Structured approach for synthesizing planners from specifications

Biplav Srivastava, Subbarao Kambhampati, Amol D. Mali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Plan synthesis approaches in AI 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 can be very efficient for the domain for which they are designed, but would need to be written separately for each domain of interest. The tediousness and the error-proneness of manual coding have hither-to inhibited work on domain-dependent planners. In this paper, we describe a novel way of automating the development of domain dependent planners using knowledge-based software synthesis tools. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used in conjunction with a declarative theory of domain independent planning, and the declarative control knowledge specific to a given domain, to semi-automatically derive customized planning code. We discus what it means to write declarative theory of planning and control knowledge for KIDS, and illustrate it by generating a range of domain-specific planners using state space and plan space refinements. We demonstrate that the synthesized planners can have superior performance compared to classical refinement planners using the same control knowledge.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Automated Software Engineering Conference, ASE
Place of PublicationLos Alamitos, CA, United States
PublisherIEEE Comp Soc
Pages18-26
Number of pages9
StatePublished - 1997
EventProceedings of the 1997 12th IEEE International Automated Software Engineering Conference, ASE - Incline Village, NV, USA
Duration: Nov 3 1997Nov 5 1997

Other

OtherProceedings of the 1997 12th IEEE International Automated Software Engineering Conference, ASE
CityIncline Village, NV, USA
Period11/3/9711/5/97

Fingerprint

Specifications
Planning

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Srivastava, B., Kambhampati, S., & Mali, A. D. (1997). Structured approach for synthesizing planners from specifications. In Proceedings of the IEEE International Automated Software Engineering Conference, ASE (pp. 18-26). Los Alamitos, CA, United States: IEEE Comp Soc.

Structured approach for synthesizing planners from specifications. / Srivastava, Biplav; Kambhampati, Subbarao; Mali, Amol D.

Proceedings of the IEEE International Automated Software Engineering Conference, ASE. Los Alamitos, CA, United States : IEEE Comp Soc, 1997. p. 18-26.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Srivastava, B, Kambhampati, S & Mali, AD 1997, Structured approach for synthesizing planners from specifications. in Proceedings of the IEEE International Automated Software Engineering Conference, ASE. IEEE Comp Soc, Los Alamitos, CA, United States, pp. 18-26, Proceedings of the 1997 12th IEEE International Automated Software Engineering Conference, ASE, Incline Village, NV, USA, 11/3/97.
Srivastava B, Kambhampati S, Mali AD. Structured approach for synthesizing planners from specifications. In Proceedings of the IEEE International Automated Software Engineering Conference, ASE. Los Alamitos, CA, United States: IEEE Comp Soc. 1997. p. 18-26
Srivastava, Biplav ; Kambhampati, Subbarao ; Mali, Amol D. / Structured approach for synthesizing planners from specifications. Proceedings of the IEEE International Automated Software Engineering Conference, ASE. Los Alamitos, CA, United States : IEEE Comp Soc, 1997. pp. 18-26
@inproceedings{cd870efade7d42258b45d75ffa18132d,
title = "Structured approach for synthesizing planners from specifications",
abstract = "Plan synthesis approaches in AI 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 can be very efficient for the domain for which they are designed, but would need to be written separately for each domain of interest. The tediousness and the error-proneness of manual coding have hither-to inhibited work on domain-dependent planners. In this paper, we describe a novel way of automating the development of domain dependent planners using knowledge-based software synthesis tools. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used in conjunction with a declarative theory of domain independent planning, and the declarative control knowledge specific to a given domain, to semi-automatically derive customized planning code. We discus what it means to write declarative theory of planning and control knowledge for KIDS, and illustrate it by generating a range of domain-specific planners using state space and plan space refinements. We demonstrate that the synthesized planners can have superior performance compared to classical refinement planners using the same control knowledge.",
author = "Biplav Srivastava and Subbarao Kambhampati and Mali, {Amol D.}",
year = "1997",
language = "English (US)",
pages = "18--26",
booktitle = "Proceedings of the IEEE International Automated Software Engineering Conference, ASE",
publisher = "IEEE Comp Soc",

}

TY - GEN

T1 - Structured approach for synthesizing planners from specifications

AU - Srivastava, Biplav

AU - Kambhampati, Subbarao

AU - Mali, Amol D.

PY - 1997

Y1 - 1997

N2 - Plan synthesis approaches in AI 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 can be very efficient for the domain for which they are designed, but would need to be written separately for each domain of interest. The tediousness and the error-proneness of manual coding have hither-to inhibited work on domain-dependent planners. In this paper, we describe a novel way of automating the development of domain dependent planners using knowledge-based software synthesis tools. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used in conjunction with a declarative theory of domain independent planning, and the declarative control knowledge specific to a given domain, to semi-automatically derive customized planning code. We discus what it means to write declarative theory of planning and control knowledge for KIDS, and illustrate it by generating a range of domain-specific planners using state space and plan space refinements. We demonstrate that the synthesized planners can have superior performance compared to classical refinement planners using the same control knowledge.

AB - Plan synthesis approaches in AI 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 can be very efficient for the domain for which they are designed, but would need to be written separately for each domain of interest. The tediousness and the error-proneness of manual coding have hither-to inhibited work on domain-dependent planners. In this paper, we describe a novel way of automating the development of domain dependent planners using knowledge-based software synthesis tools. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used in conjunction with a declarative theory of domain independent planning, and the declarative control knowledge specific to a given domain, to semi-automatically derive customized planning code. We discus what it means to write declarative theory of planning and control knowledge for KIDS, and illustrate it by generating a range of domain-specific planners using state space and plan space refinements. We demonstrate that the synthesized planners can have superior performance compared to classical refinement planners using the same control knowledge.

UR - http://www.scopus.com/inward/record.url?scp=0031362108&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031362108&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0031362108

SP - 18

EP - 26

BT - Proceedings of the IEEE International Automated Software Engineering Conference, ASE

PB - IEEE Comp Soc

CY - Los Alamitos, CA, United States

ER -