We develop a game semantics for process algebra with two independent, interacting agents. The purpose of the semantics is to make manifest the role of knowledge and information flow in the interactions between agents and to control the information available…

In reasoning about multi-agent systems, it is important to look beyond the realm of propositional logic and to reason about the knowledge of agents within the system, as what they know about the environment will affect how they behave. A…

Semantic background knowledge is crucial for many intelligent applications. A classical way to represent such knowledge is through semantic networks. Wikipedia's hyperlink graph can be considered a primitive semantic network, since the links it contains usually correspond to semantic relationships…

Reinforcement Learning is a branch of Artificial Intelligence addressing the problem of single-agent autonomous sequential decision making. It proposes computational models which do not rely on the complete knowledge of the dynamics of stochastic environments. Options are a formalism used…

Learning agents that interact with complex environments often cannot predict the exact outcome of their actions due to noisy sensors or incomplete knowledge of the world. Learning the internal representation of such partially observable environments has proven to be a…