We propose a framework for general-purpose imitation learning centered on cause-effect reasoning. Our approach infers a hierarchical representation of a demonstrator’s intentions, which can explain why they acted as they did. This enables rapid generalization of the observed actions to new situations. We employ a novel causal inference algorithm with formal guarantees and connections to automat...

Current research on qualitative spatial representation and reasoning mainly focuses on one single aspect of space. In real world applications, however, multiple spatial aspects are often involved simultaneously. This paper investigates problems arising in reasoning with combined topological and directional information. We use the RCC8 algebra and the Rectangle Algebra (RA) for expressing topolo...

This paper develops Probabilistic Hybrid Action Models (PHAMs), a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans. PHAMs represent aspects of robot behavior that cannot be represented by most action models used in AI planning: the temporal structure of continuous control processes, their non-deterministic effects, and several modes of...

This work presents a defense of causal contrastivism based on contexualism. As argued, our proposal contextualism is compatible with both and binarism, including explanations why in which sense secondary counterfactuals are relevant.

Abstract Background The methods with which prediction models are usually developed mean that neither the parameters nor predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when used to support decision making, there often a need for predicting outcomes under hypothetical interventions. Aims We aimed identify published developing and validati...