Paper Info
Title
DiscoverHistory: understanding the past in planning and execution
Abstract
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.
Year
DOI
Venue
2012
10.5555/2343776.2343838
AAMAS
Keywords
Field
DocType
deterministic exogenous event,ablated version,automated planning,ablation study,new algorithm,execution performance,new formalism,goal achievement success rate,explanation generation,execution agent operating,abductive reasoning,planning
Computer science,Abductive reasoning,Artificial intelligence,Formalism (philosophy)
Conference
ISBN
Citations 
PageRank 
0-9817381-2-5
7
0.61
References 
Authors
15
3
Name
Order
Citations
PageRank
Matthew Molineaux111613.83
Ugur Kuter2126474.54
Matthew Klenk3322.86