Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Bargaining with incomplete information
Annals of Mathematics and Artificial Intelligence
An evolutionary learning approach for adaptive negotiation agents: Research Articles
International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
Guest Editorial: Agent-based Grid computing
Applied Intelligence
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
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent Systems
Alternating-offers bargaining with one-sided uncertain deadlines: an efficient algorithm
Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Concurrent negotiation and coordination for grid resource coallocation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Grid resource negotiation: survey and new directions
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Resource allocation in decentralised computational systems: an evolutionary market-based approach
Autonomous Agents and Multi-Agent Systems
Towards complex negotiation for cloud economy
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Bilateral negotiation decisions with uncertain dynamic outside options
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On the Scalability of Real-Coded Bayesian Optimization Algorithm
IEEE Transactions on Evolutionary Computation
Agents that react to changing market situations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Equilibria, prudent Compromises,and the "Waiting" game
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Grid Commerce, Market-Driven G-Negotiation, and Grid Resource Management
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Services Computing
| Hi-index | 0.00 |
In agent-mediated negotiation systems, the majority of the research focused on finding negotiation strategies for optimizing price only. However, in negotiation systems with time constraints (e.g., resource negotiations for Grid and Cloud computing), it is crucial to optimize either or both price and negotiation speed based on preferences of participants for improving efficiency and increasing utilization. To this end, this work presents the design and implementation of negotiation agents that can optimize both price and negotiation speed (for the given preference settings of these parameters) under a negotiation setting of complete information. Then, to support negotiations with incomplete information, this work deals with the problem of finding effective negotiation strategies of agents by using coevolutionary learning, which results in optimal negotiation outcomes. In the coevolutionary learning method used here, two types of estimation of distribution algorithms (EDAs) such as conventional EDAs (S-EDAs) and novel improved dynamic diversity controlling EDAs (ID2C-EDAs) were adopted for comparative studies. A series of experiments were conducted to evaluate the performance for coevolving effective negotiation strategies using the EDAs. In the experiments, each agent adopts three representative preference criteria: (1) placing more emphasis on optimizing more price, (2) placing equal emphasis on optimizing exact price and speed and (3) placing more emphasis on optimizing more speed. Experimental results demonstrate the effectiveness of the coevolutionary learning adopting ID2C-EDAs because it generally coevolved effective converged negotiation strategies (close to the optimum) while the coevolutionary learning adopting S-EDAs often failed to coevolve such strategies within a reasonable number of generations.