As robots become a mass consumer product, they will need to learn new skills by interacting with typical human users. Past approaches have adapted reinforcement learning (RL) to a...
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algo...
In this paper, a distributed and adaptive approach for resource discovery in peer-to-peer networks is presented. This approach is based on the mobile agent paradigm and the random...
The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers the ta...
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 ...