We focus on current and future complex AI autonomous systems that integrate sensors, computation, and actuation to perform tasks of benefit to humans. Examples of such systems are auto-pilots, medical assistants, internet-of-things components, and mobile service robots. One of the key aspects to bring such complex AI systems to safe and acceptable existence is the ability for such systems to provide transparency on their representations, interpretations, choices, and decisions, in summary, their internal state.

We believe that, to build AI systems that are safe, as well as accepted and trusted by humans, we need to equip them with the capability to explain their actions, recommendations, and inferences. Our proposed project aims at researching on the specification, formalization, and generation of explanations, with a concrete focus on seamlessly integrated AI systems that sense and reason about multi-modal information in symbiosis with humans. As a result, humans will be able to query robots for explanations about their recommendations or actions, and carry any needed corrections.

AI systems have long been challenged with providing explanations about their reasoning. Automated theorem provers, explanation-based learning systems, and conflict-based constraint solvers are examples where inference is supplemented by the underlying processed knowledge and rules.

We focus on current and future complex AI autonomous systems that integrate perception, cognition, and action, in tasks to service humans. These systems can be viewed as cyber-physical-social systems, such as auto-pilots, medical assistants, internet-of-things components, and mobile service robots.

We propose to research on bringing such complex AI systems to safe and acceptable existence by providing transparency on their representations, interpretations, choices, and decisions. We will develop mining techniques to enable the analysis and explanation of temporally-logged sensory and execution data, constrained by the underlying behavior architecture, as well as the uncertainty of the sensed environment. We will address the need for probabilistic and knowledge-based inference; the variety of input data modalities; and the coordination of multiple reasoning agents.

We will concretely research on autonomous mobile service robots, such as CoBots, as well as quadrotors. We envision humans setting queries about the robots performance and the choice of their actions. Our generated explanations will increase the understanding, and robot safety.