Managing multiple agents by automatically adjusting incentives
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913491933200384 |
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| author | Akatsuka, Shunichi Teramoto, Yaemi Courville, Aaron |
| author_facet | Akatsuka, Shunichi Teramoto, Yaemi Courville, Aaron |
| contents | In the coming years, AI agents will be used for making more complex decisions, including in situations involving many different groups of people. One big challenge is that AI agent tends to act in its own interest, unlike humans who often think about what will be the best for everyone in the long run. In this paper, we explore a method to get self-interested agents to work towards goals that benefit society as a whole. We propose a method to add a manager agent to mediate agent interactions by assigning incentives to certain actions. We tested our method with a supply-chain management problem and showed that this framework (1) increases the raw reward by 22.2%, (2) increases the agents' reward by 23.8%, and (3) increases the manager's reward by 20.1%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Managing multiple agents by automatically adjusting incentives Akatsuka, Shunichi Teramoto, Yaemi Courville, Aaron Multiagent Systems Artificial Intelligence Computer Science and Game Theory In the coming years, AI agents will be used for making more complex decisions, including in situations involving many different groups of people. One big challenge is that AI agent tends to act in its own interest, unlike humans who often think about what will be the best for everyone in the long run. In this paper, we explore a method to get self-interested agents to work towards goals that benefit society as a whole. We propose a method to add a manager agent to mediate agent interactions by assigning incentives to certain actions. We tested our method with a supply-chain management problem and showed that this framework (1) increases the raw reward by 22.2%, (2) increases the agents' reward by 23.8%, and (3) increases the manager's reward by 20.1%. |
| title | Managing multiple agents by automatically adjusting incentives |
| topic | Multiagent Systems Artificial Intelligence Computer Science and Game Theory |
| url | https://arxiv.org/abs/2409.02960 |