Abstract
Over the past decade automated negotiation has developed into a subject of central interest in distributed artificial intelligence. For a great part this is because of its broad application potential in different areas such as economics, e-commerce, the political and social sciences. The complexity of practical automated negotiation – a multi-issue, incomplete-information and continuous-time environment – poses severe challenges, and in recent years many negotiation strategies have been proposed in response to this challenge. Traditionally, the performance of such strategies is evaluated in game-theoretic settings in which each agent “globally” interacts (negotiates) with all other participating agents. This traditional evaluation, however, is not suited for negotiation settings that are primarily characterized by “local” interactions among the participating agents, that is, settings in which each of possibly many participating agents negotiates only with its local neighbors rather than all other agents. This paper presents an approach to handle this type of local setting. Starting out from the traditional global perspective, the negotiations are also analyzed in a new fashion that negotiation locality (hence spatial information about the agents) is taken into consideration. It is shown how both empirical and spatial evolutionary game theory can be used to interpret bilateral negotiation results among state of the art negotiating agents in these different scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
The Fourth International Automated Negotiating Agent Competition (ANAC 2013), http://www.itolab.nitech.ac.jp/ANAC2013/
Baarslag, T., Fujita, K., Gerding, E.H., Hindriks, K., Ito, T., Jennings, N.R., Jonker, C., Kraus, S., Lin, R., Robu, V., Williams, C.R.: Evaluating practical negotiating agents: Results and analysis of the 2011 international competition. Artificial Intelligence 198, 73–103 (2013)
Chen, S., Ammar, H.B., Tuyls, K., Weiss, G.: Optimizing complex automated negotiation using sparse pseudo-input Gaussian processes. In: Proceedings of the 12th Int. Joint Conf. on Automomous Agents and Multi-Agent Systems, pp. 707–714. ACM (2013)
Chen, S., Ammar, H.B., Tuyls, K., Weiss, G.: Using conditional restricted boltzmann machine for highly competitive negotiation tasks. In: Proceedings of the 23rd Int. Joint Conf. on Artificial Intelligence, pp. 69–75. AAAI Press (2013)
Chen, S., Weiss, G.: An Efficient and Adaptive Approach to Negotiation in Complex Environments. In: Proceedings of the 20th European Conference on Artificial Intelligence, pp. 228–233. IOS Press (2012)
Chen, S., Weiss, G.: An efficient automated negotiation strategy for complex environments. Engineering Applications of Artificial Intelligence 26(10), 2613–2623 (2013)
Fujita, K., Ito, T., Baarslag, T., Hindriks, K., Jonker, C., Kraus, S., Lin, R.: The Second Automated Negotiating Agents Competition (ANAC 2011). In: Ito, T., Zhang, M., Robu, V., Matsuo, T. (eds.) Complex Automated Negotiations. SCI, vol. 435, pp. 183–197. Springer, Heidelberg (2012)
Hao, J., Leung, H.: ABiNeS: An adaptive bilateral negotiating strategy over multiple items. In: Proceedings of WI/IAT 2012, pp. 95–102. IEEE Computer Society (2012)
Hindriks, K., Jonker, C., Kraus, S., Lin, R., Tykhonov, D.: Genius: negotiation environment for heterogeneous agents. In: Proceedings of the 8th International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1397–1398. ACM (2009)
Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds.): New Trends in Agent-Based Complex Automated Negotiations. SCI, vol. 383. Springer, Heidelberg (2012)
Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation 10(2), 199–215 (2001)
Jordan, P.R., Kiekintveld, C., Wellman, M.P.: Empirical game-theoretic analysis of the tac supply chain game. In: Proceedings of AAMAS 2007, pp. 1188–1195. ACM (2007)
Killingback, T., Doebeli, M.: Spatial evolutionary game theory: Hawks and doves revisited. Proceedings of the Royal Society of London. Series B: Biological Sciences 263(1374), 1135–1144 (1996)
Marsa-Maestre, I., Lopez-Carmona, M.A., Ito, T., Zhang, M., Bai, Q., Fujita, K. (eds.): Novel Insights in Agent-based Complex Automated Negotiation. Springer (2014)
Szabó, G., Fáth, G.: Evolutionary games on graphs. Physics Reports 446(4), 97–216 (2007)
Williams, C., Robu, V., Gerding, E., Jennings, N.: Using gaussian processes to optimise concession in complex negotiations against unknown opponents. In: Proceedings of the 22nd Int. Joint Conf. on Artificial Intelligence, pp. 432–438. AAAI Press (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chen, S., Hao, J., Weiss, G., Tuyls, K., Leung, Hf. (2014). Evaluating Practical Automated Negotiation Based on Spatial Evolutionary Game Theory. In: Lutz, C., Thielscher, M. (eds) KI 2014: Advances in Artificial Intelligence. KI 2014. Lecture Notes in Computer Science(), vol 8736. Springer, Cham. https://doi.org/10.1007/978-3-319-11206-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-11206-0_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11205-3
Online ISBN: 978-3-319-11206-0
eBook Packages: Computer ScienceComputer Science (R0)