Intention is choice with commitment
Artificial Intelligence
A validation-structure-based theory of plan modification and reuse
Artificial Intelligence
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
An Architecture for Normative Reactive Agents
Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
Intention Reconsideration Reconsidered
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Role-assignment in open agent societies
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Suspending and resuming tasks in BDI agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Stratified Multi-agent HTN Planning in Dynamic Environments
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Interleaving temporal planning and execution in robotics domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
A domain-independent algorithm for plan adaptation
Journal of Artificial Intelligence Research
Provably bounded-optimal agents
Journal of Artificial Intelligence Research
Abstracting probabilistic actions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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In this article, we present a replanning algorithm for a decision-theoretic hierarchical planner, illustrate the experimental methodology we designed to investigate its performance, and provide an evaluation of the algorithm. The methodology relies on an agent-based framework, in which plan failures can emerge from the interplay of the agent and the environment. Given this framework, the performance of the replanning algorithm is compared with the one of planning from scratch the solution to the planning problem by executing experiments in different domains. The empirical evaluation shows the superiority of replanning with respect to planning from scratch. However, the observation of significant differences in the data collected across planning domains confirm the importance of empirical evaluation in practical systems.