Journal article
Probably approximately correct Nash equilibrium learning
- Abstract:
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We consider a multi-agent noncooperative game with agents’ objective functions being affected by uncertainty. Following a data driven paradigm, we represent uncertainty by means of scenarios and seek a robust Nash equilibrium solution. We treat the Nash equilibrium computation problem within the realm of probably approximately correct (PAC) learning. Building upon recent developments in scenario-based optimization, we accompany the computed Nash equilibrium with a priori and a posteriori prob...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, 793.8KB, Terms of use)
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- Publisher copy:
- 10.1109/TAC.2020.3030754
Authors
Funding
Bibliographic Details
- Publisher:
- Institute of Electrical and Electronics Engineers
- Journal:
- IEEE Transactions on Automatic Control More from this journal
- Volume:
- 66
- Issue:
- 9
- Pages:
- 4238 - 4245
- Publication date:
- 2020-10-13
- Acceptance date:
- 2020-10-04
- DOI:
- EISSN:
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1558-2523
- ISSN:
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0018-9286
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1136255
- Local pid:
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pubs:1136255
- Deposit date:
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2020-10-06
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2020
- Rights statement:
- © 2020 IEEE.
- Notes:
- This is the accepted manuscript version of the article. The final version is available from IEEE at: https://doi.org/10.1109/TAC.2020.3030754
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