Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper in 1974 [1]. Since then, there have been many fundamental a...
This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear un...
W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sa...
Abstract--In the Relational Reinforcement learning framework, we propose an algorithm that learns an action model allowing to predict the resulting state of each action in any give...
Scientific literature with rich metadata can be represented as a labeled directed graph. This graph representation enables a number of scientific tasks such as ad hoc retrieval o...
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...