, V. Julian, V. Botti and A. Garcia-Fornes. A Work- flow of Tasks for Agent-Based Negotiation Teams: Analysis and Challenges. 7.4 Scientific Research Stays • 01-09-2010 to 30-11-2011. Carnegie Mellon University, Pittsburgh, Penn- sylvania, USA, research stay supervised by Prof. Katia Sycara on Cultural Factors in Negotiation Teams.
  6. M. Wooldridge. Multi-agent systems : an introduction. Wiley, 2001. 1, 17
  7. R. H. Guttman, A. G. Moukas, and P. Maes. Agent- mediated electronic commerce: a survey.
  8. Knowledge Engineering Review, 13(2):147-159, 1998. 1, 16
  9. J. Ota. Multi-agent robot systems as dis- tributed autonomous systems. Advanced engi- neering informatics, 20(1):59-70, 2006. 1
  10. J. Pita, M. Jain, J. Marecki, F. Ordóñez, C. Portway, M. Tambe, C. Western, P. Paruchuri, and S. Kraus. De- ployed ARMOR protection: the application of a game theoretic model for security at the Los Angeles International Airport. In Pro- ceedings of the 7th international joint conference on Au- tonomous agents and multiagent systems: industrial track, pages 125-132. International Foundation for Au- tonomous Agents and Multiagent Systems, 2008. 1
  11. E. Argente, V. Botti, C. Carrascosa, A. Giret, V. Ju- lian, and M. Rebollo. An Abstract Architecture for Virtual Organizations: The THOMAS approach. Knowledge and Information Systems, 29(2):379-403, 2011. 1, 13
  12. E. Argente, V. Botti, and V. Julian. GORMAS: An Organizational-Oriented Methodological Guideline for Open MAS. In Agent-Oriented Soft- ware Engineering X-10th Int. Workshop AOSE 2009 -Re- vised Selected Papers, 6038, pages 32-47. Springer, 2011. 1
  13. M. Luck and P. McBurney. Computing as Interac- tion: Agent and Agreement Technologies. In Proc. IEEE Conference on Distributed Human-Machine Systems, pages 1-6, 2008. 1, 12
  14. C. Sierra, V. Botti, and S. Ossowski. Agreement Computing. KI-Kunstliche Intelligenz, pages 1-5, 2011. 1, 12
  15. D. G. Pruitt. Negotiation Behavior. Academic Press, 1981. 1, 15
  16. L. Thompson. The Mind and heart of the negotiator. Prentice Hall Press, Upper Saddle River, NJ, USA, 2005. 2, 37
  17. J.F. Nash. The bargaining problem. Econometrica, 18:155-162, 1950. 2, 15
  18. A. Rubinstein. Perfect equilibrium in a bargain- ing model. Econometrica, 50:155-162, 1982. 2, 15, 105, 106, 107, 120, 122, 185
  19. K. Binmore, A. Rubinstein, and A. Wolinsky. The Nash bargaining solution in economic modelling. Rand Journal of Economics, 17:176-188, 1986. 2, 15
  20. K.P. Sycara. Multiagent compromise via nego- tiation. In Distributed Artificial Intelligence, 2, pages 119-137. Morgan Kaufmann Publishers Inc., 1989. 2, 20, 41, 42
  21. K. Sycara. Negotiation Planning: An AI Approach, 46(1):216-234, May 1990. 2, 20, 41, 42
  22. K. Sycara. Problem restructuring in negotia- tion. Management Science, 37(10):1248-1268, 1991. 2, 20, 41, 42
  23. M. Tawfik Jelassi and A. Foroughi. Negotiation sup- port systems: An overview of design issues and existing software. Decision Support Systems, 5(2):167-181, 1989. 2
  24. P. Faratin, C. Sierra, and N. R. Jennings. Negotiation Decision Functions for Autonomous Agents. International Journal of Robotics and Autonomous Sys- tems, 24(3-4):159-182, 1998. 3, 18, 26, 41, 59, 121, 130, 162, 209, 219
  25. H. H. Bui, S. Venkatesh, and D. Kieronska. Learning Other Agents' Preferences in Multi-Agent Negotiation Using the Bayesian Classifier. In- ternational Journal of Cooperative Information Systems, 8(4):275-293, 1999. 3, 22, 41, 42
  26. P. Faratin. Automated service negotiation between au- tonomous computational agents. PhD thesis, Univer- sity of London, 2000. 3, 104
  27. C.M. Jonker, V. Robu, and J. Treur. An agent archi- tecture for multi-attribute negotiation using incomplete preference information. Autonomous Agents and Multi-Agent Systems, 15(2):221-252, 2007. 3, 20, 41
  28. J. M. Corchado, J. Bajo, Y. de Paz, and D. I. Tapia. Intel- ligent environment for monitoring Alzheimer patients, agent technology for health care. De- cision Support Systems, 44(2):382-396, 2008. 3, 17
  29. W. Weber, J. M. Rabaey, and E. Aarts. Ambient Intelli- gence. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2005. 3, 17
  30. L. Thompson, E. Peterson, and S.E. Brodt. Team ne- gotiation: An examination of integrative and distributive bargaining. Journal of Personality and Social Psychology, 70(1):66-78, 1996. 4, 35, 37
  31. E. Peterson and L. Thompson. Negotiation Team- work: The Impact of Information Distri- bution and Accountability on Performance Depends on the Relationship among Team Members. Organizational Behavior and Human De- cision Processes, 72(3):364-383, 1997. 4, 35, 36, 37
  32. Susan Brodt and Leigh Thompson. Negotiating Teams: A levels of analysis. Group Dynamics, 5(3):208-219, 2001. 4, 35
  33. C. Hewitt. Open information systems seman- tics for distributed artificial intelligence. Ar- tificial intelligence, 47(1-3):79-106, 1991. 11
  34. I. Foster, C. Kesselman, and S. Tuecke. The anatomy of the grid: Enabling scalable virtual orga- nizations. International Journal of High Performance Computing Applications, 15(3):200-222, 2001. 12
  35. H. Kitano and S. Tadokoro. RoboCup Rescue: A Grand Challenge for Multiagent and Intelli- gent Systems. AI Magazine, 22(1):39-52, 2001. 12
  36. M.P. Papazoglou and D. Georgakopoulos. Service- oriented computing. Communications of the ACM, 46(10):25-28, 2003. 13
  37. Y. Kalfoglou and M. Schorlemmer. IF-Map: An ontology-mapping method based on information-flow theory. Journal on data seman- tics I, pages 98-127, 2003. 13
  38. N.F. Noy. Semantic integration: a survey of ontology-based approaches. ACM Sigmod Record, 33(4):65-70, 2004. 13
  39. N. Choi, I.Y. Song, and H. Han. A survey on ontol- ogy mapping. ACM Sigmod Record, 35(3):34-41, 2006. 13
  40. G. Boella, L. Van Der Torre, and H. Verhagen. In- troduction to normative multiagent systems. Computational & Mathematical Organization Theory, 12(2):71-79, 2006. 13
  41. N. Criado, E. Argente, and V. Botti. THOMAS: An Agent Platform For Supporting Normative Multi-Agent Systems. 13
  42. B. Horling and V. Lesser. Using quantitative mod- els to search for appropriate organizational designs. Autonomous Agents and Multi-Agent Systems, 16:95-149, 2008. 13, 108
  43. S. Esparcia and E. Argente. Defining Virtual Or- ganizations Following a Formal Approach. 13
  44. J. Sabater and C. Sierra. Review on computational trust and reputation models. Artificial Intelli- gence Review, 24(1):33-60, 2005. 14, 98, 132
  45. J. M. Such, A. Espinosa, A. Garcia-Fornes, and V. Botti. Partial identities as a foundation for Trust and Reputation. Engineering Applications of Artifi- cial Intelligence, 24(7):1128-1136, 2011. 14, 98, 99, 132
  46. S. S. Fatima, M. J. Wooldridge, and N. R. Jennings. An agenda-based framework for multi-issue ne- gotiation. Artificial Intelligence, 152(1):1-45, 2004. 15
  47. Y. In and R. Serrano. Agenda restrictions in multi-issue bargaining (II): unrestricted agendas. Economics Letters, 79(3):325-331, 2003. 15
  48. C. Beam and A. Segev. Automated negotiations: A survey of the state of the art. Wirtschaftsin- formatik, 39(3):263-268, 1997. 16
  49. S. Kraus. Negotiation and cooperation in multi-agent environments. Artificial Intelligence, 94(1-2):79-97, 1997. 16
  50. N. R. Jennings, P. Faratin, A. R. Lomuscio, S. Parsons, M. J. Wooldridge, and C. Sierra. Automated Ne- gotiation: Prospects, Methods and Chal- lenges. Group Decision and Negotiation, 10:199-215, Mar 2001. 16
  51. Alessio R. Lomuscio, Michael Wooldridge, and Nicholas R. Jennings. A Classification Scheme for Negotiation in Electronic Commerce. Group Decision and Negotiation, 12:31-56, 2003. 16
  52. F. Lopes, M. Wooldridge, and AQ Novais. Negotiation among autonomous computational agents: principles, analysis and challenges. Artificial Intelligence Review, 29(1):1-44, 2008. 16, 91, 92, 93
  53. J. A. Fraile, J. Bajo, A. Abraham, and J. M. Corchado. HoCaMA: Home Care Hybrid Multiagent Ar- chitecture. In Pervasive Computing, Computer Communications and Networks, pages 259-285.
  54. Springer London, 2009. 17
  55. P. Remagnino and G.L. Foresti. Ambient Intelli- gence: A New Multidisciplinary Paradigm. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 35(1):1-6, Jan. 2005. 17
  56. S. Keegan, G.M.P O'Hare, and M.J. O'Grady. Easishop: Ambient intelligence assists everyday shop- ping. Information Sciences, 178(3):588-611, 2008. 17
  57. J. Bajo, J. M. Corchado, Y. de Paz, J. Francisco de Paz, S. Rodríguez, Q. Martin, and A. Abraham. SHOMAS: Intelligent guidance and suggestions in shop- ping centres. Applied Soft Computing, 9(2):851-862, 2009. 17
  58. M. J. Osborne and A. Rubinstein. A Course in Game The- ory. MIT Press, 1999. 18, 75 REFERENCES
  59. K. V. Hindriks and D. Tykhonov. Opponent mod- elling in automated multi-issue negotiation using Bayesian learning. In The 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS'08), pages 331-338, Richland, SC, 2008. International Foundation for Autonomous Agents and Multiagent Systems. 18, 24, 41, 42
  60. C. M. Jonker and J. Treur. An Agent Architecture for Multi-Attribute Negotiation. In The 17th In- ternational Joint Conference on Artificial Intelligence (IJ- CAI'01), pages 1195-1201. Morgan Kaufman, 2001. 20, 41
  61. D.B. Leake. Case-based reasoning. The Knowledge Engineering Review, 9(01):61-64, 1994. 20, 94
  62. R. Lopez de Mantaras. Case-Based Reasoning. In Machine Learning and Its Applications, 2049 of Lecture Notes in Computer Science, pages 127-145. Springer Berlin / Heidelberg, 2001. 20, 94
  63. P. Faratin, C. Sierra, and N. R. Jennings. Using Sim- ilarity Criteria to Make Issue Trade-Offs in Automated Negotiations. Artificial Intelligence, 142:205-237, 2002. 20, 21, 41, 42, 52, 55, 130
  64. G. Lai, K. Sycara, and C. Li. A decentralized model for automated multi-attribute negotiations with incomplete information and general util- ity functions. Multiagent and Grid Systems, 4(1):45- 65, 2008. 20, 21, 41, 42, 47, 52, 53, 55, 61, 73, 76, 80, 84, 85, 105, 106, 130, 209, 219
  65. S.J. Russell and P. Norvig. Artificial Intelligence: A Mod- ern Approach. Pearson Education, 2003. 22, 211
  66. D. Zeng and K. Sycara. Bayesian learning in ne- gotiation. International Journal of Human-Computer Studies, 48(1):125-141, 1998. 22, 40, 41
  67. V. Narayanan and N. R. Jennings. Learning to Ne- gotiate Optimally in Non-stationary Envi- ronments. In Cooperative Information Agents X, 10th International Workshop, CIA 2006, Edinburgh, UK, September 11-13, 2006, Proceedings, 4149 of Lecture Notes in Computer Science, pages 288-300. Springer, 2006. 23, 40, 41, 112
  68. S. Buffett and B. Spencer. A bayesian classifier for learning opponents' preferences in multi- object automated negotiation. Electronic Com- merce Research and Applications, 6(3):274-284, 2007. 23, 29, 41, 42
  69. D. E. Goldberg. Genetic Algorithms in Search, Op- timization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1989. 25, 57
  70. J. R. Oliver. On Artificial Agents for Negotia- tion in Electronic Commerce. In The 29 Hawaii International Conference on System Sciences (HICSS'96), 4, pages 337-346, Washington, DC, USA, 1996. IEEE Computer Society. 25, 27, 41, 42, 109
  71. N. Matos, C. Sierra, and N. R. Jennings. Determining Successful Negotiation Strategies: An Evolu- tionary Approach. In The 3rd International Confer- ence on Multi Agent Systems (ICMAS '98), pages 182- 189, Washington, DC, USA, 1998. IEEE Computer Society. 26, 27, 41, 42, 109
  72. T. Tu, E. Wolff, and W. Lamersdorf. Genetic Algo- rithms for Automated Negotiations: A FSM- based Application Approach. In Proceedings of the 11th International Conference on Database and Ex- pert Systems (DEXA 2000), pages 1029-1033. IEEE, 9 2000. 27, 41, 42, 109
  73. E. Gerding, D. Van Bragt, and H. La Poutré. Multi- Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions. Computational Economics, 22(1):39-63, 2003. 27, 41, 42, 109
  74. R. Krovi, A. C. Graesser, and W. E. Pracht. Agent be- haviors in virtual negotiation environments. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 29(1):15-25, 1999. 28, 41, 42
  75. S. P. M. Choi, J. Liu, and S. Chan. A genetic agent- based negotiation system. Computer Networks, 37(2):195-204, 2001. 29, 41, 42
  76. H.J. Kushner and G. Yin. Stochastic approximation and recursive algorithms and applications, 35. Springer Verlag, 2003. 29
  77. R.Y.K. Lau. Towards Genetically Optimised Multi-Agent Multi-Issue Negotiations. In The 38 Hawaii International Conference on System Sciences (HICSS'05), pages 35-45, 2005. 29, 41, 42
  78. Robert M. Coehoorn and Nicholas R. Jennings. Learn- ing on opponent's preferences to make ef- fective multi-issue negotiation trade-offs. In The 6th International Conference on Electronic Commerce (ICEC'04), pages 59-68, 2004. 29, 40, 41
  79. S.J. Sheather and M.C. Jones. A reliable data-based bandwidth selection method for kernel den- sity estimation. Journal of the Royal Statistical So- ciety. Series B (Methodological), pages 683-690, 1991. 29
  80. R. Carbonneau, G.E. Kersten, and R. Vahidov. Predict- ing opponent's moves in electronic negotia- tions using neural networks. Expert Systems with Applications, 34(2):1266-1273, 2008. 30, 40, 41
  81. Réal A. Carbonneau, Gregory E. Kersten, and Rustam M. Vahidov. Pairwise issue modeling for negotia- tion counteroffer prediction using neural net- works. Decision Support Systems, 50(2):449 -459, 2011. 30, 40, 41
  82. J. Wallenius, J. S. Dyer, P. C. Fishburn, R. E. Steuer, S. Zionts, and K. Deb. Multiple Criteria Decision Making, Multiattribute Utility Theory: Re- cent Accomplishments and What Lies Ahead. Management Science, 54(7):1336-1349, 2008. 31
  83. R. L. Keeney and H. Raiffa. Decisions with Multiple Ob- jectives: Preferences and Value Tradeoffs. Cambridge University Press, 1993. 31
  84. M. Klein, P. Faratin, H. Sayama, and Y. Bar-Yam. Nego- tiating Complex Contracts. Group Decision and Negotiation, 12:111-125, March 2003. 31, 32, 40, 41
  85. T. Ito, M. Klein, and H. Hattori. A multi-issue ne- gotiation protocol among agents with nonlin- ear utility functions. Multiagent and Grid Systems, 4(1):67-83, 2008. 32, 34, 35, 40, 41, 66, 68, 73
  86. V. Robu, D. J. A. Somefun, and J. A. La Poutré. Mod- eling complex multi-issue negotiations using utility graphs. In Proceedings of the fourth interna- tional joint conference on Autonomous agents and multia- gent systems (AAMAS'05), pages 280-287, New York, NY, USA, 2005. ACM. 33, 40, 41
  87. V. Robu and J. A. La Poutré. Retrieving the Struc- ture of Utility Graphs Used in Multi-Item Negotiation through Collaborative Filtering of Aggregate Buyer Preferences. In Rational, Robust and Secure Negotiations, 89 of Computational Intelligence. Springer, 2008. 33, 40, 41
  88. H. Hattori, M. Klein, and T. Ito. Using iterative narrowing to enable multi-party negotiations with multiple interdependent issues. In Pro- ceedings of the 6th international joint conference on Au- tonomous agents and multiagent systems (AAMAS'07), pages 1-3, New York, NY, USA, 2007. ACM. 34, 35, 40, 41
  89. I. Marsa-Maestre, M. A. Lopez-Carmona, J. R. Velasco, and E. de la Hoz. Effective bidding and deal identification for negotiations in highly non- linear scenarios. In Proceedings of The 8th Inter- national Conference on Autonomous Agents and Multia- gent Systems (AAMAS'09), pages 1057-1064, Rich- land, SC, 2009. International Foundation for Au- tonomous Agents and Multiagent Systems. 35, 40, 41, 73
  90. I. Marsa-Maestre, M. A. López-Carmona, J. R. Velasco, T. Ito, M. Klein, and K. Fujita. Balancing Utility and Deal Probability for Auction-Based Ne- gotiations in Highly Nonlinear Utility Spaces. In International Joint Conference on Artificial Intelli- gence, pages 214-219, 2009. 35, 40, 41, 73
  91. G. Browder. An analysis of the negotiations for the 1995 Mekong Agreement. International Ne- gotiation, 5(2):237-261, 2000. 36
  92. K.M. O'Connor. Groups and solos in context: The effects of accountability on team nego- tiation. Organizational Behavior and Human Decision Processes, 72(3):384-407, 1997. 37
  93. M. J. Gelfand, J. M. Brett, L. Imai, H. Tsai, and D. Huang. Team Negotiation across Cultures: When and Where Are Two Heads Better Than One? Proceedings of 18th Annual Conference for the Inter- national Association for Conflict Management (IACM- 2005), 2005. 37
  94. K. Behfar, R. A. Friedman, and J. M. Brett. The Team Negotiation Challenge: Defining and Man- aging the Internal Challenges of Negotiating Teams. In Proceedings of the 21st Annual Conference for the International Association for Conflict Manage- ment (IACM-2008), 2008. 37, 45
  95. E. Mannix. Strength in Numbers: Negotiating as a Team. Negotiation, 8(5), 2005. 37, 38
  96. S. E. Brodt and M. Tuchinsky. Working Together but in Opposition: An Examination of the Good-Cop/Bad-Cop Negotiating Team Tac- tic. Organizational Behavior and Human Decision Pro- cesses, 81(2):155 -177, 2000. 37
  97. Nir Halevy. Team Negotiation: Social, Epis- temic, Economic, and Psychological Conse- quences of Subgroup Conflict. Personality and Social Psychology Bulletin, 34:1687-1702, 2008. 37, 38
  98. H. Taylor. Most people are privacy pragma- tists who, while concerned about privacy, will sometimes trade it off for other benefits. The Harris Poll, 17:19, 2003. 44
  99. J. Holland. Adaptation in natural and artificial systems. University of Michigan Press, 1975. 48, 57
  100. S. W. Mahfoud. Niching methods for genetic algorithms. PhD thesis, University of Illinois at Urbana- Champaign, Champaign, IL, USA, 1995. 48, 57
  101. M. Osborne and A. Rubinstein. Bargaining and Markets. The Academic Press, 1990. 51, 52
  102. O. J. Mengshoel and D. E. Goldberg. The crowding approach to niching in genetic algorithms. Evolutionary Computation, 16(3):315-354, 2008. 57
  103. C. Tomlin, G.J. Pappas, and S. Sastry. Conflict reso- lution for air traffic management: A study in multiagent hybrid systems. IEEE Transactions on Automatic Control, 43(4):509-521, 1998. 93
  104. J. Chu-Carroll and S. Carberry. Conflict resolution in collaborative planning dialogs. International Journal of Human-Computer Studies, 53(6):969 -1015, 2000. 93
  105. K.S. Barber, T.H. Liu, and S. Ramaswamy. Conflict detection during plan integration for multi- agent systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 31(4):616-628, 2001. 93
  106. S. Wollkind, J. Valasek, and T.R. Ioerger. Auto- mated conflict resolution for air traffic man- agement using cooperative multiagent nego- tiation. In AIAA guidance, navigation, and control con- ference, pages 1-11, 2004. 93 REFERENCES
  107. F. Lopes, N. Mamede, A.Q. Novais, and H. Coelho. A negotiation model for autonomous computa- tional agents: Formal description and empir- ical evaluation. Journal of Intelligent & Fuzzy Sys- tems, 12(3):195-212, 2002. 93
  108. Kwang Mong Sim and Chung Yu Choi. Agents that re- act to changing market situations. IEEE Trans- actions on on Systems, Man, and Cybernetics, Part B: Cybernetics, 33(2):188 -201, 2003. 95, 112
  109. C. Li, J. Giampapa, and K. Sycara. Bilateral ne- gotiation decisions with uncertain dynamic outside options. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 36(1):31-44, 2006. 95, 112
  110. B. An, K. M. Sim, C. Y. Miao, and Z. Q. Shen. Decision making of negotiation agents using markov chains. Multiagent and Grid Systems, 4(1):5-23, 2008. 95, 112
  111. K. Mansour and R. Kowalczyk. A Meta-Strategy for Coordinating of One-to-Many Negotia- tion over Multiple Issues. In Foundations of Intel- ligent Systems, 122, pages 343-353. Springer Berlin / Heidelberg, 2012. 95
  112. D. Sarne, E. Manisterski, and S. Kraus. Multi-goal economic search using dynamic search struc- tures. Autonomous Agents and Multi-Agent Systems, 21:204-236, 2010. 95
  113. E. Del Val, M. Rebollo, and V. Botti. Self-Organized Service Management in Social Systems. In Proceedings of the 45 Hawaii International Conference on System Sciences (HICSS 2012), pages 810-817, 2012. 95
  114. H. Raiffa. The Art and Science of Negotiation. Cam- bridge, USA, 1982. 96
  115. M. Tsvetovat, K. Sycara, Y. Chen, and J. Ying. Cus- tomer Coalitions in Electronic Markets. In Agent-Mediated Electronic Commerce III, 2003 of Lecture Notes in Computer Science, pages 121-138.
  116. J. Yamamoto and K. Sycara. A stable and effi- cient buyer coalition formation scheme for e- marketplaces. In Proceedings of the fifth international conference on Autonomous agents (AGENTS'01), pages 576-583, New York, NY, USA, 2001. ACM. 96, 97
  117. T. Ito, H. Ochi, and T. Shintani. A group-buy pro- tocol based on coalition formation for agent- mediated e-commerce. International Journal of Co- operative Information Systems, 3(1):11-20, 2002. 96, 97
  118. C. Li, U. Rajan, S. Chawla, and K. Sycara. Mecha- nisms for coalition formation and cost shar- ing in an electronic marketplace. In Proceed- ings of the 5th international conference on Electronic com- merce (ICEC'03), pages 68-77, New York, NY, USA, 2003. ACM. 96, 97
  119. M. Belmonte, R. Conejo, J. Pérez-de-la Cruz, and F. Triguero. A Stable and Feasible Payoff Di- vision for Coalition Formation in a Class of Task Oriented Domains. In Intelligent Agents VIII, 2333 of Lecture Notes in Computer Science, pages 324- 334. Springer Berlin / Heidelberg, 2002. 96
  120. V. Conitzer and T. Sandholm. Computing shapley values, manipulating value division schemes, and checking core membership in multi-issue domains. In Proceedings of the 19th national conference on Artifical intelligence (AAAI 2004), pages 219-225. AAAI Press, 2004. 96
  121. B. Blankenburg and M. Klusch. On Safe Kernel Stable Coalition Forming among Agents. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems (AA- MAS'04), pages 580-587. IEEE Computer Society, 2004. 96
  122. N. Ohta, V. Conitzer, Y. Satoh, A. Iwasaki, and M. Yokoo. Anonymity-proof shapley value: extending shapley value for coalitional games in open environments. In Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, pages 927-934. International Foundation for Autonomous Agents and Multiagent Systems, 2008. 96
  123. Y. Zick and E. Elkind. Arbitrators in overlapping coalition formation games. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'11), pages 55-62. Inter- national Foundation for Autonomous Agents and Multiagent Systems, 2011. 96
  124. F. Dignum, B. Dunin-Keplicz, and R. Verbrugge. Agent Theory for Team Formation by Dialogue. In Proceedings of the 7th International Workshop on In- telligent Agents VII. Agent Theories Architectures and Languages (ATAL'00), pages 150-166, London, UK, 2001. Springer-Verlag. 97
  125. P. Cohen, H. Levesque, and I. Smith. On Team Forma- tion. In Contemporary Action Theory. Synthese, pages 87-114. Kluwer Academic Publishers, 1999. 97
  126. M. E. Gaston and M. desJardins. Agent-organized networks for dynamic team formation. In Pro- ceedings of the fourth international joint conference on Autonomous agents and multiagent systems (AAMAS'05), pages 230-237, New York, NY, USA, 2005. ACM. 97
  127. J. French and B. Raven. The bases of social power. In Studies in social power, pages 150-167. Ann Arbor, MI: Institute for Social Research, 1959. 98
  128. B. Rezabakhsh, D. Bornemann, U. Hansen, and U. Schrader. Consumer Power: A Com- parison of the Old Economy and the Internet Economy. Journal of Consumer Policy, 29:3-36, 2006. 98 REFERENCES
  129. A. Perreau de Pinninck, C. Sierra, and M. Schorlemmer. Distributed Norm Enforcement: Ostracism in Open Multi-Agent Systems. In Computable Models of the Law, 4884 of Lecture Notes in Computer Science, pages 275-290. Springer Berlin / Heidel- berg, 2008. 98
  130. J. Brzostowski and R. Kowalczyk. Experimental Evaluation of Possibilistic Mechanism for Ne- gotiation Partners Selection. In Rational, Ro- bust, and Secure Negotiations in Multi-Agent Systems, 89, pages 127-145. Springer Berlin / Heidelberg, 2008. 99, 100
  131. J. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative Filtering Recommender Sys- tems. In The Adaptive Web, 4321 of Lecture Notes in Computer Science, pages 291-324. Springer Berlin / Heidelberg, 2007. 99
  132. S. Koc-Menard. Team performance in negotia- tion: a relational approach. Team Performance Management, 15(7-8):357-365, 2009. 100
  133. I. Rahwan, S.D. Ramchurn, N.R. Jennings, P. Mcbur- ney, S. Parsons, and L. Sonenberg. Argumentation- based negotiation. The Knowledge Engineering Re- view, 18(4):343-375, 2003. 100, 102, 105
  134. P. McBurney, D. Hitchcock, and S. Parsons. The eight- fold way of deliberation dialogue. International Journal of Intelligent Systems, 22(1):95-132, 2007. 100, 102, 105
  135. G. Brewka and T. Eiter. Argumentation Context Systems: A Framework for Abstract Group Argumentation. In Logic Programming and Non- monotonic Reasoning, 5753 of Lecture Notes in Com- puter Science, pages 44-57. Springer Berlin / Hei- delberg, 2009. 100, 102, 105
  136. S. Heras, V. Botti, and V. Julian. Argument-based Agreements in Agent Societies. Neurocomputing, 75(1):156-162, 2012. 100, 102, 105
  137. S. Sa and J. Alcantara. Cooperative Dialogues with Conditional Arguments. In Proceedings of the 11th International Joint Conference on Autonomous Agents and Multiagent Systems. ACM, 2012. 100, 102, 105
  138. H. Nurmi. Voting systems for social choice. Handbook of Group Decision and Negotiation, pages 167-182, 2010. 100, 105, 132, 140, 207
  139. K.P. Sycara and M. Lewis. Modeling teams of spe- cialists. In Proceedings of the 27 Hawaii International Conference on System Sciences (HICSS 1994), 4, pages 271 -280, 1994. 102
  140. R. Booth. Social contraction and belief negoti- ation. Information Fusion, 7(1):19-34, 2006. 102
  141. G. Qi, W. Liu, and D. Bell. Combining multiple pri- oritized knowledge bases by negotiation. Fuzzy Sets and Systems, 158(23):2535-2551, 2007. 102
  142. S. Konieczny and R. Pino Pérez. Logic based merg- ing. Journal of Philosophical Logic, 40(2):239-270, 2011. 102, 103
  143. P. Everaere, S. Konieczny, P. Marquis, et al. The strategy-proofness landscape of merging. Journal of Artificial Intelligence Research, 28(1):49- 105, 2007. 103
  144. J. M. Such, A. García-Fornes, and A. Espinosa. Enhancing Privacy in Multi-agent Systems: Avoiding undesired In- formation Collection and Information Processing. LAP LAMBERT Academic Publishing, 2012. 103
  145. M. Klein, P. Faratin, H. Sayama, and Y. Bar-Yam. Proto- cols for Negotiating Complex Contracts. IEEE Intelligent Systems, 18(6):32-38, 2003. 105, 106
  146. G. Lai, C. Li, and K. Sycara. Efficient Multi- Attribute Negotiation with Incomplete Infor- mation. Group Decision and Negotiation, 15:511-528, 2006. 105, 106
  147. T. Miller and P. McBurney. Annotation and matching of first-class agent interaction pro- tocols. Argumentation in Multi-Agent Systems, pages 141-158, 2009. 105
  148. J. Hulstijn, M. Dastani, and L. van der Torre. Negotia- tion protocols and dialogue games. In Proceed- ings of the Belgian-Dutch AI Conference (BNAIC-2000), PUBLISHER =, 2000. 106
  149. Chris Reed, Timothy J. Norman, and Nicholas R. Jen- nings. Negotiating the semantics of agent com- munications languages. Computational Intelligence, 18(2):229-252, 2002. 106
  150. R. Nair, T. Ito, M. Tambe, and S. Marsella. Task Al- location in the RoboCup Rescue Simulation Domain: A Short Note. In RoboCup 2001: Robot Soccer World Cup V, pages 751-754, London, UK, 2002. Springer-Verlag. 108
  151. R. Nair, M. Tambe, and S. Marsella. Role alloca- tion and reallocation in multiagent teams: Towards a practical analysis. In Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pages 552-559. ACM, 2003. 108
  152. M. Hoogendoorn and C. Jonker. Formation of Vir- tual Organizations Through Negotiation. In Multiagent System Technologies, 4196 of Lecture Notes in Computer Science, pages 135-146. Springer Berlin / Heidelberg, 2006. 108
  153. A. Campbell and A. Wu. Multi-agent role alloca- tion: issues, approaches, and multiple per- spectives. Autonomous Agents and Multi-Agent Sys- tems, 22:317-355, 2011. 108
  154. B. J. Grosz, S. Kraus, D. G. Sullivan, and S. Das. The in- fluence of social norms and social conscious- ness on intention reconciliation. Artificial Intel- ligence, 142(2):147 -177, 2002. 108 REFERENCES
  155. P. Paruchuri, M. Tambe, S. Kapetanakis, and S. Kraus. Between collaboration and competition: An initial Formalization using Distributed POMDPs. In Proceedings of the GTDT workshop at AAMAS-2003, 2007. 108
  156. N. Matos and C. Sierra. Evolutionary computing and negotiating agents. In Workshop on Agent Mediated Electronic Trading (AMET'98), pages 91-111, 1998. 109, 112
  157. Raz Lin, Sarit Kraus, Tim Baarslag, Dmytro Tykhonov, Koen Hindriks, and Catholijn M. Jonker. Genius: An Integrated Environment for Supporting the Design of Generic Automated Negotiators. Computational Intelligence, 2012. 109, 216
  158. T. Baarslag, K. V. Hindriks, and C. M. Jonker. To- wards a Quantitative Concession-Based Clas- sification Method of Negotiation Strategies. In Agents in Principle, Agents in Practice. Lecture Notes of The 14th International Conference on Principles and Practice of Multi-Agent Systems, pages 143-158, 2011. 109, 218
  159. B.I. Spector. Creativity heuristics for impasse resolution: Reframing intractable negotia- tions. The Annals of the American Academy of Political and Social Science, 542(1):81-99, 1995. 111
  160. L. Babcock and G. Loewenstein. Explaining bargain- ing impasse: The role of self-serving biases. The Journal of Economic Perspectives, 11(1):109-126, 1997. 111
  161. R.D. Tollison and T.D. Willett. An economic the- ory of mutually advantageous issue linkages in international negotiations. International Orga- nization, 33(4):425-449, 1979. 111
  162. J.K. Sebenius. Negotiation arithmetic: adding and subtracting issues and parties. International Organization, 37(2):281-316, 1983. 111
  163. T.C. Morgan. Issue linkages in international cri- sis bargaining. American Journal of Political Science, 34(2):311-333, 1990. 111
  164. C. Hou. Predicting agents tactics in automated negotiation. In Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2004), pages 127-133. IEEE, 2004. 112
  165. C.R. Williams, V. Robu, E.H. Gerding, and N.R. Jennings. Using gaussian processes to optimise con- cession in complex negotiations against un- known opponents. In Proceedings of the 22nd Inter- national Joint Conference on Artificial Intelligence (IJ- CAI'11), pages 432-438, 2011. 112
  166. C. Yu, F. Ren, and M. Zhang. An Adaptive Bilateral Negotiation Model Based on Bayesian Learn- ing. In The 4th International Workshop on Agent-based Complex Automated Negotiations (ACAN 2011), pages 40-47, 2011. 112
  167. Yann Chevaleyre, Paul E. Dunne, Ulle Endriss, Jérôme Lang, Michel Lemaître, Nicolas Maudet, Julian Pad- get, Steve Phelps, Juan A. Rodríguez-aguilar, and Paulo Sousa. Issues in Multiagent Resource Alloca- tion. Informatica, 30:2006, 2006. 167
  168. T. Baarslag, K. Hindriks, C. Jonker, S. Kraus, and R. Lin. The first automated negotiating agents com- petition (ANAC 2010). New Trends in Agent- Based Complex Automated Negotiations, pages 113- 135, 2010. 216
  169. S. Kawaguchi, K. Fujita, and T. Ito. Compromising Strategy Based on Estimated Maximum Util- ity for Automated Negotiation Agents Com- petition (ANAC-10). In Modern Approaches in Ap- plied Intelligence, 6704, pages 501-510. Springer Berlin / Heidelberg, 2011. 218
  170. K.V. Hindriks, C.M. Jonker, and D. Tykhonov. The Benefits of Opponent Models in Negotiation. In Proceedings of the 2009 IEEE/WIC/ACM interna- tional joint conference on Web Intelligence and Intelligent Agent Technology, pages 439-444, 2009. 219
  171. V. Sanchez-Anguix, T. Dai, Z. Semnani-Azad, K. Sycara, and V. Botti. Modeling power distance and in- dividualism/collectivism in negotiation team dynamics. In 45 Hawaii International Conference on System Sciences (HICSS-45), pages 628-637, 2012.

Related papers

Negotiation In Multi-Agent Systems
Martin Beer, Michael Luck

Knowledge …, 1999

View PDFchevron_right
Automated negotiation and decision making in multiagent environments
Naynayminmyint Myint

Multi-agent systems and applications, 2006

This paper presents some of the key techniques for reaching agreements in multi-agent environments. It discusses game-theory and economics based techniques: strategic negotiation, auctions, coalition formation, market-oriented programming and contracting. It also presents logical based mechanisms for argumentations. The focus of the survey is on negotiation of self-interested agents, but several mechanisms for cooperative agents who need to resolve conflicts that arise from conflicting beliefs about different aspects of their environment are also mentioned. For space reasons, we couldn't cover all the relevant works, and the papers that are mentioned only demonstrate the possible approaches. We present some of the properties of the approaches using our own previous work.

View PDFchevron_right
Architecture of an agent-based negotiation mechanism
Rachid Anane

Distributed Computing …, 2002

One of the central issues in facilitating mobile teamwork is the creation and establishment of teams from autonomous agents. It is widely accepted that team building assumes an expression of and agreement on, common interests. The paper describes an ...

View PDFchevron_right
A Generic Model for Negotiation Behaviors in Multi-Agent Engineering Applications
Moath Jarrah

In this paper, we will present a generic Domain-Independent Marketplace architecture that allows user agents to interact with service providers using two simple and yet powerful negotiation protocols. Service providers have different capabilities depending on the domain of interest. Hence, a dynamic message structuring capability is needed. A key role to support such an expressive power is to design an ontology that contains specializations between different domains. Integrating of the Domain-Dependent Ontology with the Domain-Independent marketplace gives the designer a powerful tool in which systems can be tailored based on the operational purposes. Encounter, Oceanography designing a Domain-Independent Marketplace architecture that allows user agents to interact with service providers using two simple and yet powerful negotiation protocols which define the rules of interactions in multi-agent environments. Having a trusted third party marketplace supports privacy and transparency among collaborative agents and service providers. Service providers have different capabilities depending on the domain of interest. Such providers can be Radar sensors as in oceanography surveillance systems, print servers in distributed printing jobs community, or they can be online stores providing products on the Web in the e-commerce domain. In order to provide negotiation in different domains, a dynamic message structuring capability is needed. A key role to support such an expressive power is to design an Ontology that contains specialization relations between the different domains of interest.

View PDFchevron_right
Towards agent-based negotiation teams
Vicent Botti, Victor Sanchez-Anguix

2010

Even though teamwork has been actively studied in multi-agent research, not much attention has been paid to negotiation teams from the point of view of agent research. Nevertheless, social scientists have studied the topic of negotiation teams to some extent. Their studies may provide a very useful insight for agent-based negotiation teams. Firstly, they may give some guidelines for how people confront these kinds of scenarios. Such strategies may be adapted and enhanced for agents working in very complex scenarios, which is quite adequate since their calculation capabilities are far bigger than those of people. Secondly, human behaviour can be mimicked by agents in order to design more realistic systems, give support to human decisions, simulate human societies, or train real negotiators. Our long term research goal is to provide an agent-based model for negotiation teams inspired by social sciences. In this paper we try to provide a general workflow for an agent-based negotiation team inspired by social sciences. The proposed schema aims to be general in order to be potentially adaptable to a wide range of domains.

View PDFchevron_right

Related topics

  • Artificial Intelligence
  • Multiagent Systems
  • Ambient Intelligence
  • Automated Negotiations
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved