作为推理的意义协商
Meaning Negotiation as Inference
摘要：
意义协商（MN）是主体就一组术语的意义达成一致的一般过程。人工智能学者通过论证方案、信念合并和信息融合算子以及本体对齐来处理MN问题，但所提出的方法依赖于参与者的数量。在本文中，我们给出了一个一般模型的MN为任意数量的代理，其中每个参与者讨论与其他人她的观点，通过展示它在一个实际的一组约束的意义上的谈判条款。我们称这种对个体观点的呈现为角度。代理人的目的不是形成一个共同的观点，而是就一个可接受的共同角度达成一致。我们分别分析了两个代理（\emph{bilateral}或\emph{pairwise} MN MN）和两个以上的代理（\emph{multiparty} MN）的MN的过程，我们使用博弈论模型来理解这两种情况下的过程是如何发展的：该模型是双边MN的讨价还价游戏和多方MN的英式拍卖。我们正式的过程中达成这样的协议，给出了一个演绎系统，包括规则是一致的，足以代表MN。
英文摘要：
Meaning negotiation (MN) is the general process with which agents reach an agreement about the meaning of a set of terms. Artificial Intelligence scholars have dealt with the problem of MN by means of argumentations schemes, beliefs merging and information fusion operators, and ontology alignment but the proposed approaches depend upon the number of participants. In this paper, we give a general model of MN for an arbitrary number of agents, in which each participant discusses with the others her viewpoint by exhibiting it in an actual set of constraints on the meaning of the negotiated terms. We call this presentation of individual viewpoints an angle. The agents do not aim at forming a common viewpoint but, instead, at agreeing about an acceptable common angle. We analyze separately the process of MN by two agents (\emph{bilateral} or \emph{pairwise} MN) and by more than two agents (\emph{multiparty} MN), and we use game theoretic models to understand how the process develops in both cases: the models are Bargaining Game for bilateral MN and English Auction for multiparty MN. We formalize the process of reaching such an agreement by giving a deduction system that comprises of rules that are consistent and adequate for representing MN.
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。
