Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Communications of the ACM
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
On Constraint-Based Reasoning in e-Negotiation Agents
Agent-Mediated Electronic Commerce III, Current Issues in Agent-Based Electronic Commerce Systems (includes revised papers from AMEC 2000 Workshop)
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Data & Knowledge Engineering - Special issue: The language/action perspective
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
A Negotiation Meta Strategy Combining Trade-off and Concession Moves
Autonomous Agents and Multi-Agent Systems
Intelligent agents for e-marketplace: negotiation with issue trade-offs by fuzzy inference systems
Decision Support Systems
Artificial Intelligence
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent Systems
A Bayesian classifier for learning opponents' preferences in multi-object automated negotiation
Electronic Commerce Research and Applications
Predicting opponent's moves in electronic negotiations using neural networks
Expert Systems with Applications: An International Journal
On-demand e-supply chain integration: A multi-agent constraint-based approach
Expert Systems with Applications: An International Journal
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The agent-based negotiation process for B2C e-commerce
Expert Systems with Applications: An International Journal
Multistage Fuzzy Decision Making in Bilateral Negotiation with Finite Termination Times
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Benefits of learning in negotiation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent's preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent's preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent's preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent's intention. Experimental results showed that our learning approach can estimate the opponent's preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility.