Publications

The first objective of TUPLES is to advance the scientific state of the art in the field of Trustworthy AI and its application in the design and management of safe, robust, scalable and explainable Planning & Scheduling decision support systems and tools.The original contributions produced by the TUPLES consortium partners, as part of the project, will be linked below, as soon as they are published.

Publications

Simplifying Step-wise Explanation Sequences

Authors: Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, Tias Guns
Link

Proceedings of the 29th International Conference on Principles and Practice of Constraint Programming (CP), 2023

Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning

Authors: Mattia Silvestri, Senne Berden, Jayanta Mandi, Ali Mahmutogullari, Maxime Mulamba, Allegra De Filippo, Tias Guns, Michele Lombardi
Link

Differentiable Almost Everything workshop at the International Conference on Machine Learning (ICML), 2023

Detecting Evasion Attacks in Deployed Tree Ensembles

Authors: Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis
Link

Machine Learning and Knowledge Discovery in Databases (ECML PKDD) Springer Nature Switzerland, 2023

A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer

Authors: Maaike Van Roy, Peter Robberechts, Wen Chi Yang, Luc De Raedt, Jesse Davis
Link

Journal Of Artificial Intelligence Research, Vol. 77, 2023

Formal Explanations of Neural Network Policies for Planning

Authors: Renee Selvey, Alban Grastien, Sylvie Thiébaux
Link

Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2023

Computational Asymmetries in Robust Classification

Authors: Samuele Marro, Michele Lombardi
Link

40th International Conference on Machine Learning (ICML) 2023

Blossom: an Anytime Algorithm for Computing Optimal Decision Trees

Authors: Emir Demirović, Emmanuel Hebrard, Louis Jean
Link

Proceedings of the 40th International Conference on Machine Learning (ICML) 2023

Assessing the Transparency and Explainability of AI Algorithms in Planning and Scheduling Tools: A Review of the Literature

Authors: Sofia Morandini, Federico Fraboni, Enzo Balatti, Aranka Hackmann, Hannah Brendel, Gabriele Puzzo, Lucia Volpi, Davide Giusino, Marco De Angelis, Luca Pietrantoni
Link

AHFE (2023) International Conference

Neural Policy Safety Verification via Predicate Abstraction: CEGAR

Authors: Marcel Vinzent, Siddhant Sharma, Jörg Hoffmann
Link

Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) 2023

Action Policy Testing with Heuristic-Based Bias Functions

Authors: Xandra Schuler, Jan Eisenhut, Daniel Höller, Daniel Fišer and Jörg Hoffmann
Link

Proceedings of the ICAPS Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS) 2023

Automatic Metamorphic Test Oracles for Action-Policy Testing

Authors: Jan Eisenhut, Álvaro Torralba, Maria Christakis, Jörg Hoffmann
Link

Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS), 2023

Policy-Specific Abstraction Predicate Selection in Neural Policy Safety Verification

Authors: Marcel Vinzent, Min Wu, Haoze Wu, Jörg Hoffmann
Link

Proceedings of the ICAPS Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS), 2023

Action Policy Explanations in Oversubscription Planning

Authors: Aleena Siji, Rebecca Eifler, Daniel Fišer, Jörg Hoffmann
Link

Proceedings of the ICAPS Workshop on Human-Aware and Explainable Planning (HAXP) 2023

Fast and robust resource-constrained scheduling with graph Neural Networks

Authors: Florent Teichteil-Koenigsbuch, Guillaume Poveda, Guillermo Gonzales de Garibay Barba, Tim Luchterhand, Sylvie Thiébaux
Link

Proceedings of the 33rd International Conference on Automated Planning and Scheduling (ICAPS) 2023

Keynotes & Seminars

Reasoning about Tree Ensembles

Jesse Davis is a keynote speaker at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023) in Arras (France), September 2023.
Link

Perception-based solving with CPMpy

By Tias Guns at the ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming.
Link

Welcome and Intro to Constraint Programming

By Tias Guns at the ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming
Link

Rambling Away from Decision-Focused Learning

By Michele Lombardi at the ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming
Link

Constraint Acquisition: Learning Constraint Models from Data

Constraint Acquisition: Learning Constraint Models from Data by Dimos Tsouros at the ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming.
Link

Optimisation in Machine Learning

Invited talk on “Optimisation in Machine Learning” by Tias Guns at VAIA, June 2023.
Link

Measuring Passing Creativity in Soccer

Invited talk on “Measuring Passing Creativity in Soccer” by Jesse Davis at the World Congress on Science and Football, Groningen. May 2023.
Link

Learning from user and environment in combinatorial optimisation

Invited talk “Learning from user and environment in combinatorial optimisation”by Tias Guns at IPAM workshop. Feb 2023
Link

Predict and Optimize

Invited seminar by Tias Guns on “Predict and Optimize: combinatorial optimisation with machine-learning based input” at VAIA (Flemish AI Academy). March 2023.
Link

Conferences & Workshop

VeriLearn

The Verifying Learning AI Systems (VeriLearn) workshop at the 26th European Conference on Artificial Intelligence (ECAI23)
Co-organizer: Prof. Jesse Davis
Link

HPlan

ICAPS Workshop on Hierarchical Planning (HPlan)
Co-organizer: Daniel Höller
Link

HAXP 2023

HAXP @ ICAPS 2023. International Workshop on Human-Aware and Explainable Planning (HAXP)
Co-organizer: Rebecca Eifler
Link

SMiLe 2023

Spring workshop on Mining and Learning 2023 (SMiLe 2023) in Sint-Michielgestel, Netherlands.
Co-organizers: Prof. Jesse Davis, Prof. Tias Guns
Link

10th Annual Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA)

In conjunction with ECMLPKDD 2023 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery).
Co-organizer: Prof. Jesse Davis
Link

ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming

KU Leuven University.
Co-organizer: Prof. Tias Guns
Link

Reliable Data-Driven Planning and Scheduling (RDDPS)

Workshop @ ICAPS
Organizers: Jesse Davis, Daniel Höller, Jörg Hoffmann, Michele Lombardi, Sylvie Thiebaux
Link

Publications

Publications

Simplifying Step-wise Explanation Sequences

Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning

Detecting Evasion Attacks in Deployed Tree Ensembles

A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer

Formal Explanations of Neural Network Policies for Planning

Computational Asymmetries in Robust Classification

Blossom: an Anytime Algorithm for Computing Optimal Decision Trees

Assessing the Transparency and Explainability of AI Algorithms in Planning and Scheduling Tools: A Review of the Literature

Neural Policy Safety Verification via Predicate Abstraction: CEGAR

Action Policy Testing with Heuristic-Based Bias Functions

Automatic Metamorphic Test Oracles for Action-Policy Testing

Policy-Specific Abstraction Predicate Selection in Neural Policy Safety Verification

Action Policy Explanations in Oversubscription Planning

Fast and robust resource-constrained scheduling with graph Neural Networks

Keynotes & Seminars

Reasoning about Tree Ensembles

Perception-based solving with CPMpy

Welcome and Intro to Constraint Programming

Rambling Away from Decision-Focused Learning

Constraint Acquisition: Learning Constraint Models from Data

Optimisation in Machine Learning

Measuring Passing Creativity in Soccer

Learning from user and environment in combinatorial optimisation

Predict and Optimize

Conferences & Workshop

VeriLearn

HPlan

HAXP 2023

SMiLe 2023

10th Annual Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA)

ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming

Reliable Data-Driven Planning and Scheduling (RDDPS)

TRUSTWORTHY AI

Keep in touch