6 accepted papers
Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes. Such models are able to explain each decision by pointing to the prototypes that contributed the most to the final outc
Automated planning involves finding a sequence of actions that changes the world from an initial state to a final state with goals satisfied. The general problem is PSPACE-hard. Nevertheless, many restricted variants are NP-complete or even in P. Existing complexity work focuses mostly on plan exist
Multi-agent pathfinding MAPF is a problem where multiple autonomous agents must find paths to their respective destinations without colliding. Decisional MAPF on undirected graphs can be solved in polynomial time; Several optimization MAPF variants however are NP-complete. The directed graph variant…
Hardness of modeling a planning domain is a major obstacle for making automated planning techniques accessible. We developed a tool that helps modelers correct domains based on available information such as the known feasibility or infeasibility of certain plans. Designing model repair strategies th…
Deep learning is increasingly used to learn policies for planning problems, yet policies represented by neural networks are difficult to interpret, verify and trust. Existing formal approaches to post-hoc explanations provide concise reasons for a single decision made by an ML model. However, unders…
Designing a planning domain is a difficult task in AI planning. Assisting tools are thus required if we want planning to be used more broadly. In this paper, we are interested in automatically correcting a flawed domain. In particular, we are concerned with the scenario where a domain contradicts a…