ent agent types and the other party can be any one of these types.

Human-Agent negotiations

The issue of automated negotiation is too broad to cover in a short review paper. To this end, we have decided to concentrate on adversarial bilateral bargaining in which the automated agent is matched with people. The challenges in this area could motivate readers to pursue this field (note that this sets the focus and leaves most auction settings outside the scope of this article, even though automated agents that bid in auctions competing with humans have been proposed and evaluated in the literature; for example, Grossklags and Schmidt11).

Automated Negotiator Agents. The problem of developing an automated agent for negotiations is not new for researchers in the fields of multiagent systems and game theory (for example, Kraus20 and Muthoo26). However, designing an automated agent that can successfully negotiate with a human counterpart is quite different from negotiating with another automated agent. Although an automated agent that played in the Diplomacy game with other human players was introduced by Kraus and Lehmann22 some 20 years ago, the difficulties of designing proficient automated negotiators have not been resolved.

In essence, assumptions in most research are made that do not necessarily apply in genuine negotiations with humans, such as assuming complete information or the rationality of the opponent negotiator. In this sense, both parties are assumed to be rational in their behavior (for example, the decisions made by the agents are described as rational and the agents are considered to be expected utility maximizing agents that cannot deviate from their prescribed behavior). Yet, when dealing with human counterparts, one must take into consideration the fact that humans do not necessarily maximize expected utility or behave rationally. In particular, results from social sciences suggest that people do not follow equilibrium strategies. 6, 25 Moreover, when playing with humans, the theoretical equilibrium strategy is not necessarily the optimal strategy. 38 In this respect,

equilibrium-based automated agents that play with people must incorporate heuristics to allow for “unknown” deviations in the behavior of the other party. Moreover, when people are the ones who design agents, they do not always design them to follow equilibrium strategies. 12 Nonetheless, some assumptions are made, mainly that the other party will not necessarily maximize its expected utility. However, if given two offers, it will prefer the one with the highest utility value. Lastly, it has been shown that whether the opponent is oblivious or has full knowledge that its counterpart is a computer agent can change the overall result. For example, Grossklags and Schmidt11 showed that efficient market prices were achieved when human subjects knew that computer agents existed in a double auction market environment. Sanfey34 matched humans with other humans and with computer agents in the Ultimatum Game and showed that people rejected unfair offers made by humans at significantly higher rates than those made when matched with a computer agent.

Automated Agents Negotiating with People. Researchers have tried to take some of these issues into consideration when designing agents that are capable of proficiently negotiating with people. For example, dealing only with the bounded rationality of the opponent, several researchers have suggested new notions of equilibria (for example, the trembling hand equilibrium described in Rasmusen30). Approximately 10 years ago, Kasbah, a seminal negotiation model between agents designed by humans, was presented in the virtual marketplace by Chavez and Maes. 5 Here, the agent’s behavior was fully controlled by human players. The main idea was to help users in the negotiation process between buyers and sellers by using automated negotiators. Chavez and Maes’s main innovation was not so much the sophisticated design of the automated negotiators but rather the creation of a multiagent negotiation environment. Kraus21 describe an automated agent that negotiates proficiently with humans. Although they also deal with negotiation with humans, there is complete information in their settings. Other researchers have suggested a

shift from quantitative decision theory to qualitative decision theory. 36 In using such a model it is not necessary to assume that the opponent will follow the equilibrium strategy or try to be a utility maximizer. Another approach was to develop heuristics for negotiations motivated by the behavior of people in negotiations. 22 However, the fundamental question of whether it is possible to build automated agents for negotiations with humans in open environments has not been fully addressed by these researchers.

Another direction being pursued is the development of virtual humans to train people in interpersonal skills (for example, Kenny19). Achieving this goal requires cognitive and emotional modeling, natural language processing, speech recognition, knowledge representation, as well as the construction and implementation of the appropriate logic for the task at hand (for example, negotiation), is in order to make the virtual human into a good trainer. An example of the researchers’ prototype, in which trainees conduct real-time negotiations with a virtual human doctor and a village elder to move a clinic to another part of the town out of harm’s way is given in Figure 2.

Commercial companies and schools have also displayed interest in automated negotiation technologies. Many courses and seminars are offered for the public and for institutions. These courses often guarantee that upon completion you will “know many strategies on which to base the negotiation,” “Discover the negotiation secrets and techniques,” “Learn common rival’s tactics and how to neutralize them” and “Be able to apply an efficient negotiation strategy.” 1, 27 Yet, in many of these courses, the agents are restricted to one domain and cannot be generalized. Some of the automated agents cannot be adapted to the user and are restricted to a single attribute negotiation with no time constraints. Nonetheless, human factors and results of laboratory and field experiments reviewed in esteemed publications9, 29 provide guidelines for the design of automated negotiators. Yet, it is still a great challenge to incorporate these guidelines in the inherent design of an agent to allow it to proficiently negotiate with people.