Mechanisms for Automated Negotiation in State Oriented Domains
<|reference_start|>Mechanisms for Automated Negotiation in State Oriented Domains: This paper lays part of the groundwork for a domain theory of negotiation, that is, a way of classifying interactions so that it is clear, given a domain, which negotiation mechanisms and strategies are appropriate. We define State Oriented Domains, a general category of interaction. Necessary and sufficient conditions for cooperation are outlined. We use the notion of worth in an altered definition of utility, thus enabling agreements in a wider class of joint-goal reachable situations. An approach is offered for conflict resolution, and it is shown that even in a conflict situation, partial cooperative steps can be taken by interacting agents (that is, agents in fundamental conflict might still agree to cooperate up to a certain point). A Unified Negotiation Protocol (UNP) is developed that can be used in all types of encounters. It is shown that in certain borderline cooperative situations, a partial cooperative agreement (i.e., one that does not achieve all agents' goals) might be preferred by all agents, even though there exists a rational agreement that would achieve all their goals. Finally, we analyze cases where agents have incomplete information on the goals and worth of other agents. First we consider the case where agents' goals are private information, and we analyze what goal declaration strategies the agents might adopt to increase their utility. Then, we consider the situation where the agents' goals (and therefore stand-alone costs) are common knowledge, but the worth they attach to their goals is private information. We introduce two mechanisms, one 'strict', the other 'tolerant', and analyze their affects on the stability and efficiency of negotiation outcomes.<|reference_end|>
@article{zlotkin1996mechanisms,
    title={Mechanisms for Automated Negotiation in State Oriented Domains},
    author={G. Zlotkin, J. S. Rosenschein},
    journal={Journal of Artificial Intelligence Research, Vol 5, (1996),
  163-238},
    year={1996},
    archivePrefix={arXiv},
    eprint={cs/9610101},
    primaryClass={cs.AI}
}