A Deep Reinforcement Learning Approach to Concurrent Bilateral Negotiation
- Submitting institution
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Royal Holloway and Bedford New College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 38685842
- Type
- E - Conference contribution
- DOI
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10.24963/ijcai.2020/42
- Title of conference / published proceedings
- 29th International Joint Conference on Artificial Intelligence
- First page
- 297
- Volume
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- Issue
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- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2020
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
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- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first negotiation model that allows an agent to learn how to strategically negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The work was presented at IJCAI'20, with 12.6% acceptance rate. After acceptance at IJCAI'20 the work was invited to the fast-track reviewing process of the Journal of Autonomous Agents and Multi-Agent Systems.
- Author contribution statement
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- Non-English
- No
- English abstract
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