A solution approach is to structure the deductions as arguments, whereby each distinct argument has a set of premises and a conclusion. Argumentation is emerging as an important part of AI for working with uncertain knowledge. It is common to find a variety of arguments for and against a given proposition. How to accumulate these competing arguments to draw a firm conclusion is an area of much research focus in AI. Argumentation is emerging as a strong technique for handling inconsistency and uncertainty.
This talk looks at a) a way of representing arguments and their aggregations, b) various naove argument aggregation schemes from the literature and c) some novel proposals for more effective, true-to-life ways of aggregating arguments. Motivating examples are given from various professions where the use of argument is widespread. Of particular interest is using arguments that confirm each other to establish that a confirmed proposition is more definite than an unconfirmed one. These aggregations are then used to resolve inconsistencies, in a way akin to a judge deciding which side has more solid evidence. The other novel approach to be examined is decomposition, where an inconsistency arises from the muddling of two contexts.