Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Motivations as an Abstraction of Meta-level Reasoning
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Experience management: foundations, development methodology, and internet-based applications
Experience management: foundations, development methodology, and internet-based applications
Integrated learning for goal-driven autonomy
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Hi-index | 0.00 |
The vast majority of research on AI planning has focused on automated plan recognition, in which a planning agent is provided with a set of inputs that include an initial goal (or set of goals). In this context, the goal is presumed to be static; it never changes, and the agent is not provided with the ability to reason about whether it should change this goal. For some tasks in complex environments, this constraint is problematic; the agent will not be able to respond to opportunities or plan execution failures that would benefit from focusing on a different goal. Goal driven autonomy (GDA) is a reasoning framework that was recently introduced to address this limitation; GDA systems perform anytime reasoning about what goal(s) should be satisfied [4]. Although promising, there are natural roles that case-based reasoning (CBR) can serve in this framework, but no such demonstration exists. In this paper, we describe the GDA framework and describe an algorithm that uses CBR to support it. We also describe an empirical study with a multiagent gaming environment in which this CBR algorithm outperformed a rule-based variant of GDA as well as a non-GDA agent that is limited to dynamic replanning.