The constraint paradigm provides powerful concepts to represent and solve different kinds of planning problems, e. g. factory scheduling. Factory scheduling is a demanding optimiz...
The problem of reinforcement learning in large factored Markov decision processes is explored. The Q-value of a state-action pair is approximated by the free energy of a product o...
This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [2, 23]. Resource allocation decisions in heterogeneous parallel a...
Vladimir Shestak, Howard Jay Siegel, Anthony A. Ma...
— Most research in machine learning focuses on scenarios in which a learner faces a single learning task, independently of other learning tasks or prior knowledge. In reality, ho...
: The use of technology to assist human decision making is not a novel idea. However, we argue that there is a need for a unified model which synthesizes and extends existing model...
Lars Niklasson, Maria Riveiro, Fredrik Johansson, ...