Reputation-based decisions for logic-based cognitive agents
Reputation-based decisions for logic-based cognitive agents
复制标题
基于逻辑的认知代理的基于声誉的决策
DOI：
10.1007/s10458-010-9149-y
复制
发表时间：
2010
影响因子：
1.9
通讯作者：
Mario Paolucci
中科院分区：
文献类型：
--
作者：
Isaac Pinyol;J. Sabater;Pilar Dellunde;Mario Paolucci
计算信任和声誉模型已被认为是设计和实现代理系统所需的关键技术之一。这些模型管理和聚合代理所需的信息，以便在不确定的情况下有效地执行合作伙伴选择。对于简单的应用程序，与大多数模型中使用的博弈论方法类似的博弈论方法就足够了。然而，如果我们想要解决社会复杂的虚拟社会中发现的问题，我们需要更复杂的信任和声誉系统。在这种情况下，代理做出的基于声誉的决策具有特殊的相关性，并且可以与声誉模型本身一样重要。在本文中，我们提出了将认知声誉模型 Repage 集成到认知 BDI 代理中的可能性。首先，我们指定一个能够捕获 Repage 信息语义的置信逻辑，该信息对概率进行编码。该逻辑是通过两个一阶语言层次结构来定义的，允许将公理规范作为一阶理论。信念逻辑整合来自 Repage 的图像和声誉信息，并将它们组合起来，根据这种组合定义代理的类型。我们使用这种逻辑来构建一个完整的分级 BDI 模型，指定为一个多上下文系统，其中信念、愿望、意图和计划相互交互以执行 BDI 推理。我们用一个示例和相关工作部分来总结本文，将我们的方法与当前最先进的模型进行比较。
Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification ofaxiomsas first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.