We previously designed Partial Order Conflict Driven Clause Learning (PO-CDCL), a variation of the satisfiability solving CDCL algorithm with a partial order on decision levels, and showed that it can speed up the solving on problems with a high independence between decision levels. In this paper, we more thoroughly analyze the reasons of the efficiency of PO-CDCL. Of particular importance is t...

We present the solver RestartSATwhich includes a novel technique to reduce the cost to perform a restart in CDCL SAT solvers. This technique, called ReusedTrail, exploits the observation that CDCL solvers often reassign the same variables to the same truth values after a restart. It computes a partial restart level for which it is guaranteed that all variables below this level will be reassigne...

The OT error-driven learner is known to admit guarantees of efficiency, stochastic tolerance and noise robustness which hold independently of any substantive assumptions on the constraints. This paper shows that the HG learner instead does not admit such constraint-independent guarantees. The HG theory of error-driven learning thus needs to be substantially restricted to specific constraint sets.

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In searching for hidden objects, infants younger than 12 months frequently commit “A-not-B errors,” in which they successfully search for an object in one location (A) and then fail to search for it when it is conspicuously hidden in a new location (B). Why do they fail to make the switch and perseverate at the first location? Although these errors have often been attributed to cognitive and co...

In this paper, we introduce a new set of reinforcement learning (RL) tasks in Minecraft (a flexible 3D world). We then use these tasks to systematically compare and contrast existing deep reinforcement learning (DRL) architectures with our new memory-based DRL architectures. These tasks are designed to emphasize, in a controllable manner, issues that pose challenges for RL methods including par...

Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a frame...