
TLib: A Flexible C++ Tensor Framework for Numerical Tensor Calculus
Numerical tensor calculus comprise basic tensor operations such as the e...
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Neural Guided Constraint Logic Programming for Program Synthesis
Synthesizing programs using example input/outputs is a classic problem i...
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On the Robustness of Domain Constraints
Machine learning is vulnerable to adversarial examplesinputs designed t...
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High order cumulant tensors and algorithm for their calculation
In this paper, we introduce a novel algorithm for calculating arbitrary ...
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Possibilistic Constraint Satisfaction Problems or "How to handle soft constraints?"
Many AI synthesis problems such as planning or scheduling may be modeliz...
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Constrained episodic reinforcement learning in concaveconvex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specified in the model. Determining the right constraints is a hard and laborsome task. We propose an approach to automate this process using artificial intelligence and machine learning principles. So far there has been only little work on learning constraints within the operations research community. We focus on personnel rostering and scheduling problems in which there are often past schedules available and show that it is possible to automatically learn constraints from such examples. To realize this, we adapted some techniques from the constraint programming community and we have extended them in order to cope with multidimensional examples. The method uses a tensor representation of the example, which helps in capturing the dimensionality as well as the structure of the example, and applies tensor operations to find the constraints that are satisfied by the example. To evaluate the proposed algorithm, we used constraints from the Nurse Rostering Competition and generated solutions that satisfy these constraints; these solutions were then used as examples to learn constraints. Experiments demonstrate that the proposed algorithm is capable of producing human readable constraints that capture the underlying characteristics of the examples.
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