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...
This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. Th...
Automated addition of fault-tolerance to existing programs is highly desirable, as it allows the designer to focus on the system behavior in the absence of faults and leave the fa...
The Constraint-Based Agent (CBA) framework is a set of tools for designing, simulating, building, verifying, optimizing, learning and debugging controllers for agents embedded in a...
Imitation represents a powerful approach for programming and autonomous learning in robot and computer systems. An important aspect of imitation is the mapping of observations to ...