What Just Happened? Explaining the Past in Planning and Execution

Abstract

We consider the problem of automated planning in partiallyobservable dynamic environments, where exogenous events that cannot be directly observed affect the state of the world. In these environments, a planner?s knowledge of the world is limited, and state transitions can be both ambiguous and difficult to predict due to that lack of knowledge. We describe a new formalism and new algorithms that enable a planner to proactively expand its knowledge of the environment during planning and execution, by modeling the exogenous events that can occur and forming explanations that reveal information about the world. We have implemented our new algorithms in a variant of the well-known SHOP2 planner that can replan when a failure occurs during plan execution. We have conducted an ablation study in two planning domains to examine the effects of explanation on execution. The results demonstrate that our algorithm successfully increases the performance of an agent using it in two planning domains. This improvement results from the agent having increased knowledge of the environment which allows it to more accurately predict future events and ultimately make better plans.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA559969

Entities

People

  • Matthew Klenk
  • Matthew Molineaux
  • Ugur Kuter

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Artificial Satellites
  • Autonomous Agents
  • Environment
  • Failure Mode And Effect Analysis
  • High Energy
  • Information Operations
  • Malfunctions
  • Mathematics
  • Military Research
  • Observation
  • Probability
  • Reasoning
  • Recognition
  • Side Effects

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Artificial Intelligence
  • Computational Modeling and Simulation