We address two open theoretical questions in Policy Gradient Reinforcement Learning. The first concerns the efficacy of using function approximation to represent the state action ...
We propose an extension of rewriting techniques to derive inclusion relations a ⊆ b between terms built from monotonic operators. Instead of using only a rewriting relation ⊆ ...
In this paper we give a finer separation of several known paging algorithms. This is accomplished using a new technique that we call relative interval analysis. This technique com...
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machin...
In a series of publications, we have proposed a foundational system of ontological categories which has been used to evaluate and improve the quality of conceptual modeling languag...