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Identifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement Learning

2019Book part Open access
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dc.contributor.authorSilva, Francisco
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.date.accessioned2021-02-03T16:45:13Z
dc.date.available2021-02-03T16:45:13Z
dc.date.issued2019
dc.description.abstractThis paper proposes an adaptation of the Q-Learning reinforcement learning algorithm, for the identification of the most probable scenario that a player may face, under different contexts, when negotiating bilateral contracts. For that purpose, the proposed methodology is integrated in a Decision Support System that is capable to generate several different scenarios for each negotiation context. With this complement, the tool can also identify the most probable scenario for the identified negotiation context. A realistic case study is conducted, based on real contracts data, which confirms the learning capabilities of the proposed methodology. It is possible to identify the most probable scenario for each context over the learned period. Nonetheless, the identified scenario might not always be the real negotiation scenario, given the variable nature of such negotiations. However, this work greatly reduces the frequency of such unexpected scenarios, contributing to a greater success of the supported player over time.pt_PT
dc.description.sponsorshipThis work has received funding from National Funds through FCT (Fundaçao da Ciencia e Tecnologia) under the project SPET – 29165, call SAICT 2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-16181-1_53pt_PT
dc.identifier.isbn978-3-030-16180-4
dc.identifier.urihttp://hdl.handle.net/10400.22/16861
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-16181-1_53pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectAutomated Negotiationpt_PT
dc.subjectBilateral Contractspt_PT
dc.subjectDecision support systemspt_PT
dc.subjectElectricity Marketspt_PT
dc.subjectReinforcement learning algorithmpt_PT
dc.titleIdentifying Most Probable Negotiation Scenario in Bilateral Contracts with Reinforcement Learningpt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage571pt_PT
oaire.citation.startPage556pt_PT
oaire.citation.titleIntelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)pt_PT
oaire.citation.volume930pt_PT
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typebookPartpt_PT
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relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery8d58ddc0-1023-47c0-a005-129d412ce98d

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