Optimizing Agent Collaboration through Heuristic Multi-Agent Planning
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866914929176477696 |
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| author | Soffair, Nitsan |
| author_facet | Soffair, Nitsan |
| contents | The SOTA algorithms for addressing QDec-POMDP issues, QDec-FP and QDec-FPS, are unable to effectively tackle problems that involve different types of sensing agents. We propose a new algorithm that addresses this issue by requiring agents to adopt the same plan if one agent is unable to take a sensing action but the other can. Our algorithm performs significantly better than both QDec-FP and QDec-FPS in these types of situations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_01246 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimizing Agent Collaboration through Heuristic Multi-Agent Planning Soffair, Nitsan Artificial Intelligence Multiagent Systems The SOTA algorithms for addressing QDec-POMDP issues, QDec-FP and QDec-FPS, are unable to effectively tackle problems that involve different types of sensing agents. We propose a new algorithm that addresses this issue by requiring agents to adopt the same plan if one agent is unable to take a sensing action but the other can. Our algorithm performs significantly better than both QDec-FP and QDec-FPS in these types of situations. |
| title | Optimizing Agent Collaboration through Heuristic Multi-Agent Planning |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2301.01246 |