Distributed Hypothesis Generation and Evaluation

该博士正在开发支持分析师和半自动代理在智能场景中协同执行假设生成和评估的方法。这些方法利用论证理论，一种表示论证之间冲突的数学结构。论证可以计算地表示为一个本体，它允许链接数据方法进行分布式分析。已经开发了一些方法，允许具有专业知识的分析师通过生成与整体分析一致的子论点，以协作的方式将他们的知识贡献给情报分析的不同部分。分析人员或代理人，使用定义良好的抽象论证理论，然后可以评估假设，这可能建议进一步收集信息或需要改进假设。智能分析被认为是一个循环，因此过程的所有阶段都应该与协作和分布式分析兼容。开发的方法应该与其他情报分析技术进行验证。已经采取了谨慎措施，以减轻系统中存在偏见的可能性。情报循环包括理解现有信息，根据分析人员的情境理解和现有情报，产生假设，并利用现有证据和结构分析技术评估这些假设，例如竞争性假设分析。在每一个步骤中，分析人员，以及未来的半自主代理，根据不同的可用智能和他们的背景知识执行推理。允许分析人员在情报问题上有效合作，可以提高分析的质量，减少分析中的偏见。

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.