Evaluating practical negotiating agents: results and analysis of the 2011 international competition
Baarslag, Tim, Fujita, Katsuhide, Gerding, Enrico H., Hindriks, Koen, Ito, Takayuki, Jennings, Nicholas R., Jonker, Catholijn, Kraus, Sarit, Lin, Raz, Robu, Valentin and Williams, Colin R. (2013) Evaluating practical negotiating agents: results and analysis of the 2011 international competition. Artificial Intelligence, 198, 73-103. (doi:10.1016/j.artint.2012.09.004).
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This paper presents an in-depth analysis and key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). ANAC is an international competition that challenges researchers to develop successful automated negotiation agents for scenarios where there is no information about the strategies and preferences of the opponents. The key objectives of this competition are to advance the state-of-the-art in the area of practical bilateral multi-issue negotiations, and to encourage the design of agents that are able to operate effectively across a variety of scenarios. Eighteen teams from seven different institutes competed. This paper describes these agents, the set-up of the tournament, including the negotiation scenarios used, and the results of both the qualifying and final rounds of the tournament. We then go on to analyse the different strategies and techniques employed by the participants using two methods: (i) we classify the agents with respect to their concession behaviour against a set of standard benchmark strategies (ii) we employ empirical game theory (EGT) to investigate the robustness of the strategies. Our analysis of the competition results allows us to highlight several interesting insights for the broader automated negotiation community. In particular, we show that the most adaptive negotiation strategies, while robust across different opponents, were not necessarily the ones to win the competition. Furthermore, our EGT analysis highlights the importance of considering other metrics, besides utility maximisation, in determining what makes a successful and robust negotiation agent in practical settings.
|Digital Object Identifier (DOI):||doi:10.1016/j.artint.2012.09.004|
|Keywords:||automated negotiation, bilateral negotiation, AI competitions, multi-agent systems, intelligent agents|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
|Date Deposited:||29 Nov 2011 23:14|
|Last Modified:||27 Mar 2015 15:22|
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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