tribute to the feasibility of comparing between different automated agents when matched with people. Preliminary work on this facet is already under way by Hindriks et al. 13 and Oshrat et al., 28 however, we believe the aspect of generality should be addressed more by researchers. In this respect, metrics should be designed to allow a comparison between agents. To achieve this, some of the questions described earlier regarding “what constitutes a good negotiator agent?” should be answered as well.

In addition, argumentation, though dealt with in the past, still poses a challenge for researchers in this field. For example, about 10 years ago Kraus23 presented argumentation as an iterative process emerging from exchanges among agents to persuade each other and bring about a change in intentions. They developed a formal logic that forms a basis for the development of a formal axiomatization system for argumentation. In particular, Kraus identified argumentation categories in human negotiations and demonstrated how the logic can be used to specify argument formulations and evaluations. Finally, they developed an agent that was implemented, based on the logical model.

However, this agent was not matched with human negotiators. Moreover, there are several open research questions associated with how to integrate the argumentation model into automated negotiators. Since the argumentation module is based on logic and thus is time consuming, a more efficient approach should be used. In addition, the current model is built on a very complex model of the opponent and therefore should be incorporated in the automated negotiator’s model of the opponent. In order to facilitate the design, a mapping between the logical model and the utili-ty-based model is required.

To conclude, in recent years the field of automated negotiators that can proficiently negotiate with human players has received much needed focus and the results are encouraging. We presented several of these automated negotiators and showed it is indeed possible to design such proficient agents. Nonetheless, there are still challenges that pose interest-

ing research questions that must be pursued and exciting work is still very much in progress.

Acknowledgments

We thank David Sarne, Ya’akov (Kobi) Gal, and the anonymous referees for their helpful remarks and suggestions.

 

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this research is based upon work supported in part by the u.s. army research laboratory and the u.s. army research office under grant number W911nF-08-1-0144 and under nsF grant 0705587.

Raz Lin is a Postdoctoral Fellow in the computer science department at bar-Ilan university, ramat-Gan, Israel.

Sarit Kraus is a professor of computer science department at bar-Ilan university, ramat-Gan, Israel, and adjunct professor in the Institute for advanced Computer studies at the university of Maryland, College Park, Md.