基于论证的场景解释使用可行的逻辑编程

C. Koc, Sanem Sariel
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引用次数: 1

摘要

在一个需要在真实世界中运行的智能体系统中,面对不可靠、不完整、不一致的感官数据,如何保持世界模型的一致性是需要解决的问题。在本文中,我们提出了一种解决这个问题的方法,通过应用基于论证的场景解释框架来准确地建模和表示代理的观察和信念。我们的方法是基于时间和概率可行逻辑规划的推理。在舞台机器人模拟器上进行了仿真实验,对该方法的性能进行了评价。我们还证明,我们的方法适用于一个自主先锋3-AT机器人的现实世界场景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Argumentation-based scene interpretation using defeasible logic programming
In an agent system that needs to operate in a real world, the problem of maintaining a consistent world model in the face of unreliable, incomplete and inconsistent sensory data should be solved. In this paper, we present an approach that addresses this problem by applying an argumentation-based scene interpretation framework for accurately modelling and representing the observations and beliefs of an agent. Our approach is based on temporal and probabilistic defeasible logic programming for reasoning. The performance of our approach is evaluated on simulation experiments in the Stage Robot Simulator. We also show that our approach is applicable to real world scenarios with an autonomous Pioneer 3-AT robot.
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