首页> 外文会议>Proceedings of the Twenty-Sixth international Florida Artificial Intelligence Research Society Conference >Trace-Based Reasoning -Modeling Interaction Traces for Reasoning on Experiences
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Trace-Based Reasoning -Modeling Interaction Traces for Reasoning on Experiences

机译:基于轨迹的推理-基于经验的交互轨迹建模

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This paper addresses Trace-Based Reasoning (TBR) by using Case-Based Reasoning (CBR) as a descriptive framework. TBR is a reasoning paradigm in which inferences are made on specific objects called traces. Traces are sequential records of events observed and stored during an interactive process. We report two contributions. First, we propose a review of the current researches related to TBR. Then, we compare CBR and TBR. From this comparison, we show that the exploitation of traces instead of cases as knowledge sources raises very specific challenges. More precisely, new methods for defining similarity measures and for performing adaptation of traces are required. These new methods have to take into account the sequential properties of traces. We emphasis the benefits of using traces as a knowledge container in a reasoning process and we pinpoint promising applications of TBR.
机译:本文通过使用基于案例的推理(CBR)作为描述性框架来解决基于跟踪的推理(TBR)。 TBR是一种推理范例,其中对称为跟踪的特定对象进行推理。跟踪是在交互过程中观察和存储的事件的顺序记录。我们报告了两个贡献。首先,我们对当前与TBR相关的研究进行综述。然后,我们比较CBR和TBR。通过这种比较,我们表明,使用痕迹而不是案例作为知识来源会带来非常具体的挑战。更精确地,需要用于定义相似性度量和执行迹线适配的新方法。这些新方法必须考虑跟踪的顺序属性。我们强调在推理过程中使用跟踪作为知识容器的好处,并指出了TBR的有希望的应用。

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