Negotiation and cooperation in multi-agent environments
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Modelling social action for AI agents
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Reaching agreements through argumentation: a logical model and implementation
Artificial Intelligence
Emergent cooperative goal-satisfaction in large-scale automated-agent systems
Artificial Intelligence
Soft computing techniques for the design of mobile robot behaviors
Information Sciences: an International Journal - Special issue on frontiers in evolutionary algorithms
A behavioral model for linguistic uncertainty
Information Sciences—Informatics and Computer Science: An International Journal - Special issue computing with words
Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm
Information Sciences: an International Journal
A new generalized particle approach to parallel bandwidth allocation
Computer Communications
Information Sciences: an International Journal
Information Sciences: an International Journal
Methods for task allocation via agent coalition formation
Artificial Intelligence
Information Sciences: an International Journal
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
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Social Networks (SN) is an increasingly popular topic in artificial intelligence research. One of the key directions is to model and study the behaviors of social agents. In this paper, we propose a new computational model which can serve as a powerful tool for the analysis of SN. Specifically, we add to the traditional sociometric methods a novel analytical method in order to deal with social behaviors more effectively, and then present a new bio-inspired model, the coordination generalized molecule model (CGMM). The proposed analytical method for social behaviors and CGMM are combined to give an algorithm that can be used to solve complex problems in SN. Traditionally, SN models were mainly descriptive and were built at a very coarse level, typically with only a few global parameters, and turned out to be not sufficiently useful for analyzing social behaviors. In this work, we explore bio-inspired analytical models for analyzing social behaviors of intelligent agents. Our objective is to propose an effective and practical method to model intelligent systems and their behaviors in an open and complex unpredictable world.