200 citations · 450 across the 51 of their papers we have counts for
23 papers · 1 filter
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…
Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search
Jiaoyang Li, Daniel Harabor, Peter J. Stuckey +1
Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents. In this work, we show that one of…
A Scalable Two Stage Approach to Computing Optimal Decision Sets
Alexey Ignatiev, Edward Lam, Peter J. Stuckey +1
Machine learning (ML) is ubiquitous in modern life. Since it is being deployed in technologies that affect our privacy and safety, it is often crucial to understand the reasoning b…
Symmetry Breaking for k-Robust Multi-Agent Path Finding
Zhe Chen, Daniel Harabor, Jiaoyang Li +1
During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events. To address such situations recent work describes k-Robust Conflict-BasedSearch (k-CBS):…
Optimal Decision Lists using SAT
Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic +1
Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is…
Computing Optimal Decision Sets with SAT
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey +1
As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use…