The underlying hypothesis of knowledge-based explainable artificial intelligence is the data required for data-centric artificial intelligence agents (e.g., neural networks) are less diverse in contents than the data required to explain the decisions of such agents to humans. The idea is that a classifier can attain high accuracy using data that express a phenomenon from one perspective whereas the audience of explanations can entail multiple stakeholders and span diverse perspectives. We hence propose to use domain knowledge to complement the data used by agents. We formulate knowledge-based explainable artificial intelligence as a supervised data classification problem aligned with the CBR methodology. In this formulation, the inputs are case problems composed of both the inputs and outputs of the data-centric agent and case solutions, the outputs, are explanation categories obtained from domain knowledge and subject matter experts. This formulation does not typically lead to an accurate classification, preventing the selection of the correct explanation category. Knowledge-based explainable artificial intelligence extends the data in this formulation by adding features aligned with domain knowledge that can increase accuracy when selecting explanation categories.

Collaboration between research scientists, particularly those with diverse backgrounds, is a driver of scientific innovation. However, finding the right collaborator is often an unscientific process that is subject to chance. This paper explores recommending collaborators based on repeating patterns of previous successful collaboration experiences, what we term prototypical collaborations. We investigate a method for discovering such prototypes to use them as a basis to guide the recommendation of new collaborations. To this end, we also examine two methods for matching collaboration seekers to these prototypical collaborations. Our initial studies reveal that though promising, improving collaborations through recommendation is a complex goal.

Report on the Seventh International Conference on Case-Based Reasoning

Led by David C. Wilson (University of of usages of generalization in from the University of Ulster. The workshop CBR in robotic soccer, a theme that is researchers and practitioners. The workshops in this year's program were Case-Based An introspective talk, given by David The technical program consisted of fifteen Reasoning and Context-Awareness, W. Aha (Naval Research Lab, USA) papers and eighteen posters. They Case-Based Reasoning in the Health kicked off the event, making attendees are all included in the proceedings Sciences, Textual Case-Based Reasoning: question how case-based reasoning published by Springer. Beyond Retrieval, Uncertainty is perceived by the outside world The first oral session included contributions and Fuzziness in Case-Based Reasoning, and the balance between theoretical in textual CBR, logic-based and Knowledge Discovery and foundations and applied research.