Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
This paper defines a new, context-driven programming model for pervasive spaces. Existing models are prone to conflict, as it is hard to predict the outcome of interleaved actions ...
We use a realistic interdomain routing experiment platform to conduct real-time attack and defense exercises for training purposes. Our interdomain routing experiment platform int...
Abstract. We present a coalgebraic semantics for reasoning about information update in multi-agent systems. The novelty is that we have one structure for both states and actions an...
Software systems are becoming more and more complex with a large number of interacting partners often distributed over a network. A common dilemma faced by software engineers in b...