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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2307.12289 |
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| _version_ | 1866916378109280256 |
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| author | Acampora, Renato Geatti, Luca Gigante, Nicola Montanari, Angelo Picotti, Valentino |
| author_facet | Acampora, Renato Geatti, Luca Gigante, Nicola Montanari, Angelo Picotti, Valentino |
| contents | In the timeline-based approach to planning, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, by providing an effective and computationally optimal approach to controller synthesis for timeline-based games. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_12289 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Controller Synthesis for Timeline-based Games Acampora, Renato Geatti, Luca Gigante, Nicola Montanari, Angelo Picotti, Valentino Artificial Intelligence In the timeline-based approach to planning, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, by providing an effective and computationally optimal approach to controller synthesis for timeline-based games. |
| title | Controller Synthesis for Timeline-based Games |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2307.12289 |