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Quantifying privacy in multiagent planning

机译：量化多主体规划中的隐私

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Privacy is often cited as the main reason to adopt a multiagent approach for a certain problem. This also holds true for multiagent planning. Still, a metric to evaluate the privacy performance of planners is virtually non-existent. This makes it hard to compare different algorithms on their performance with regards to privacy. Moreover, it prevents multiagent planning methods from being designed specifically for this aspect.rnThis paper introduces such a measure for privacy. It is based on Shannon's theory of information and revolves around counting the number of alternative plans that are consistent with information that is gained during, for example, a negotiation step, or the complete planning episode. To accurately obtain this measure, one should have intimate knowledge of the agent's domain. It is unlikely (although not impossible) that an opponent who learns some information on a target agent has this knowledge. Therefore, it is not meant to be used by an opponent to understand how much he has learned. Instead, the measure is aimed at agents who want to know how much privacy they have given up, or are about to give up, in the planning process. They can then use this to decide whether or not to engage in a proposed negotiation, or to limit the options they are willing to negotiate upon.
机译：经常将隐私视为针对某些问题采用多代理方法的主要原因。对于多主体计划也是如此。尽管如此，评估规划者的隐私表现的指标实际上还是不存在的。这使得很难比较不同算法在隐私方面的性能。而且，它阻止了针对此方面专门设计多代理计划方法。它基于Shannon的信息理论，围绕计算与在谈判步骤或整个计划阶段获得的信息相一致的替代计划的数量进行。为了准确地获得这一措施，应该对代理人的领域有深入的了解。从目标代理那里学到一些信息的对手不太可能（尽管不是不可能）知道这一点。因此，这并不意味着对手会用它来了解他所学到的东西。相反，该措施针对的是希望知道在计划过程中他们已经放弃或将要放弃多少隐私的代理商。然后，他们可以使用它来决定是否进行提议的谈判，或者限制他们愿意进行谈判的选择。

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