Algebric Decision Diagrams and Their Applications
Formal Methods in System Design
Exploiting first-order regression in inductive policy selection
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
ReTrASE: integrating paradigms for approximate probabilistic planning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A formal study on the dualities in temporal projection problems
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Generalizing and executing plans
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Flexible execution of partial order plans with temporal constraints
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Domain-independent multi-agent plan repair
Journal of Network and Computer Applications
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Partial-order plans (POPs) have the capacity to compactly represent numerous distinct plan linearizations and as a consequence are inherently robust. We exploit this robustness to do effective execution monitoring. We characterize the conditions under which a POP remains viable as the regression of the goal through the structure of a POP. We then develop a method for POP execution monitoring via a structured policy, expressed as an ordered algebraic decision diagram. The policy encompasses both state evaluation and action selection, enabling an agent to seamlessly switch between POP linearizations to accommodate unexpected changes during execution. We demonstrate the effectiveness of our approach by comparing it empirically and analytically to a standard technique for execution monitoring of sequential plans. On standard benchmark planning domains, our approach is 2 to 17 times faster and up to 2.5 times more robust than comparable monitoring of a sequential plan. On POPs that have few ordering constraints among actions, our approach is significantly more robust, with the ability to continue executing in up to an exponential number of additional states.