Provenance-Based Assessment of Plans in Context

TL;DR

This paper introduces a provenance-based method for explaining and assessing plans in complex domains by integrating dependency information into planners and using graph algorithms to evaluate confidence and risk.

Contribution

It extends the SHOP3 HTN planner to generate dependency data and transforms it into PROV-O format for dynamic plan assessment, a novel integration of provenance in planning.

Findings

Supports dynamic assessment of plan confidence and risk

Enables explanation of plan pertinence and sensitivity

Demonstrates effectiveness in complex planning scenarios

Abstract

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.