Efficiently Reasoning with Interval Constraints in Forward Search Planning
- Submitting institution
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King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 142408346
- Type
- E - Conference contribution
- DOI
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10.1609/aaai.v33i01.33017562
- Title of conference / published proceedings
- Thirty-Third AAAI Conference on Artificial IntelligenceThirty-First Conference on Innovative Applications of Artificial IntelligenceThe Ninth Symposium on Educational Advances in Artificial Intelligence
- First page
- 7562
- Volume
- 33
- Issue
- 1
- ISSN
- 2374-3468
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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7
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first paper to bridge two major temporal planning paradigms: from timeline, to PDDL. It presents two ways of capturing, in forward-chaining planners, the temporal constraints of timeline planners: via compilation to PDDL, and extending the planner to reason with them natively. A detailed empirical evaluation shows this outperforms the APSI timeline planner on all but trivial problems, and gives good performance across several benchmarks. This paper is a crowning output of the EU project ERGO (https://www.h2020-ergo.eu/): the techniques scale for use on on-board robotic space missions, and supported funding for continued development in the ADE project (https://www.h2020-ade.eu/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -