Search Results for author: Peter J. Stuckey

Found 25 papers, 7 papers with code

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.

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.

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).

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.

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.

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

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.

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

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.

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

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

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

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

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.

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

Reducing Redundant Work in Jump Point Search

no code implementations28 Jun 2023 Shizhe Zhao, Daniel Harabor, Peter J. Stuckey

JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding.

On Formal Feature Attribution and Its Approximation

1 code implementation7 Jul 2023 Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey

For instance and besides the scalability limitation, the formal approach is unable to tackle the feature attribution problem.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +3

Lifted Sequential Planning with Lazy Constraint Generation Solvers

no code implementations17 Jul 2023 Anubhav Singh, Miquel Ramirez, Nir Lipovetzky, Peter J. Stuckey

This paper studies the possibilities made open by the use of Lazy Clause Generation (LCG) based approaches to Constraint Programming (CP) for tackling sequential classical planning.

valid

The divergence time of protein structures modelled by Markov matrices and its relation to the divergence of sequences

no code implementations11 Aug 2023 Sandun Rajapaksa, Lloyd Allison, Peter J. Stuckey, Maria Garcia de la Banda, Arun S. Konagurthu

Using these inferred models and the relationship between the divergence of sequences and structures, we demonstrate a competitive performance in secondary structure prediction against neural network architectures commonly employed for this task.

Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding

1 code implementation22 Aug 2023 Zhe Chen, Daniel Harabor, Jiaoyang Li, Peter J. Stuckey

To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths.

Multi-Agent Path Finding

Anytime Approximate Formal Feature Attribution

no code implementations12 Dec 2023 Jinqiang Yu, Graham Farr, Alexey Ignatiev, Peter J. Stuckey

A recent alternative is so-called formal feature attribution (FFA), which defines feature importance as the fraction of formal abductive explanations (AXp's) containing the given feature.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

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