A context-aware approach to automated negotiation using reinforcement learning |
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Affiliation: | 1. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, 274-8510 Funabashi, Japan;2. Department of Applied Mathematics and Computational Sciences, E.T.S.I. Caminos, Canales y Puertos, University of Cantabria, Avda. de los Castros, s/n, 39005 Santander, Spain;3. School of Civil Engineering, Universidad de Cantabria, Avda. de los Castros 44, E-39005 Santander, Spain;4. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor SI-2000, Slovenia;5. R&D EgiCAD, School of Civil Engineering, Universidad de Cantabria, Avda. de los Castros 44, 39005 Santander, Spain;1. Civil Engineering, National Taiwan University, Taiwan;2. Industrial Engineering and Engineering Management, National Tsing Hua University, Taiwan;1. School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;2. Department of Construction Management, Tsinghua University, Beijing 100084, China |
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Abstract: | Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent’s preferences, bargaining strategies and resulting benefits, given the possible negotiation outcomes. Despite the relevance of the context, the existing literature on automated negotiation is scarce about how to account for it in learning and adapting negotiation strategies. In this paper, a novel contextual representation of the negotiation setting is proposed, where an agent resorts to private and public data to negotiate using an individual perception of its necessity and risk. A context-aware negotiation agent that learns through Self-Play and Reinforcement Learning (RL) how to use key contextual information to gain a competitive edge over its opponents is discussed in two levels of temporal abstraction. Learning to negotiate in an Eco-Industrial Park (EIP) is presented as a case study. In the Peer-to-Peer (P2P) market of an EIP, two instances of context-aware agents, in the roles of a buyer and a seller, are set to bilaterally negotiate exchanges of electrical energy surpluses over a discrete timeline to demonstrate that they can profit from learning to choose a negotiation strategy while selfishly accounting for contextual information under different circumstances in a data-driven way. Furthermore, several negotiation episodes are conducted in the proposed EIP between a context-aware agent and other types of agents proposed in the existing literature. Results obtained highlight that context-aware agents do not only reap selfishly higher benefits, but also promote social welfare as they resort to contextual information while learning to negotiate. |
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Keywords: | Agent intelligence Automated negotiation Context-aware agents Peer-to-peer markets Reinforcement learning |
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