Many real-life optimization problems frequently contain one or more constraints or objectives
for which there are no explicit formulae. If however data on feasible and/or infeasible states …

With the aging of electricity transmission and distribution infrastructures and increasing
intensity of extreme weather events, the aggravated vulnerability of electric distribution …

We present strong mixed-integer programming (MIP) formulations for high-dimensional
piecewise linear functions that correspond to trained neural networks. These formulations …

Value-function-based methods have long played an important role in reinforcement
learning. However, finding the best next action given a value function of arbitrary complexity …

Power systems around the world are experiencing an energy revolution that substitutes
fossil fuels with renewable energy. Such a transition poses two significant challenges: highly …

We present the first approach capable of learning domain-independent planning heuristics
entirely from scratch. The heuristics we learn map the hypergraph representation of the …

In robotic domains, learning and planning are complicated by continuous state spaces,
continuous action spaces, and long task horizons. In this work, we address these challenges …

Stochastic Programming is a powerful modeling framework for decision-making under
uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely …

In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …

The thermal capacity of buildings enables heating, ventilating, and air-conditioning (HVAC)
systems to be exploited as demand response (DR) resources. Optimal DR of HVAC units is …