One of the key issues in reasoning with multiple interacting intelligent agents is how to model and code the decision making process of the agents. In Artificial Intelligence (AI...
We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents, and present a framework that uses this paradigm t...
In this paper, a model is proposed for multi-agent probabilistic reasoning in a distributed environment. Unlike other methods, this model is capable of processing input in a truly...
This paper presents a self-organizing cognitive architecture, known as TD-FALCON, that learns to function through its interaction with the environment. TD-FALCON learns the value ...
This paper presents approaches for building, managing, and evaluating consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent h...