Synthesizing Customized Planners from Specifications
B. Srivastava, S. Kambhampati

TL;DR
This paper explores automatically synthesizing domain-specific planners from a declarative planning theory and control knowledge using software synthesis tools, demonstrating improved performance over classical methods.
Contribution
It introduces the CLAY architecture that semi-automatically synthesizes domain-customized planners using the KIDS system and declarative planning theories.
Findings
Synthesized planners outperform classical refinement planners.
The approach reduces customization costs compared to conventional methods.
Demonstrates feasibility of automatic planner synthesis from declarative specifications.
Abstract
Existing plan synthesis approaches in artificial intelligence fall into two categories -- domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very efficient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very efficient in the domain for which they are designed. One enticing alternative to these approaches is to automatically synthesize domain independent planners given the knowledge about the domain and the theory of planning. In this paper, we investigate the feasibility of using existing automated software synthesis tools to support such synthesis. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used to derive a domain-customized planner through a semi-automatic…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAI-based Problem Solving and Planning · Semantic Web and Ontologies · Logic, Reasoning, and Knowledge
