Neural networks primer, part III
AI Expert
Temporal difference learning and TD-Gammon
Communications of the ACM
Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Making Hard Decisions with Decisiontools Suite
Making Hard Decisions with Decisiontools Suite
A Multiagent Framework for Automated Online Bargaining
IEEE Intelligent Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Dialogues for Negotiation: Agent Varieties and Dialogue Sequences
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
On automated discovery of models using genetic programming in game-theoretic contexts
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Computational Model for Online Agent Negotiation
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 1 - Volume 1
An Experience Based Evolutionary Negotiation Model
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
A hybrid negotiation strategy mechanism in an automated negotiation system
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Learning opponents' preferences in multi-object automated negotiation
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
A machine-learning approach to automated negotiation and prospects for electronic commerce
Journal of Management Information Systems - Special issue: Information technology and its organizational impact
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An intelligent negotiator agent design for bilateral contracts of electrical energy
Expert Systems with Applications: An International Journal
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This research treats a bargaining process as a Markov decision process, in which a bargaining agent's goal is to learn the optimal policy that maximizes the total rewards it receives over the process. Reinforcement learning is an effective method for agents to learn how to determine actions for any time steps in a Markov decision process. Temporal-difference (TD) learning is a fundamental method for solving the reinforcement learning problem, and it can tackle the temporal credit assignment problem. This research designs agents that apply TD-based reinforcement learning to deal with online bilateral bargaining with incomplete information. This research further evaluates the agents' bargaining performance in terms of the average payoff and settlement rate. The results show that agents using TD-based reinforcement learning are able to achieve good bargaining performance. This learning approach is sufficiently robust and convenient, hence it is suitable for online automated bargaining in electronic commerce.