Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Scientific approaches and techniques for negotiation. A game theoretic and artificial intelligence perspective
Using temporal-difference learning for multi-agent bargaining
Electronic Commerce Research and Applications
An agent-based system for bilateral contracts of energy
Expert Systems with Applications: An International Journal
Applying SAQ-Learning Algorithm for Trading Agents in Bilateral Bargaining
UKSIM '12 Proceedings of the 2012 UKSim 14th International Conference on Modelling and Simulation
Using Gaussian processes to optimise concession in complex negotiations against unknown opponents
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Automated Bilateral Multiple-issue Negotiation with No Information About Opponent
HICSS '13 Proceedings of the 2013 46th Hawaii International Conference on System Sciences
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In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator-load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market.