Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. Both genetic algorithms (GAs) and te...
We describe a novel integration of Planning with Probabilistic State Estimation and Execution resulting in a unified representational and computational framework based on declarat...
Conor McGann, Frederic Py, Kanna Rajan, John Ryan,...
The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the outp...
Abstract-- Learning by imitation and learning from demonstration have received considerable attention in robotics. However, very little research has been in the direction of provid...
This paper presents a task allocation scheme via selforganizing swarm coalitions for distributed mobile sensor network coverage. Our approach uses the concepts of ant behavior to ...
Kian Hsiang Low, Wee Kheng Leow, Marcelo H. Ang Jr...