An Artificial Intelligence Framework for Investigative Reasoning

Abstract

Problem solving is one of the major paradigms in Artificial Intelligence research in which an intelligent task to automate is interpreted as a series of problems to be solved. Various problem solving techniques have been spawned in the field of AI, mostly by concentrating on a certain reasoning approach to tackle a particular class of problems. For instance, theorem proving, constraint solving and machine learning provide powerful techniques for solving AI problems. In all these approaches, background knowledge needs to be provided, from which the system will infer new knowledge. Often, however, in real world scenarios, there may not be enough background information for any single solver to solve the problem. In these situations, some researches have demonstrated the benefits of using combined reasoning, i.e., a reasoning process which employs various, often disparate, problem solving techniques in concert, in order to solve a given task. The systems that engage such reasoning processes are called combined reasoning systems. Their power draws upon disparate techniques they employ. As such, combined reasoning systems are supposed to be more capable than their constituents. In this thesis we mainly focus on using a combined reasoning approach in solving a type of problems that cannot be solved by any of the aforementioned standalone systems. We refer to this type as investigation problem which models to some extent a generic situation which might arise in, say, medical diagnosis or the solving of a crime. That is, there are a number of possible diagnoses/suspects (candidates), and the problem is to use the facts of the case to rank them in terms of their likelihood of being the cause of the illness/guilty of the crime. Such ranking often leads to further medical tests/police enquiries focusing on the most likely candidates, which will bring to light further information about the current case. We use the term dynamic investigation problems to describe a series of such problems to be solved. Solving each problem entails using the facts of the case, coupled with prior knowledge about the domain to narrow down the candidates to just one. However, when there is no upright solution due to lack of some essential information, additional relevant information can often be found in related past cases thereby irregularities can be observed and utilized. Hence, dynamic investigation problems are hybrid machine-learning/constraint solving problems, and as such are more realistic and of interest to the wider AI community. In this thesis we focus on formal definition, exploration, generation and solution of “Dynamic Investigation Problems”, and we develop a framework which performs “Investigative Reasoning”, that is a framework in which a combination of reasoning techniques are incorporated in order to tackle dynamic investigation problems. i