Search Results for author: Peter J. Stuckey

Found 19 papers, 5 papers with code

Tracking Progress in Multi-Agent Path Finding

no code implementations15 May 2023 Bojie Shen, Zhe Chen, Muhammad Aamir Cheema, Daniel D. Harabor, Peter J. Stuckey

Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications.

Multi-Agent Path Finding

Eliminating The Impossible, Whatever Remains Must Be True

1 code implementation20 Jun 2022 Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey, Nina Narodytska, Joao Marques-Silva

It also means the "why not" explanations may be suspect as the counterexamples they rely on may not be meaningful.

Explainable artificial intelligence

Planning with Learned Binarized Neural Networks Benchmarks for MaxSAT Evaluation 2021

no code implementations2 Aug 2021 Buser Say, Scott Sanner, Jo Devriendt, Jakob Nordström, Peter J. Stuckey

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 general MaxSAT encoding for this problem, and describes the four domains, namely: Navigation, Inventory Control, System Administrator and Cellda, that are submitted as benchmarks for MaxSAT Evaluation 2021.

Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search

no code implementations12 Mar 2021 Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Sven Koenig

Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks us to plan collision-free paths for a team of cooperative agents.

Multi-Agent Path Finding

Symmetry Breaking for k-Robust Multi-Agent Path Finding

no code implementations17 Feb 2021 Zhe Chen, Daniel Harabor, Jiaoyang Li, Peter J. Stuckey

During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events.

Multi-Agent Path Finding

Divide and Learn: A Divide and Conquer Approach for Predict+Optimize

no code implementations4 Dec 2020 Ali Ugur Guler, Emir Demirovic, Jeffrey Chan, James Bailey, Christopher Leckie, Peter J. Stuckey

We compare our approach withother approaches to the predict+optimize problem and showwe can successfully tackle some hard combinatorial problemsbetter than other predict+optimize methods.

Combinatorial Optimization

Optimal Decision Lists using SAT

no code implementations19 Oct 2020 Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic, Peter J. Stuckey

Decision lists are one of the most easily explainable machine learning models.

BIG-bench Machine Learning

Optimal Decision Trees for Nonlinear Metrics

1 code implementation15 Sep 2020 Emir Demirović, Peter J. Stuckey

Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes-Mallows index, are often used to evaluate the performance of machine learning models, in particular, when facing imbalanced datasets that contain more samples of one class than the other.

Computing Optimal Decision Sets with SAT

no code implementations29 Jul 2020 Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey, Pierre Le Bodic

Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large.

BIG-bench Machine Learning

You can do RLAs for IRV

no code implementations1 Apr 2020 Michelle Blom, Andrew Conway, Dan King, Laurent Sandrolini, Philip B. Stark, Peter J. Stuckey, Vanessa Teague

The City and County of San Francisco, CA, has used Instant Runoff Voting (IRV) for some elections since 2004.

Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems

1 code implementation22 Nov 2019 Jaynta Mandi, Emir Demirović, Peter J. Stuckey, Tias Guns

Recently, Smart Predict and Optimize (SPO) has been proposed for problems with a linear objective function over the predictions, more specifically linear programming problems.

Combinatorial Optimization Scheduling

Solution Dominance over Constraint Satisfaction Problems

no code implementations21 Dec 2018 Tias Guns, Peter J. Stuckey, Guido Tack

We define Constraint Dominance Problems (CDPs) as CSPs with a dominance relation, that is, a preorder over the solutions of the CSP.

Unsatisfiable Cores and Lower Bounding for Constraint Programming

no code implementations25 Aug 2015 Nicholas Downing, Thibaut Feydy, Peter J. Stuckey

We adapt the original MAXSAT unsatisfiable core solving approach to be usable for constraint programming and define a number of extensions.

Efficient Computation of Exact IRV Margins

no code implementations20 Aug 2015 Michelle Blom, Peter J. Stuckey, Vanessa J. Teague, Ron Tidhar

The margin of victory is easy to compute for many election schemes but difficult for Instant Runoff Voting (IRV).

Structure Based Extended Resolution for Constraint Programming

no code implementations19 Jun 2013 Geoffrey Chu, Peter J. Stuckey

Nogood learning solvers can be seen as resolution proof systems.

Unsatisfiable Cores for Constraint Programming

no code implementations8 May 2013 Nicholas Downing, Thibaut Feydy, Peter J. Stuckey

Since Lazy Clause Generation (LCG) solvers can also return unsatisfiable cores we can adapt the MAXSAT unsatisfiable core approach to CP.

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