{"type":"ScholarlyArticle","title":"Automated Negotiation with Gaussian Process-based Utility Models","authors":[{"name":"Haralambie Leahu","slug":"haralambie-leahu","given":"Haralambie","family":"Leahu","affiliation":"Centrum Wiskunde & Informatica , Amsterdam , The Netherlands"},{"name":"Michael Kaisers","slug":"michael-kaisers","given":"Michael","family":"Kaisers","affiliation":"Centrum Wiskunde & Informatica , Amsterdam , The Netherlands"},{"name":"Tim Baarslag","slug":"tim-baarslag","given":"Tim","family":"Baarslag","affiliation":"Centrum Wiskunde & Informatica , Amsterdam , The Netherlands"}],"abstract":"Designing agents that can efficiently learn and integrate user's preferences into decision making processes is a key challenge in automated negotiation. While accurate knowledge of user preferences is highly desirable, eliciting the necessary information might be rather costly, since frequent user interactions may cause inconvenience. Therefore, efficient elicitation strategies (minimizing elicitation costs) for inferring relevant information are critical. We introduce a stochastic, inverse-ranking utility model compatible with the Gaussian Process preference learning framework and integrate it into a (belief) Markov Decision Process paradigm which formalizes automated negotiation processes with incomplete information. Our utility model, which naturally maps ordinal preferences (inferred from the user) into (random) utility values (with the randomness reflecting the underlying uncertainty), provides the basic quantitative modeling ingredient for automated (agent-based) negotiation.","doi":"10.24963/ijcai.2019/60","url":"https://ijcai.org/proceedings/2019/60","pdf":"https://ijcai.org/proceedings/2019/0060.pdf","markdown":"https://ijcai.org/proceedings/2019/60.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/60","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":60,"pages":{"first":421,"last":427},"keywords":["Uncertainty in AI: Uncertainty in AI","Uncertainty in AI: Sequential Decision Making","Agent-based and Multi-agent Systems: Agreement Technologies: Negotiation and Contract-Based Systems","Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning"],"videos":[],"references":[{"position":1,"raw":"[Baarslag and Gerding, 2015] Tim Baarslag and Enrico Gerding. Optimal incremental preference elicitation dur- ing negotiation. In Proceedings of the 24th International Joint Conference on Artificial Intelligence, pages 3-9, Palo Alto, CA, USA, 2015. AAAI Press.","title":"Optimal incremental preference elicitation during negotiation","authors":"Gerding Baarslag, Tim Baarslag, Enrico Gerding","year":"2015","venue":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baarslag et al., 2015] Tim Baarslag, Enrico Gerding, Rey- han Aydogan, and Monica Schraefel. Optimal negotia- tion decision functions in time-sensitive domains. In 2015 IEEE/WIC/ACM International Conference on Web Intel- ligence and Intelligent Agent Technology (WI-IAT), vol- ume 2, pages 190-197, Dec 2015.","title":"Optimal negotiation decision functions in time-sensitive domains","authors":"Tim Baarslag, Enrico Gerding, Reyhan Aydogan, Monica Schraefel","year":"2015","venue":"IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Baarslag et al., 2017] Tim Baarslag, Michael Kaisers, En- rico Gerding, Catholijn Jonker, and Jonathan Gratch. When will negotiation agents be able to represent us? the challenges and opportunities for autonomous negotiators. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 4684-4690, Freiburg, DE, 2017. IJCAI.","title":"When will negotiation agents be able to represent us? the challenges and opportunities for autonomous negotiators","authors":"Tim Baarslag, Michael Kaisers, Enrico Gerding, Catholijn Jonker, Jonathan Gratch","year":"2017","venue":"Proceedings of the 26th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/653"},{"position":4,"raw":"[Bonilla et al., 2010] Edwin Bonilla, Shengbo Guo, and Scott Sanner. Gaussian processes preference elicitation. Advances in Neural Information Processing Systems 23, pages 262-270, 2010.","title":"Gaussian processes preference elicitation","authors":"Edwin Bonilla, Shengbo Guo, Scott Sanner","year":"2010","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Boutilier, 2002] Craig Boutilier. A POMDP formulation of preference elicitation problems. In Proceedings of the 18th National Conference on Artificial Intelligence, pages 239- 246, Menlo Park, CA, USA, 2002. AAAI.","title":"A POMDP formulation of preference elicitation problems","authors":"Craig Boutilier","year":"2002","venue":"Proceedings of the 18th National Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chajewska et al., 2000] Urszula Chajewska, Daphne Koller, and Ron Parr. Making rational decisions using adaptive utility elicitation. In Proceedings of the 17th National Conference on Artificial Intelligence, pages 363-369, Palo Alto, CA, USA, 2000. AAAI Press.","title":"Making rational decisions using adaptive utility elicitation","authors":"Urszula Chajewska, Daphne Koller, Ron Parr","year":"2000","venue":"Proceedings of the 17th National Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chu and Ghahramani, 2005a] Wei Chu and Zoubin Ghahra- mani. Gaussian processes for ordinal regression. Journal of Machine Learning Research, 6:1019-1041, 2005.","title":"Gaussian processes for ordinal regression","authors":"Chu, Ghahramani, Wei Chu, Zoubin Ghahramani","year":"2005","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chu and Ghahramani, 2005b] Wei Chu and Zoubin Ghahra- mani. Preference learning with gaussian processes. In Proceedings of the 22nd Conference on Machine Learn- ing, pages 137-144, New York, NY, USA, 2005. ACM.","title":"Preference learning with gaussian processes","authors":"Chu, Ghahramani, Wei Chu, Zoubin Ghahramani","year":"2005","venue":"Proceedings of the 22nd Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ketter et al., 2018] Wolfgang Ketter, John Collins, and Prashant Reddy. Power tac: A competitive economic sim- ulation of the smart grid. Energy Economics, 39:262-270, 2018.","title":"Power tac: A competitive economic simulation of the smart grid","authors":"Ketter","year":"2018","venue":"Energy Economics","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kingsley and Brown, 2006] David Kingsley and Thomas Brown. Preference uncertainty, preference learning and paired comparison experiments. Land Economics, 84:530-544, 2006.","title":"Preference uncertainty, preference learning and paired comparison experiments","authors":"David Kingsley, Thomas Brown","year":"2006","venue":"Land Economics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kowalczyk and Bui, 2000] Ryszard Kowalczyk and Vinh Bui. On fuzzy e-negotiation agents: autonomous nego- tiation with incomplete and imprecise information. In Proceedings 11th International Workshop on Database and Expert Systems Applications, pages 1034-1038, Sept 2000. Kraus et al., 1995] Sarit Kraus, Jonathan Wilkenfeld, and Gilad Zlotkin. Multiagent negotiation under time con- straints. Artificial Intelligence, 75:297-345, 1995.","title":"On fuzzy e-negotiation agents: autonomous negotiation with incomplete and imprecise information","authors":"Bui Kowalczyk, Ryszard Kowalczyk, Vinh Bui, ; Kraus","year":"1995","venue":"Proceedings 11th International Workshop on Database and Expert Systems Applications","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Leahu et al., 2019] Haralambie Leahu, Michael Kaisers, and Tim Baarslag. Preference learning in automated ne- gotiation using gaussian uncertainty models. In Proceed- ings of the 18th International Conference on Autonomous Agents and Multiagent Systems, pages 2087-2089, Rich- land, SC, USA, 2019. IFAAMAS.","title":"Preference learning in automated negotiation using gaussian uncertainty models","authors":"Leahu","year":"2019","venue":"Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[McGroarty et al., 2018] Frank McGroarty, Ash Booth, En- rico Gerding, and Raju Chinthalapati. High-frequency trading strategies, market fragility and price spikes: An agent-based model perspective. Annals of Operations Re- search, pages 1-28, 2018.","title":"High-frequency trading strategies, market fragility and price spikes: An agent-based model perspective","authors":"Frank Mcgroarty, Ash Booth, Enrico Gerding, Raju Chinthalapati","year":"2018","venue":"Annals of Operations Research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Mohammad and Nakadai, 2018] Yasser Mohammad and Shinji Nakadai. Fastvoi: Efficient utility elicitation during negotiations. In Proceedings of the International Confer- ence on Principles and Practice of Multi-Agent Systems, pages 560-567, Zürich, Switzerland, 2018. Springer.","title":"Fastvoi: Efficient utility elicitation during negotiations","authors":"Mohammad, Nakadai ;, Yasser Mohammad, Shinji Nakadai","year":"2018","venue":"Proceedings of the International Conference on Principles and Practice of Multi-Agent Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Paruchuri et al., 2009] Praveen Paruchuri, Nilanjan Chakraborty, Roie Zivan, Katia Sykara, Miroslav Dudik, and Geoffrey Gordon. Pomdp based negotiation modeling. IJCAI MICON Workshop, 2009.","title":"Pomdp based negotiation modeling","authors":"Praveen Paruchuri, Nilanjan Chakraborty, Roie Zivan, Katia Sykara, Miroslav Dudik, Geoffrey Gordon","year":"2009","venue":"IJCAI MICON Workshop","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ramchurn et al., 2004] Sarvapali Ramchurn, Benjamin Deitch, Mark Thompson, David De Roure, Nicholas Jennings, and Michael Luck. Minimising intrusiveness in pervasive computing environments using multi-agent negotiation. In Proceedings of the First Annual In- ternational MobiQuitous Conference: Networking and Services, pages 364-371, Los Alamitos, CA, USA, 2004. IEEE Computer Society Press.","title":"Minimising intrusiveness in pervasive computing environments using multi-agent negotiation","authors":"Ramchurn","year":"2004","venue":"Proceedings of the First Annual International MobiQuitous Conference: Networking and Services","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Rasmussen and Williams, 2006] Carl Edward Rasmussen and Cristopher Williams. Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA, USA, 2006.","title":"Gaussian Processes for Machine Learning","authors":"Carl Edward, Rasmussen, Cristopher Williams","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sim, 2011] Kwang Mong Sim. Agent-based cloud comput- ing. IEEE Transactions on Services Computing, 5:564- 577, 2011.","title":"Agent-based cloud computing","authors":"Kwang Mong, Sim","year":"2011","venue":"IEEE Transactions on Services Computing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2009] Minhong Wang, Huaiqing Wang, Doug Vogel, Kuldeep Kumar, and Dickson Chiu. Agent-based negotiation and decision making for dynamic supply chain formation. Engineering Applications of Artificial Intelli- gence, 22:1046-1055, 2009.","title":"Agent-based negotiation and decision making for dynamic supply chain formation","authors":"Wang","year":"2009","venue":"Engineering Applications of Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zhong et al., 2002] Fang Zhong, Steven Kimbrough, and Dong Wu. Cooperative agent systems: Artificial agents play the ultimatum game. Group Decision and Negotia- tion, 11(6):433-447, Nov 2002.","title":"Cooperative agent systems: Artificial agents play the ultimatum game","authors":"Fang Zhong, Steven Kimbrough, Dong Wu","year":"2002","venue":"Group Decision and Negotiation","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}