Distributed Hypothesis Generation and Evaluation

这个博士正在开发方法，以支持分析师和半自动代理在智能场景中协作执行假设生成和评估。这些方法利用了论证理论，这是一种代表论点之间冲突的数学结构。论证可以在计算上表示为一个本体，它允许使用关联数据方法进行分布式分析。已经开发了一种方法，允许具有专业知识的分析师通过产生与分析整体一致的子论点，以协作的方式将他们的知识贡献给情报分析的不同部分。然后，分析师或代理人可以使用定义明确的抽象论证理论来评估假设，这可能会建议进一步收集信息或需要改进假设。情报分析被认为是一个循环，因此该过程的所有阶段都应与协作和分布式分析相兼容。所开发的方法应对照其他情报分析技术进行验证。情报周期包括了解可用信息，根据分析员对情景的理解和可用情报生成假设，并使用现有证据和结构性分析技术评估这些假设，如分析竞争性假设。在这些步骤中的每一个步骤中，分析师以及未来的半自主代理根据可用的不同情报和他们的背景知识进行推理。允许分析师在情报问题上进行有效合作，可以提高分析质量，减少分析中的偏差。

This PhD is developing approaches to support analysts and semi-automated agents in collaboratively performing hypothesis generation and evaluation in an intelligence scenario. The approaches utilise argumentation theory, a mathematical construction representing conflicts between arguments. Argumentation can be represented computationally as an ontology which allows a linked data approach to distributed analysis.Approaches have been developed to allow analysts with expertise to contribute their knowledge to different parts of an intelligence analysis in a collaborative manner by generating sub-arguments which are coherent with the analysis as a whole. Analysts or agents, using a well-defined theory of abstract argumentation, may then evaluate the hypotheses, which may suggest further collection of information or require refinement of hypotheses. Intelligence analysis is considered as a cycle and therefore all stages of the process should be compatible with collaborative and distributed analysis. The approaches developed should be validated against other intelligence analysis techniques. Care has been taken to mitigate the potential for biases in the system.The intelligence cycle consists of understanding the information available, generating hypotheses, based on the analysts' situational understanding and the intelligence available and evaluating these hypotheses using the available evidence and structural analytical techniques, eg Analysis of Competing Hypotheses. In each of these steps, analysts, and in future semi-autonomous agents, perform reasoning based on different intelligence available and their background knowledge. Allowing analysts to collaborate effectively on intelligence problems could improve the quality of analyses and reduce biases in an analysis.