In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
This paper explores hybrid agents that use a variety of techniques to improve their performance in an environment over time. We considered, specifically, geneticlearning-parentin...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Optimization of performance in collective systems often requires altruism. The emergence and stabilization of altruistic behaviors are dicult to achieve because the agents incur ...
This paper describes an investigation into the refinement of context-based human behavior models through the use of experiential learning. Specifically, a tactical agent was endow...