A Theory of Intra-Agent Replanning

Abstract

When autonomous agents execute in the real world, the world state as well as the objectives may change from the agent's original conception of those things. In such cases, the agent's planning process must modify the existing plan to make it amenable to the new conditions, and to resume execution. The need for inter-agent replanning, in terms of commitments to other agents, is understood in the multi-agent systems community. Such inter-agent replanning also motivates an intra-agent replanning problem for each individual agent. However the single-agent planning community has mostly limited its view of replanning to reducing the computational effort involved by minimally perturbing the current plan structure to replan. This is not very appropriate as a general model for intra-agent replanning, which may consist of various techniques that are employed according to the scenario at hand. In this paper, we present a general replanning problem that is built on various types of replanning constraints. We show that these constraints can model different types of replanning, including the similarity-based approaches used in the past and sensitivity to commitments made to other agents. Finally, we show that partial satisfaction planning provides a good substrate for modeling this general replanning problem.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2013
Accession Number
ADA583466

Entities

People

  • David E. Smith
  • Kartik Talamadupula
  • Subbarao Kambhampati
  • William Cushing

Organizations

  • Arizona State University

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Autonomous Agents
  • Communities
  • Computations
  • Computer Science
  • Information Systems
  • Intelligent Agents
  • Intelligent Systems
  • Multiagent Systems
  • Perturbations
  • Standards
  • Substrates
  • Urban Areas

Readers

  • Artificial Intelligence
  • Nanofabrication and Microfabrication.
  • Oncology

Technology Areas

  • AI & ML