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A Situation–Based Multi–Agent Architecture for Handling Misunderstandings in Interactions

机译:基于情境的多代理架构,用于处理交互中的误解

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During interactions, system actors may face up misunderstandings when their local states contain inconsistent data about the same fact. Misunderstandings in interactions are likely to reduce interactivity performances (deviation or deadlock) or even affect overall system behavior. In this paper, we characterize misunderstandings in interactions between system actors (that may be human users or system agents) in interactive adaptive systems. To deal with such misunderstandings and ensure state consistency, we present an agent-based architecture and a scenario structuring approach. The system includes several agents devoted to scenario unfolding, plot adaptation and consistency management. Scenario structuring is based on the notion of a situation that is an elementary building block dividing the interactions between systems’ actors into contextual scenes. This pattern supports not only scenario execution but consistency management as well. In order to organize and control interactions, the situation contextualizes interactions and activity of the system’s actors. It also includes prevention and tolerance agent-based mechanisms to deal with the misunderstandings and their causes. We validate our consistency management mechanisms using Uppaal simulation and provide some experimental results to show the effectiveness of our approach on an online distance learning case study
机译:在交互过程中,当系统参与者的本地状态包含有关同一事实的不一致数据时,可能会面临误解。对交互的误解很可能会降低交互性能(偏差或死锁),甚至影响整个系统的行为。在本文中,我们描述了交互式自适应系统中系统参与者(可能是人类用户或系统代理)之间的交互中的误解。为了解决这种误解并确保状态一致性,我们提出了一种基于代理的体系结构和方案结构化方法。该系统包括几个专门负责场景展开,情节适应和一致性管理的代理。场景结构基于一种情况的概念,这种情况是将系统参与者之间的交互划分为上下文场景的基本组成部分。此模式不仅支持方案执行,而且还支持一致性管理。为了组织和控制交互,情境将系统参与者的交互和活动进行了情境分析。它还包括基于预防和容忍因素的机制来处理误解及其原因。我们使用Uppaal仿真验证了我们的一致性管理机制,并提供了一些实验结果,以证明我们的方法在在线远程学习案例研究中的有效性

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