Provenance-Based Assessment of Plans in Context
This work addresses the need for better plan explanation in automated planning for domains with uncertain or diverse information sources, though it appears incremental as it builds on existing planners and representation standards.
The paper tackles the problem of explaining automated plans in complex domains with diverse information sources and variable-reliability agents by presenting a provenance-based approach that extends the SHOP3 HTN planner to generate dependency information, transforms it into PROV-O representation, and uses graph propagation and TMS-inspired algorithms to assess plan properties like pertinence, sensitivity, risk, assumption support, diversity, and relative confidence.
Many real-world planning domains involve diverse information sources, external entities, and variable-reliability agents, all of which may impact the confidence, risk, and sensitivity of plans. Humans reviewing a plan may lack context about these factors; however, this information is available during the domain generation, which means it can also be interwoven into the planner and its resulting plans. This paper presents a provenance-based approach to explaining automated plans. Our approach (1) extends the SHOP3 HTN planner to generate dependency information, (2) transforms the dependency information into an established PROV-O representation, and (3) uses graph propagation and TMS-inspired algorithms to support dynamic and counter-factual assessment of information flow, confidence, and support. We qualified our approach's explanatory scope with respect to explanation targets from the automated planning literature and the information analysis literature, and we demonstrate its ability to assess a plan's pertinence, sensitivity, risk, assumption support, diversity, and relative confidence.