no code implementations • 12 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
1 code implementation • 22 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.
no code implementations • 11 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.
no code implementations • 17 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.
1 code implementation • 7 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
no code implementations • 28 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.
no code implementations • 15 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.
1 code implementation • 20 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 Explainable Artificial Intelligence (XAI)
no code implementations • 2 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.
no code implementations • 12 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.
no code implementations • 17 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.
1 code implementation • 3 Feb 2021 • Alexey Ignatiev, Edward Lam, Peter J. Stuckey, Joao Marques-Silva
Machine learning (ML) is ubiquitous in modern life.
no code implementations • 4 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.
no code implementations • 19 Oct 2020 • Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic, Peter J. Stuckey
Decision lists are one of the most easily explainable machine learning models.
1 code implementation • 15 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.
no code implementations • 29 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.
1 code implementation • 24 Jul 2020 • Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
no code implementations • 1 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.
1 code implementation • 22 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.
no code implementations • 21 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.
no code implementations • 15 Dec 2018 • Hang Ma, Daniel Harabor, Peter J. Stuckey, Jiaoyang Li, Sven Koenig
We study prioritized planning for Multi-Agent Path Finding (MAPF).
no code implementations • 25 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.
no code implementations • 20 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).
no code implementations • 19 Jun 2013 • Geoffrey Chu, Peter J. Stuckey
Nogood learning solvers can be seen as resolution proof systems.
no code implementations • 8 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.