Papers
Experiential AI: Between Arts and Explainable AI.
Leonardo, 2024.
D. Hemment, D Murray-Rust, V. Belle, R. Aylett, M. Vidmar, F. Broz.
Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges.
AAMAS 2023, LNAI 14456, 2024.
V. Belle, M. Fisher, A. Russo, E. Komendantskaya & A. Nottle.
Learning Explanatory Logical Rules in Non-linear Domains: A Neuro-Symbolic Approach.
Machine Learning, 2024.
A. Bueff & V. Belle.
Progression with probabilities in the situation calculus: representation and succinctness.
AAMAS, 2024.
D. Liu, & V. Belle.
Knowledge representation and acquisition in the era of large language models: Reflections on learning to reason via PAC-Semantics.
Natural Language Processing Journal, 2023.
I. Mocanu & V. Belle.
Toward Robots That Reason: Logic, Probability & Causal Laws.
Morgan Claypool/Springer, 2023.
V. Belle.
Principled Diverse Counterfactuals in Multilinear Models.
Machine Learning, 2023.
I. Papantonis and V. Belle.
Logic + probabilistic programming + causal laws.
Royal Society Open Science, 2023.
V. Belle.
Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond.
JELIA, 2023.
V. Belle.
Boolean Connectives and Deep Learning: Three Interpretations.
Compendium of Neurosymbolic Artificial Intelligence, IOS Press, 2023.
M. Mendez-Lucero, V. Belle.
Transparency in Sum-Product Network Decompilation.
ECAI, 2023.
I. Papantonis, V. Belle.
Verifying Belief-based Programs via Symbolic Dynamic Programming.
ECAI,2023.
D. Liu, Q. Huang, G. Lakemeyer, V. Belle.
Concerning Measures in a First-order Logic with Actions and Meta-beliefs.
KR, 2023.
D. Liu, Q. Feng, V. Belle, and G. Lakemeyer.
Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures.
KR, 2023.
P. Dilkas and V. Belle.
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models.
AAAI, 2023.
J. Feldstein, D. Phillips, and E. Tsamoura.
Parallel Neurosymbolic Integration with Concordia.
ICML, 2023.
J. Feldstein, M. Jurcius, E. Tsamoura
Deep Inductive Logic Programming meets Reinforcement Learning.
ICLP, 2023.
A. Bueff and V. Belle.
Toward A Logical Theory Of Fairness and Bias.
ICLP, 2023.
V. Belle.
Learnability with PAC Semantics for Multi-agent Beliefs.
ICLP, 2023.
I. Mocanu, B. Juba and V Belle.
Counterfactual Explanations as Plans.
ICLP, 2023.
V. Belle.
Knowledge Representation and Acquisition for Ethical AI: Challenges and Opportunities.
Ethics and Information Technology, 2023.
V. Belle.
Potential Pitfalls with Automatic Sentiment Analysis: The Example of Queerphobic Bias.
Social science computer review, 2023.
E. Ungless, B. Ross, V. Belle.
Ethical AI: Issues and Opportunites.
Royal Society of Edinburgh - Resource Magazine, 2023.
V. Belle.
Learnability with PAC Semantics for Multi-agent Beliefs.
AAMAS, 2023.
I Mocanu, B Juba, V Belle.
A Logic of Only-Believing over Arbitrary Probability Distributions.
AAMAS, 2023.
Q Feng, D Liu, G Lakemeyer, V Belle.
Actions, Continuous Distributions and Meta-Beliefs.
AAMAS, 2023.
V Belle.
Abstracting Noisy Robot Programs.
AAMAS, 2023.
T Hoffman, V Belle.
Logic + Reinforcement Learning + Deep Learning: A Survey.
ICAART, 2023.
A. Bueff, V. Belle.
Model Transparency: why do we care?
ICAART, 2023.
G. Papantonis, V. Belle.
Epistemic planning: Perspectives on the special issue.
Artificial Intelligence, 2023.
V. Belle, T. Bolander, A. Herzig, B. Nebel.
Tractable Probabilistic Models for Ethical AI.
ICCS, 2022.
V. Belle.
Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond.
Frontiers in AI, 2022.
M. Lucero, M-A., Karampatsis, R-M., Gallardo, E., Belle, V.
Breaking CAPTCHA with Capsule Networks.
Neural Networks, 2022.
I. Mocanu, Z. Yang, V. Belle.
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief.
Artificial Intelligence, 2022.
Muise, C., Belle, V., Felli, P., McIlraith, S., Miller, T., Pearce, A., Sonenberg, L.
Analyzing Generalized Planning Under Nondeterminism.
Artificial Intelligence, 2022.
Belle, V.
Logic meets Learning: From Aristotle to Neural Networks.
Neuro-Symbolic Artificial Intelligence — The State of the Art, 2022.
V. Belle.
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks.
AAAI, 2022.
N. Hoernle, R. Karampatsis, V. Belle, K. Gal.
One Down, 699 to Go: or, synthesising compositional desugarings.
OOPSLA, 2021.
Bartha, S., Cheney, J., Belle, V.
Principles and Practice of Explainable Machine Learning.
Frontiers in Big Data, 2021.
V. Belle and I. Papantonis.
Lifted Reasoning Meets Weighted Model Integration
UAI, 2021.
J. Feldstein and V. Belle.
Weighted Model Counting with Conditional Weights for Bayesian Networks
UAI, 2021.
P. Dilkas and V. Belle.
Weighted Model Counting Without Parameter Variables
SAT, 2021.
P. Dilkas and V. Belle.
Learning Implicitly with Noisy Data in Linear Arithmetic.
IJCAI, 2021.
A. Rader, I. Mocanu, V. Belle, and B. Juba.
Closed-Form Results for Prior Constraints in Sum-Product Networks.
Frontiers in artificial intelligence, 2021.
I. Papantonis and V. Belle.
Fairness in Machine Learning with Tractable Models.
Knowledge-Based Systems, 2021.
M. Varley and V. Belle.
Tractable Probabilistic Models for Moral Responsibility and Blame.
Data Mining and Knowledge Discovery, 2021.
L. Hammond and V. Belle.
Probabilistic Tractable Models in Mixed Discrete-Continuous Domains.
Data Intelligence, 2021.
A. Bueff, S. Speichert and V. Belle.
Semiring Programming: A Semantic Framework for Generalized Sum Product Problems.
International Journal of Approximate Reasoning, 2020.
V. Belle and L. De Raedt.
A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains.
ICAPS (Journal Track), 2020.
L. Treszkai and V. Belle.
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains.
SUM, 2020.
V. Belle.
Implicitly Learning to Reason in First-Order Logic (Extended Abstract).
KR, 2020.
V. Belle and B. Juba.
Abstracting Probabilistic Models: Relations, Constraints and Beyond (Extended Abstract).
KR, 2020.
V. Belle.
Generating Random Logic Programs Using Constraint Programming.
CP, 2020.
P. Dilkas and V. Belle.
Logic, Probability and Action: A Situation Calculus Perspective.
SUM, 2020.
V. Belle.
Learning Credal Sum-Product Networks.
AKBC, 2020.
A. Levray and V. Belle.
Abstracting Probabilistic Models: Relations, Constraints and Beyond.
Knowledge-based Systems, 2020.
V. Belle.
Regression and Progression in Stochastic Domains.
Artificial Intelligence, 2020.
V. Belle and H. Levesque.
Logical Interpretations of Autoencoders.
ECAI, 2020.
A. Fuxjaeger and V. Belle.
Polynomial-time Implicit Learnability in SMT.
ECAI, 2020.
I. Mocanu, V. Belle and B. Juba.
A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains.
International Journal of Approximate Reasoning, 2020.
L. Treszkai and V. Belle.
ICAART, 2020.
A. Fuxjaeger and V. Belle.
Fairness in Machine Learning with Tractable Models.
AAAI Workshop: Statistical Relational Artificial Intelligence, 2020.
M. Varley and V. Belle.
Abstracting Probabilistic Models: A Logical Perspective.
AAAI Workshop: Statistical Relational Artificial Intelligence, 2020.
V. Belle.
AAAI Workshop: Statistical Relational Artificial Intelligence, 2020.
V. Belle.
Semiring Programming: A Declarative Framework for Generalized Sum Product Problems.
AAAI Workshop: Statistical Relational Artificial Intelligence, 2020.
V. Belle and L. De Raedt.
AAAI Workshop: Statistical Relational Artificial Intelligence, 2020.
A. Fuxjaeger and V. Belle.
Implicitly Learning to Reason in First-Order Logic.
NeurIPS, 2019.
V. Belle and B. Juba.
The quest for interpretable and responsible artificial intelligence.
The Biochemist, 2019.
V. Belle.
Logical Interpretations of Autoencoders.
NeurIPS Workshop on Knowledge Representation & Reasoning Meets Machine Learning, 2019.
A. Fuxjaeger and V. Belle.
Tractable Probabilistic Models for Moral Responsibility.
NeurIPS Workshop on Knowledge Representation & Reasoning Meets Machine Learning, 2019.
L. Hammond and V. Belle.
NeurIPS Workshop on Knowledge Representation & Reasoning Meets Machine Learning, 2019.
I. Mocanu, V. Belle and B. Juba.
Interventions and Counterfactuals in Tractable Probabilistic Models.
NeurIPS Workshop on Knowledge Representation & Reasoning Meets Machine Learning, 2019.
I. Papantonis and V. Belle.
Implicitly Learning to Reason in First-Order Logic.
Fourth International Workshop on Declarative Learning Based Programming (DeLBP 2019), 2019.
V. Belle and B. Juba.
Learning Probabilistic Logic Programs in Continuous Domains.
ILP, 2019.
S. Speichert and V. Belle.
Best student paper award
Toward Fairness, Morality and Transparency in Artificial Intelligence through Experiential AI.
Leonardo, 2019.
D. Hemment, V. Belle, R. Aylett, D. Murray-Rust, L. Pschetz and F. Broz.
Deep Tractable Probabilistic Models for Moral Responsibility.
Human-Like Computing Third Wave of AI Workshop, 2019.
L. Hammond and V. Belle.
AI Matters, 5(1):25–31, 2019.
D. Hemment, R. Aylett, V. Belle, D. Murray-Rust, E. Luger, J. Hillston, M. Rovatsos and F. Broz.
Learning Symbolic Representations in Mixed Discrete-Continuous Domains.
Human-Like Computing Third Wave of AI Workshop, 2019.
S. Speichert, A. Bueff and V. Belle.
On Plans With Loops and Noise.
AAMAS, 2018.
V. Belle.
Probabilistic Planning by Probabilistic Programming.
AAAI Workshop on Planning and Inference, 2018.
V. Belle.
Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems.
Artificial Intelligence, 262:189–221, 2018.
V. Belle and H. Levesque.
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks.
Workshop on Hybrid Reasoning, Workshop Learning (HRL 2018), KR, 2018.
A. Bueff, S. Speichert and V. Belle.
Efficient Symbolic Integration for Probabilistic Inference.
IJCAI, 2018.
S. Kolb, M. Mladenov, S. Sanner, V. Belle and K. Kersting.
Planning in hybrid relational MDPs.
ICAPS (Journal Track), 2018.
D. Nitti, V. Belle, T. De Laet and L. De Raedt.
Learning Probabilistic Logic Programs in Continuous Domains.
Workshop on Hybrid Reasoning, Workshop Learning (HRL 2018), KR, 2018.
S. Speichert and V. Belle.
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds.
IJCAI, 2017.
V. Belle.
Weighted Model Counting With Function Symbols.
UAI, 2017.
V. Belle.
Open-Universe Weighted Model Counting.
AAAI, 2017.
V. Belle.
Open-Universe Weighted Model Counting: Extended Abstract.
AAAI Workshop: Symbolic Inference and Optimization, 2017.
V. Belle.
Reasoning about Probabilities in Unbounded First-Order Dynamical Domains.
IJCAI, 2017.
V. Belle and G. Lakemeyer.
Solving Probability Problems in Natural Language.
IJCAI, 2017.
A. Dries, A. Kimmig, J. Davis, V. Belle and L. De Raedt.
Solving Probability Problems in Natural Language.
ILP, 2017.
A. Dries, A. Kimmig, J. Davis, V. Belle and L. De Raedt.
The Symbolic Interior Point Method.
AAAI, 2017.
M. Mladenov, V. Belle and K. Kersting.
Planning in hybrid relational MDPs.
Machine Learning:1–28, 2017.
D. Nitti, V. Belle, T. De Laet and L. De Raedt.
A First-Order Logic of Probability and Only Knowing in Unbounded Domains.
AAAI, 2016.
V. Belle, G. Lakemeyer and H. Levesque.
Action-Centric Probabilistic Programming.
StarAI Workshop at IJCAI, 2016.
V. Belle.
Component Caching in Hybrid Domains with Piecewise Polynomial Densities.
AAAI, 2016.
V. Belle, G. Van den Broeck and A. Passerini.
Foundations for Generalized Planning in Unbounded Stochastic Domains.
KR, 2016.
V. Belle and H. Levesque.
Hashing-based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report.
IJCAI, 2016.
V. Belle, G. Van den Broeck and A. Passerini.
Satisfiability and Model Counting in Open Universes.
Beyond NP Workshop, AAAI, 2016.
V. Belle.
Multi-Agent Only Knowing on Planet Kripke.
IJCAI, 2015.
G. Aucher and V. Belle.
Only Knowing Meets Common Knowledge.
IJCAI, 2015.
V. Belle and G. Lakemeyer.
Probabilistic Inference in Hybrid Domains by Weighted Model Integration.
IJCAI, 2015.
V. Belle, A. Passerini and G. Van den Broeck.
ALLEGRO: Belief-based Programming in Stochastic Dynamical Domains.
IJCAI, 2015.
V. Belle and H. J. Levesque.
Probabilistic Inference in Hybrid Domains by Weighted Model Integration.
Workshop on Hybrid Reasoning, IJCAI, 2015.
V. Belle, A. Passerini and G. Van den Broeck.
A Logical Theory of Localization.
Studia Logica:1–32, 2015.
V. Belle and H. Levesque.
Hashing-based Approximate Probabilistic Inference in Hybrid Domains.
UAI, 2015.
V. Belle, G. Van den Broeck and A. Passerini.
Best paper award
Robot location estimation in the situation calculus.
Journal of Applied Logic, 13:397–413, 2015.
V. Belle and H. Levesque.
Semantical Considerations on Multiagent Only Knowing.
Artificial Intelligence, 223:1–26, 2015.
V. Belle and G. Lakemeyer.
Planning Over Multi-Agent Epistemic States: A Classical Planning Approach.
AAAI, 2015.
C. Muise, V. Belle, P. Felli, S. A. McIlraith, T. Miller, A. R. Pearce and L. Sonenberg.
Planning Over Multi-Agent Epistemic States: A Classical Planning Approach (Amended Version).
Workshop on Distributed and Multi-Agent Planning, ICAPS, 2015.
C. Muise, V. Belle, P. Felli, S. A. McIlraith, T. Miller, A. R. Pearce and L. Sonenberg.
Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.
D. Nitti, V. Belle and L. De Raedt.
Machine learning journal best student paper award.
Sample-based abstraction for hybrid relational MDPs.
European Workshop on Reinforcement Learning, ICML, 2015.
Davide Nitti, V. Belle, T. De Laet and Luc De Raedt.
A Logical Theory of Robot Localization.
AAMAS, 2014.
V. Belle and H. J. Levesque.
A Logical Theory of Robot Localization.
AAAI Spring Symposium: Knowledge Representation and Reasoning in Robotics, 2014.
V. Belle and H. J. Levesque.
PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains.
Cognitive Robotics Workshop, 2014.
V. Belle and H. J. Levesque.
How to Progress Beliefs in Continuous Domains.
KR, 2014.
V. Belle and H. J. Levesque.
Multiagent Only Knowing in Dynamic Systems.
Journal of Artificial Intelligence Research, 49:363–402, 2014.
V. Belle and G. Lakemeyer.
On the Progression of Knowledge in Multiagent Systems.
KR, 2014.
V. Belle and G. Lakemeyer.
On the Projection Problem in Active Knowledge Bases with Incomplete Information.
AI Matters, 1(2):14–16, 2014.
V. Belle.
PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains.
AAAI, 2014.
V. Belle and H. J. Levesque.
Review of programming with higher-order logic by Dale Miller and Gopalan Nadathur.
SIGACT News, 45(2):32–35, 2014.
V. Belle.
Nondeterministic Planning with Conditional Effects.
ICAPS, 2014.
C. Muise, S. McIlraith and V. Belle.
Computing Contingent Plans via Fully Observable Non-Deterministic Planning.
ICAPS Workshop: Models and Paradigms for Planning under Uncertainty, 2014.
C. J. Muise, S. A. McIlraith and V. Belle.
Computing Contingent Plans via Fully Observable Non-Deterministic Planning.
AAAI, 2014.
C. J. Muise, S. A. McIlraith and V. Belle.
A Formal Model of Belief under Continuous Uncertainty.
Non-Classical Logics, 2013.
V. Belle and H. J. Levesque.
Probabilistic State Estimation in the Situation Calculus.
IJCAI Workshop: Weighted Logics for Artificial Intelligence, 2013.
V. Belle and H. J. Levesque.
Reasoning about Continuous Uncertainty in the Situation Calculus.
IJCAI, 2013.
V. Belle and H. J. Levesque.
Reasoning about Motion Kinematics with Continuous Uncertainty in the Situation Calculus.
IJCAI Workshop: Nonmonotonic Reasoning, Action and Change, 2013.
V. Belle and H. J. Levesque.
Reasoning about Probabilities in Dynamic Systems using Goal Regression.
UAI, 2013.
V. Belle and H. J. Levesque.
Robot Location Estimation in the Situation Calculus.
ICAPS Workshop: Planning and Robotics, 2013.
V. Belle and H. J. Levesque.
Robot Location Estimation in the Situation Calculus.
Symposium on Logical Formalizations of Commonsense Reasoning, 2013.
V. Belle and H. J. Levesque.
On the Projection Problem in Active Knowledge Bases with Incomplete Information.
PhD thesis, Dept. of Computer Science, RWTH Aachen University, 2012.
V. Belle.
A Semantical Account of Progression in the Presence of Uncertainty.
AAAI, 2011.
V. Belle and G. Lakemeyer.
Multi-Agent Only-Knowing.
Knowing, Reasoning, and Acting: Essays in Honour of H. J. Levesque. College Publications, 2011.
V. Belle and G. Lakemeyer.
On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols.
IJCAI, 2011.
V. Belle and G. Lakemeyer.
Review of from zero to infinity: what makes numbers interesting by Constance Reid.
SIGACT News, 42(2), 2011.
V. Belle.
Multi-Agent Only-Knowing Revisited.
KR, 2010.
V. Belle and G. Lakemeyer.
Multi-Agent Only-Knowing Revisited.
AlgoSyn, 2010.
V. Belle.
Reasoning about Imperfect Information Games in the Epistemic Situation Calculus.
AAAI, 2010.
V. Belle and G. Lakemeyer.
Detection and Recognition of Human Faces using Random Forests for a Mobile Robot.
Master’s thesis, Dept. of Computer Science, RWTH Aachen University, 2008.
V. Belle.
Randomized trees for real-time one-step face detection and recognition.
ICPR, 2008.
V. Belle, T. Deselaers and S. Schiffer.