Airlift Challenge: A Competition for Optimizing Cargo Delivery
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913800009023488 |
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| author | Delanovic, Adis Chiu, Carmen Kolen, John F. Gülhan, Marvin Cawalla, Jonathan Beckus, Andre |
| author_facet | Delanovic, Adis Chiu, Carmen Kolen, John F. Gülhan, Marvin Cawalla, Jonathan Beckus, Andre |
| contents | Airlift operations require the timely distribution of various cargo, much of which is time sensitive and valuable. These operations, however, have to contend with sudden disruptions from weather and malfunctions, requiring immediate rescheduling. The Airlift Challenge competition seeks possible solutions via a simulator that provides a simplified abstraction of the airlift problem. The simulator uses an OpenAI gym interface that allows participants to create an algorithm for planning agent actions. The algorithm is scored using a remote evaluator against scenarios of ever-increasing difficulty. The second iteration of the competition was underway from November 2023 to April 2024. This paper describes the competition, simulation environment, and results. As a step towards applying generalized planning techniques to the problem, a temporal PDDL domain is presented for the Pickup and Delivery Problem, a model which lies at the core of the Airlift Challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17716 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Airlift Challenge: A Competition for Optimizing Cargo Delivery Delanovic, Adis Chiu, Carmen Kolen, John F. Gülhan, Marvin Cawalla, Jonathan Beckus, Andre Artificial Intelligence Airlift operations require the timely distribution of various cargo, much of which is time sensitive and valuable. These operations, however, have to contend with sudden disruptions from weather and malfunctions, requiring immediate rescheduling. The Airlift Challenge competition seeks possible solutions via a simulator that provides a simplified abstraction of the airlift problem. The simulator uses an OpenAI gym interface that allows participants to create an algorithm for planning agent actions. The algorithm is scored using a remote evaluator against scenarios of ever-increasing difficulty. The second iteration of the competition was underway from November 2023 to April 2024. This paper describes the competition, simulation environment, and results. As a step towards applying generalized planning techniques to the problem, a temporal PDDL domain is presented for the Pickup and Delivery Problem, a model which lies at the core of the Airlift Challenge. |
| title | Airlift Challenge: A Competition for Optimizing Cargo Delivery |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2404.17716 |