DiscoverHistory: understanding the past in planning and execution

By Matthew Molineaux, U. Kuter, M. Klenk. 2012

33 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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Citations in the corpus

Explainable Planning (2017, methodology): “Another promising approach in this direction is DiscoverHistory, described in [Molineaux et al., 2012].”