We present a framework that enables the discovery of diverse and natural-looking motion strategies for athletic skills such as the high jump. The strategies are realized as control …
Current work in planning with preferences assumes that user preferences are completely specified, and aims to search for a single solution plan to satisfy these. In many real world …
Explaining decisions is at the heart of explainable AI. We investigate the computational complexity of providing a formally-correct and minimal explanation of a decision taken by a …
Generating diverse populations of high-quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research …
Explaining decisions is at the heart of explainable AI. We investigate the computational complexity of providing a formally-correct and minimal explanation of a decision taken by a …
We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling …
When modeling an application of practical relevance as an instance of a combinatorial problem X, we are often interested not merely in finding one optimal solution for that …
In many planning situations, a planner is required to return a diverse set of plans satisfying the same goals which will be used by the external systems collectively. We take a domain …
Finding diverse solutions in combinatorial problems recently has received considerable attention (Baste et al. 2020; Fomin et al. 2020; Hanaka et al. 2021). In this paper we study …