Compiling Temporal Numeric Planning into Discrete PDDL+: Extended Version
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918384480813056 |
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| author | Micheli, Andrea Scala, Enrico Valentini, Alessandro |
| author_facet | Micheli, Andrea Scala, Enrico Valentini, Alessandro |
| contents | Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation is polynomial, retains the plan length up to a constant factor and is experimentally shown to be of practical relevance for hard temporal numeric problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12188 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Compiling Temporal Numeric Planning into Discrete PDDL+: Extended Version Micheli, Andrea Scala, Enrico Valentini, Alessandro Artificial Intelligence Since the introduction of the PDDL+ modeling language, it was known that temporal planning with durative actions (as in PDDL 2.1) could be compiled into PDDL+. However, no practical compilation was presented in the literature ever since. We present a practical compilation from temporal planning with durative actions into PDDL+, fully capturing the semantics and only assuming the non-self-overlapping of actions. Our compilation is polynomial, retains the plan length up to a constant factor and is experimentally shown to be of practical relevance for hard temporal numeric problems. |
| title | Compiling Temporal Numeric Planning into Discrete PDDL+: Extended Version |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.12188 |