33 citations · 76 across the 15 of their papers we have counts for
7 papers · 1 filter
Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus
Yudong Xu, Elias B. Khalil, Scott Sanner
The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms. The ARC's focus on broad generalization and few-shot…
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs
Siow Meng Low, Akshat Kumar, Scott Sanner
Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and…
Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021
Buser Say, Scott Sanner, Jo Devriendt +2
This document provides a brief introduction to learned automated planning problem where the state transition function is in the form of a binarized neural network (BNN), presents a…
Reward Potentials for Planning with Learned Neural Network Transition Models
Buser Say, Scott Sanner, Sylvie Thiébaux
Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for br…
Scalable Planning with Deep Neural Network Learned Transition Models
Ga Wu, Buser Say, Scott Sanner
In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and…
Approximate Linear Programming for First-order MDPs
Scott Sanner, Craig Boutilier
We introduce a new approximate solution technique for first-order Markov decision processes (FOMDPs). Representing the value function linearly w.r.t. a set of first-order basis fun…