A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Planning control rules for reactive agents
Artificial Intelligence
Casper: Space Exploration through Continuous Planning
IEEE Intelligent Systems
History-Based Diagnosis Templates in the Framework of the Situation Calculus
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Replanning Using Hierarchical Task Network and Operator-Based Planning
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Belief management for high-level robot programs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Replanning in domains with partial information and sensing actions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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We consider the problem of automated planning and control for an execution agent operating in environments that are partially-observable with deterministic exogenous events. We describe a new formalism and a new algorithm, DiscoverHistory, that enables our agent, DHAgent, to proactively expand its knowledge of the environment during execution by forming explanations that reveal information about the world. We describe how DHAgent uses this information to improve the projections made during planning. Finally, we present an ablation study that examines the impact of explanation generation on execution performance. The results of this study demonstrate that our approach significantly increases the goal achievement success rate of DHAgent against an ablated version that does not perform explanation.