A Strategic Model of Negotiation for Autonomous Agents

分布式人工智能（DAI）关注的是能够有效交互以实现其目标的自动代理的设计。该项目将有助于有效的自主代理的理论和设计，这些代理能够以这样一种方式进行沟通和谈判，以提高达成互利协议的可能性。作者正在开发一种战略谈判模型，该模型考虑了谈判过程本身的时间流逝。该模型用于创建自治代理的原型，该代理参与需要协商和存在时间限制的情况。基于时间对各方的差异影响、两方和受限n方环境的影响以及完全信息，构建了一个初步的模型。研究人员正在扩展该模型，以处理代理可获得的信息不完整的情况，更复杂的n方情况，以及具有有限理性的代理。与这些理论扩展平行的是，自动谈判者的设计正在开发中，并创建了一个原型。策略模型用于识别自动谈判者在各种谈判情况下的有效策略。正在建立一个元策略库，其中的策略将使用有限自动机框架来呈现。自动谈判者识别其情况的适当参数，搜索适当的策略，并根据该策略进行谈判。//

Distributed Artificial Intelligence (DAI) has been concerned with the design of automated agents which can interact effectively in order to satisfy their goals. This project will contribute to both the theory and design of efficient autonomous agents which are able to communicate and negotiate in such a way as to enhance the possibility of reaching mutually beneficial agreements. The authors are developing a strategic model of negotiation that takes the passage of time during the negotiation process itself into consideration. This model is used in the creation of a prototype of an autonomous agent that participates in situations where negotiations are required, and where time constraints exist. A preliminary model has been constructed, based on the differentialimpact of time on the parties, the effects of two-partyand restricted N-party environments, an full information. The researchers are extending the model to deal with situations where the information available to the agents is incomplete, to more complicated N-party situations, and to agents with limited rationality. Parallel to these theoretical extensions, the design of the automated negotiator is being developed and a prototype created. The strategic model is used to identify efficient strategies for the automated negotiator, for various negotiationsituations. A library of meta-strategies in which the strategies will be presented using the finite-automata framework is being built. The automated negotiator identifies the appropriate parameters of its situation, searches for the appropriate strategy, and negotiates according to that strategy. //