Fuzzy Systems Are Universal Approximators for Random Dependencies:A Simplified Proof -- How Quantum Computing Can Help With (Continuous) Optimization -- How Neural Networks (NN) Can (Hopefully) Learn Faster by Taking Into Account Known Constraints -- Fuzzy Primeness in Quantales -- Collective Defense and Possible Relaxations in Weighted Abstract Argumentation Problems -- Modeling and Specification of Nondeterministic Fuzzy Discrete-Event Systems. .

This book presents extended versions of selected papers from the annual International Workshops on Constraint Programming and Decision Making from 2016 to 2018. The papers address all stages of decision-making under constraints: (1) precisely formulating the problem of multi-criteria decision-making; (2) determining when the corresponding decision problem is algorithmically solvable; (3) finding the corresponding algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent in the corresponding decision-making problems. In many application areas, it is necessary to make effective decisions under constraints, and there are several area-specific techniques for such decision problems. However, because they are area-specific, it is not easy to apply these techniques in other application areas. As such, the annual International Workshops on Constraint Programming and Decision Making focus on cross-fertilization between different areas, attracting researchers and practitioners from around the globe. The book includes numerous papers describing applications, in particular, applications to engineering, such as control of unmanned aerial vehicles, and vehicle protection against improvised explosion devices.