5 papers
Contrastive Explanations of Plans Through Model Restrictions
Benjamin Krarup, Senka Krivic, Daniele Magazzeni +3
In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user's expectation. We frame Explainable AI Planning in the context…
Robust Plan Execution with Unexpected Observations
Oscar Lima, Michael Cashmore, Daniele Magazzeni +2
In order to ensure the robust actuation of a plan, execution must be adaptable to unexpected situations in the world and to exogenous events. This is critical in domains in which c…
Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans
Michael Cashmore, Alessandro Cimatti, Daniele Magazzeni +2
One of the major limitations for the employment of model-based planning and scheduling in practical applications is the need of costly re-planning when an incongruence between the…
Towards Explainable AI Planning as a Service
Michael Cashmore, Anna Collins, Benjamin Krarup +3
Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a servi…
Towards Providing Explanations for AI Planner Decisions
Rita Borgo, Michael Cashmore, Daniele Magazzeni
In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifica…