Large repositories of products, patents and scientific papers ofer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. In this talk, we will discuss several recent works that explore how to support creative innovation with analogies and idea representations. We propose novel representations that automatically extract diferent kinds of useful structure from idea descriptions, and demonstrate how these representations can be used to support creative tasks such as ideation, functional search for ideas, and exploration of the design space around a focal problem.
Similarity measures at the core of analogical transfer and case-based prediction
Marie-Jeanne Lesot Abstract: Case-based prediction applies the plausible inference principle of analogical transfer, according to which if two cases are similar with respect to some criteria, in particular in the situation space, then it is plausible that they are also similar with respect to other criteria, in particular in the outcome space. In a first part, the presentation will review some existing approaches to case-based prediction, distinguishing them according to the type of knowledge used to measure the compatibility between the two sets of similarity relations. In a second part, the presentation will discuss the very notion of similarity measure, highlighting their variety both for numerical and categorical descriptive features. It will finally present some equivalence results that allow to define a reduced number of similarity families, providing some guidance for their selection.
Short Biography: Marie-Jeanne Lesot is an associate professor in the Computer Science Lab of Sorbonne Université, LIP6, and a member of the Learning and Fuzzy Intelligent systems (LFI) group. Her research interests focus on fuzzy machine learning with an objective of data interpretation and semantics integration, within the eXplainable Artificial Intelligence framework; they include similarity measures, fuzzy clustering, linguistic summaries and information scoring. She is also interested in approximate reasoning and the use of non classical logics, in particular weighted variants with increased expressiveness that are close to natural human reasoning processes.
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