Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize

This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple conditions, we show how a corresponding learning algorithm can be constructed directly and methodically from the recursive algorithm. Our framework applies also to iterative algorithms by viewing them as a degenerate form of recursion. Extensive experimentation shows better performance for our proposal over classical and state-of-the-art approaches.

Augmenting Stream Constraint Programming with Eventuality Conditions

Stream constraint programming is a recent addition to the family of constraint programming frameworks, where variable domains are sets of infinite streams over finite alphabets. Previous works showed promising results for its applicability to real-world planning and control problems. In this paper, motivated by the modelling of planning applications, we improve the expressiveness of the framework by introducing 1) the "until" constraint, a new construct that is adapted from Linear Temporal Logic and 2) the @ operator on streams, a syntactic sugar for which we provide a more efficient solving algorithm over simple desugaring. For both constructs, we propose corresponding novel solving algorithms and prove their correctness. We present competitive experimental results on the Missionaries and Cannibals logic puzzle and a standard path planning application on the grid, by comparing with Apt and Brand's method for verifying eventuality conditions using a CP approach.