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...
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
We develop a Belief-Desire-Intention (BDI) style agent-oriented programming language with special emphasis on the semantics of goals in the presence of the typical BDI failure han...
The increasing number of software-based attacks has attracted substantial efforts to prevent applications from malicious interference. For example, Trusted Computing (TC) technolo...
Xinwen Zhang, Michael J. Covington, Songqing Chen,...
Complexity of near future and even nowadays applications is exponentially increasing. In order to tackle the design of such complex systems, being able to engineer self-organising ...