联盟形成的上下文和可能性推理
Contextual and Possibilistic Reasoning for Coalition Formation
摘要：
在多智能体系统中，智能体通常需要依赖其他智能体来实现其目标，例如当它们缺乏所需的资源或没有能力执行所需的动作时。因此，代理商需要合作。然后，提出的一些问题是：与哪些代理合作？代理人可以实现其目标的潜在联盟是什么？由于可能性的数量可能相当大，如何使该过程自动化？然后，如何选择最合适的联盟，考虑到代理人的能力，以执行某些任务的不确定性？在这篇文章中，我们解决的问题，如何发现和评估联盟之间的代理在多智能体系统使用MCS工具，同时考虑到周围的代理的行动的不确定性。我们的方法如下：我们首先计算的解决方案空间的形成联盟使用上下文推理的方法。其次，我们的模型代理作为上下文的多上下文系统（MCS），和代理之间的依赖关系，寻求实现自己的目标，作为桥梁规则。第三，我们系统地计算所有潜在的联盟使用算法的MCS均衡，并给出了一组功能和非功能的要求，我们提出了选择最佳解决方案的方法。最后，为了处理代理的行动中的不确定性，我们扩展了我们的方法与可能性推理的功能。我们用一个机器人的例子来说明我们的方法。
英文摘要：
In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents' abilities to carry out certain tasks? In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions. Our methodology is the following: We first compute the solution space for the formation of coalitions using a contextual reasoning approach. Second, we model agents as contexts in Multi-Context Systems (MCS), and dependence relations among agents seeking to achieve their goals, as bridge rules. Third, we systematically compute all potential coalitions using algorithms for MCS equilibria, and given a set of functional and non-functional requirements, we propose ways to select the best solutions. Finally, in order to handle the uncertainty in the agents' actions, we extend our approach with features of possibilistic reasoning. We illustrate our approach with an example from robotics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
