• Skip to primary navigation
  • Skip to main content
SRI logo
  • About
    • Press room
    • Our history
  • Expertise
    • Advanced imaging systems
    • Artificial intelligence
    • Biomedical R&D services
    • Biomedical sciences
    • Computer vision
    • Cyber & formal methods
    • Education and learning
    • Innovation strategy and policy
    • National security
    • Ocean & space
    • Quantum
    • QED-C
    • Robotics, sensors & devices
    • Speech & natural language
    • Video test & measurement
  • Ventures
  • NSIC
  • Careers
  • Contact
  • 日本支社
Search
Close
Artificial intelligence publications September 1, 2008

Efficient Message Passing and Propagation of Simple Temporal Constraints: Results on Semi-Structured Networks

Citation

Copy to clipboard


Bui, H. H., Tyson, M. and Yorke-Smith, N. Efficient Message Passing and Propagation of Simple Temporal Constraints: Results on Semi-Structured Networks, in Proceedings of CP/ICAPS’08 Joint Workshop on Constraint Satisfaction Techniques for Planning and Scheduling Problems, Sydney, Australia, Sep 2008.

Abstract

The familiar Simple Temporal Network (STN) is a widely used framework for reasoning about quantitative temporal constraints over variables with continuous or discrete domains. The inference tasks of determining consistency and deriving the minimal network are traditionally achieved by graph algorithms (e.g., Floyd Warshall, Johnson) or by iteration of narrowing operators (e.g., 4STP). However, none of these existing methods exploit effectively the treedecomposition structure of the constraint graph of an STN. Methods based on variable elimination (e.g., adaptive consistency) can exploit this structure, but have not been applied to STNs as far as they could, in part because it is unclear how to efficiently pass the ‘messages’ over a set of continuous domains. We first show that for an STN, these messages can be represented compactly as sub-STNs. We then present an efficient message passing scheme for computing the minimal constraints of an STN. Analysis of this algorithm, Prop-STP, brings formal explanation of the performance of the existing STN solvers 4STP and SR-PC. Empirical results validate the efficiency of Prop-STP, demonstrating performance comparable to 4STP, in cases where the constraint network is known to have small tree-width, such as those that arise in Hierarchical Task Network planning problems.

↓ Download

Share this

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you.

Expect a response within 48 hours.

Career call to action image

Make your own mark.

Search jobs

Our work

Case studies

Publications

Timeline of innovation

Areas of expertise

Institute

Leadership

Press room

Media inquiries

Compliance

Careers

Job listings

Contact

SRI Ventures

Our locations

Headquarters

333 Ravenswood Ave
Menlo Park, CA 94025 USA

+1 (650) 859-2000

Subscribe to our newsletter


日本支社
SRI International
  • Contact us
  • Privacy Policy
  • Cookies
  • DMCA
  • Copyright © 2022 SRI International