The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it "a priori" because the processed data se...
Abstract. This paper introduces a novel framework for designing multiagent systems, called “Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios” (...
Suranga Hettiarachchi, William M. Spears, Derek Gr...
In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it ...
In Open Multi-Agent Systems (OMAS), deciding with whom to interact is a particularly difficult task for an agent, as repeated interactions with the same agents are scarce, and rep...
This paper presents an e-Learning Web-reachable hypermedia system as the foundation of a course content development toolset. Course content, developed in XML, is stored in native X...