Randomized trees for real-time one-step face detection and recognition. - Vaishak Belle; Thomas Deselaers; Stefan Schiffer 002 (ICPR, 2008)

Reasoning about Imperfect Information Games in the Epistemic Situation Calculus. - Vaishak Belle; Gerhard Lakemeyer (AAAI, 2010)

Multi-Agent Only-Knowing Revisited. - Vaishak Belle (AlgoSyn, 2010)

Multi-Agent Only-Knowing Revisited. - Vaishak Belle; Gerhard Lakemeyer (KR, 2010)

A Semantical Account of Progression in the Presence of Uncertainty. - Vaishak Belle; Gerhard Lakemeyer (AAAI, 2011)

On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols. - Vaishak Belle; Gerhard Lakemeyer (IJCAI, 2011)

Review of from zero to infinity: what makes numbers interesting by Constance Reid. - Vaishak Belle (SIGACT News, 2011)

On the projection problem in active knowledge bases with incomplete information. - Vaishak Belle (2012)

Reasoning about Continuous Uncertainty in the Situation Calculus. - Vaishak Belle; Hector J. Levesque (IJCAI, 2013)

Reasoning about Probabilities in Dynamic Systems using Goal Regression. - Vaishak Belle; Hector J. Levesque (UAI, 2013)

PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains. - Vaishak Belle; Hector J. Levesque (AAAI, 2014)

Computing Contingent Plans via Fully Observable Non-Deterministic Planning. - Christian J. Muise; Vaishak Belle; Sheila A. McIlraith (AAAI, 2014)

A Logical Theory of Robot Localization. - Vaishak Belle; Hector J. Levesque (AAAI Spring Symposia, 2014)

Non-Deterministic Planning With Conditional Effects. - Christian J. Muise; Sheila A. McIlraith; Vaishak Belle (ICAPS, 2014)

How to Progress Beliefs in Continuous Domains. - Vaishak Belle; Hector J. Levesque (KR, 2014)

On the Progression of Knowledge in Multiagent Systems. - Vaishak Belle; Gerhard Lakemeyer (KR, 2014)

On the projection problem in active knowledge bases with incomplete information. - Vaishak Belle (AI Matters, 2014)

Multiagent Only Knowing in Dynamic Systems. - Vaishak Belle; Gerhard Lakemeyer (J. Artif. Intell. Res., 2014)

Review of programming with higher-order logic by Dale Miller and Gopalan Nadathur. - Vaishak Belle (SIGACT News, 2014)

Planning Over Multi-Agent Epistemic States: A Classical Planning Approach. - Christian J. Muise; Vaishak Belle; Paolo Felli; Sheila A. McIlraith; Tim Miller 001; Adrian R. Pearce; Liz Sonenberg (AAAI, 2015)

Multi-Agent Only Knowing on Planet Kripke. - Guillaume Aucher; Vaishak Belle (IJCAI, 2015)

Only Knowing Meets Common Knowledge. - Vaishak Belle; Gerhard Lakemeyer (IJCAI, 2015)

ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains. - Vaishak Belle; Hector J. Levesque (IJCAI, 2015)

Probabilistic Inference in Hybrid Domains by Weighted Model Integration. - Vaishak Belle; Andrea Passerini; Guy Van den Broeck (IJCAI, 2015)

Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming. - Davide Nitti 001; Vaishak Belle; Luc De Raedt (ECML/PKDD (2), 2015)

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains. - Vaishak Belle; Guy Van den Broeck; Andrea Passerini (UAI, 2015)

Semantical considerations on multiagent only knowing. - Vaishak Belle; Gerhard Lakemeyer (Artif. Intell., 2015)

Robot location estimation in the situation calculus. - Vaishak Belle; Hector J. Levesque (J. Appl. Log., 2015)

Satisfiability and Model Counting in Open Universes. - Vaishak Belle (AAAI Workshop: Beyond NP, 2016)

Component Caching in Hybrid Domains with Piecewise Polynomial Densities. - Vaishak Belle; Guy Van den Broeck; Andrea Passerini (AAAI, 2016)

A First-Order Logic of Probability and Only Knowing in Unbounded Domains. - Vaishak Belle; Gerhard Lakemeyer; Hector J. Levesque (AAAI, 2016)

Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report. - Vaishak Belle; Guy Van den Broeck; Andrea Passerini (IJCAI, 2016)

Foundations for Generalized Planning in Unbounded Stochastic Domains. - Vaishak Belle; Hector J. Levesque (KR, 2016)

Semiring Programming: A Framework for Search, Inference and Learning. - Vaishak Belle; Luc De Raedt (CoRR, 2016)

A Logical Theory of Localization. - Vaishak Belle; Hector J. Levesque (Stud Logica, 2016)

Open-Universe Weighted Model Counting. - Vaishak Belle (AAAI, 2017)

Open-Universe Weighted Model Counting: Extended Abstract. - Vaishak Belle (AAAI Workshops, 2017)

The Symbolic Interior Point Method. - Martin Mladenov; Vaishak Belle; Kristian Kersting (AAAI, 2017)

Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds. - Vaishak Belle (IJCAI, 2017)

Reasoning about Probabilities in Unbounded First-Order Dynamical Domains. - Vaishak Belle; Gerhard Lakemeyer (IJCAI, 2017)

Solving Probability Problems in Natural Language. - Anton Dries; Angelika Kimmig; Jesse Davis; Vaishak Belle; Luc De Raedt (IJCAI, 2017)

Weighted Model Counting With Function Symbols. - Vaishak Belle (UAI, 2017)

Planning in hybrid relational MDPs. - Davide Nitti 001; Vaishak Belle; Tinne De Laet; Luc De Raedt (Mach. Learn., 2017)

Probabilistic Planning by Probabilistic Programming. - Vaishak Belle (AAAI Workshops, 2018)

On Plans With Loops and Noise. - Vaishak Belle (AAMAS, 2018)

Efficient Symbolic Integration for Probabilistic Inference. - Samuel Kolb; Martin Mladenov; Scott Sanner; Vaishak Belle; Kristian Kersting (IJCAI, 2018)

Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems. - Vaishak Belle; Hector J. Levesque (Artif. Intell., 2018)

Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks. - Andreas C. Bueff; Stefanie Speichert; Vaishak Belle (CoRR, 2018)

Learning Probabilistic Logic Programs in Continuous Domains. - Stefanie Speichert; Vaishak Belle (CoRR, 2018)

Abstracting Probabilistic Relational Models. - Vaishak Belle (CoRR, 2018)

Deep Tractable Probabilistic Models for Moral Responsibility. - Lewis Hammond; Vaishak Belle (CoRR, 2018)

Learning Probabilistic Logic Programs over Continuous Data. - Stefanie Speichert; Vaishak Belle (ILP, 2019)

Implicitly learning to reason in first-order logic. - Vaishak Belle; Brendan Juba (NeurIPS, 2019)

Experiential AI. - Drew Hemment; Ruth Aylett; Vaishak Belle; Dave Murray-Rust; Ewa Luger; Jane Hillston; Michael Rovatsos; Frank Broz (AI Matters, 2019)

Learning Tractable Probabilistic Models in Open Worlds. - Amélie Levray; Vaishak Belle (CoRR, 2019)

The Quest for Interpretable and Responsible Artificial Intelligence. - Vaishak Belle (CoRR, 2019)

Learning Credal Sum-Product Networks. - Amélie Levray; Vaishak Belle (AKBC, 2020)

Generating Random Logic Programs Using Constraint Programming. - Paulius Dilkas; Vaishak Belle (CP, 2020)

Logical Interpretations of Autoencoders. - Anton Fuxjaeger; Vaishak Belle (ECAI, 2020)

Polynomial-Time Implicit Learnability in SMT. - Ionela G. Mocanu; Vaishak Belle; Brendan Juba (ECAI, 2020)

Scaling up Probabilistic Inference in Linear and Non-linear Hybrid Domains by Leveraging Knowledge Compilation. - Anton Fuxjaeger; Vaishak Belle (ICAART (2), 2020)

Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains. - Vaishak Belle (SUM, 2020)

Logic, Probability and Action: A Situation Calculus Perspective. - Vaishak Belle (SUM, 2020)

Regression and progression in stochastic domains. - Vaishak Belle; Hector J. Levesque (Artif. Intell., 2020)

SMT + ILP. - Vaishak Belle (CoRR, 2020)

Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations. - Ioannis Papantonis; Vaishak Belle (CoRR, 2020)

On Constraint Definability in Tractable Probabilistic Models. - Ioannis Papantonis; Vaishak Belle (CoRR, 2020)

Semiring programming: A semantic framework for generalized sum product problems. - Vaishak Belle; Luc De Raedt (Int. J. Approx. Reason., 2020)

A correctness result for synthesizing plans with loops in stochastic domains. - Laszlo Treszkai; Vaishak Belle (Int. J. Approx. Reason., 2020)

Abstracting probabilistic models: Relations, constraints and beyond. - Vaishak Belle (Knowl. Based Syst., 2020)

Learning Implicitly with Noisy Data in Linear Arithmetic. - Alexander Philipp Rader; Ionela G. Mocanu; Vaishak Belle; Brendan Juba (IJCAI, 2021)

Intent Recognition in Smart Homes with ProbLog. - Gary Smith; Ron P. A. Petrick; Vaishak Belle (PerCom Workshops, 2021)

Weighted Model Counting Without Parameter Variables. - Paulius Dilkas; Vaishak Belle (SAT, 2021)

Weighted model counting with conditional weights for Bayesian networks. - Paulius Dilkas; Vaishak Belle (UAI, 2021)

Lifted reasoning meets weighted model integration. - Jonathan Feldstein; Vaishak Belle (UAI, 2021)

Learning tractable probabilistic models for moral responsibility and blame. - Lewis Hammond; Vaishak Belle (Data Min. Knowl. Discov., 2021)

Probabilistic Tractable Models in Mixed Discrete-Continuous Domains. - Andreas C. Bueff; Stefanie Speichert; Vaishak Belle (Data Intell., 2021)

Principles and Practice of Explainable Machine Learning. - Vaishak Belle; Ioannis Papantonis (Frontiers Big Data, 2021)

Closed-Form Results for Prior Constraints in Sum-Product Networks. - Ioannis Papantonis; Vaishak Belle (Frontiers Artif. Intell., 2021)

Fairness in machine learning with tractable models. - Michael Varley; Vaishak Belle (Knowl. Based Syst., 2021)

One down, 699 to go: or, synthesising compositional desugarings. - Sándor Bartha; James Cheney; Vaishak Belle (Proc. ACM Program. Lang., 2021)

Logic Meets Learning: From Aristotle to Neural Networks. - Vaishak Belle (Neuro-Symbolic Artificial Intelligence, 2021)

MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks. - Nick Hoernle; Rafael-Michael Karampatsis; Vaishak Belle; Kobi Gal (AAAI, 2022)

KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments. - Ameer Saadat-Yazdi; Xue Li 018; Sandrine Chausson; Vaishak Belle; Björn Ross; Jeff Z. Pan; Nadin Kökciyan (ArgMining@COLING, 2022)

Tractable Probabilistic Models for Ethical AI. - Vaishak Belle (ICCS, 2022)

Analyzing generalized planning under nondeterminism. - Vaishak Belle (Artif. Intell., 2022)

Efficient multi-agent epistemic planning: Teaching planners about nested belief. - Christian Muise; Vaishak Belle; Paolo Felli; Sheila A. McIlraith; Tim Miller 001; Adrian R. Pearce; Liz Sonenberg (Artif. Intell., 2022)

Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities. - Xin Du 006; Bénédicte Legastelois; Bhargavi Ganesh; Ajitha Rajan; Hana Chockler; Vaishak Belle; Stuart Anderson; Subramanian Ramamoorthy (CoRR, 2022)

Explainability in Machine Learning: a Pedagogical Perspective. - Andreas C. Bueff; Ioannis Papantonis; Auste Simkute; Vaishak Belle (CoRR, 2022)

Using Abstraction for Interpretable Robot Programs in Stochastic Domains. - Till Hofmann; Vaishak Belle (CoRR, 2022)

Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond. - Miguel Ángel Méndez Lucero; Rafael-Michael Karampatsis; Enrique Bojorquez Gallardo; Vaishak Belle (Frontiers Artif. Intell., 2022)

Intention Recognition With ProbLog. - Gary Smith; Vaishak Belle; Ronald P. A. Petrick (Frontiers Artif. Intell., 2022)

Breaking CAPTCHA with Capsule Networks. - Ionela Georgiana Mocanu; Zhenxu Yang; Vaishak Belle (Neural Networks, 2022)

Actions, Continuous Distributions and Meta-Beliefs. - Vaishak Belle (AAMAS, 2023)

Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges. - Vaishak Belle; Michael Fisher 001; Alessandra Russo; Ekaterina Komendantskaya; Alistair Nottle (AAMAS Workshops, 2023)

A Web-based Tool for Detecting Argument Validity and Novelty. - Sandrine Chausson; Ameer Saadat-Yazdi; Xue Li 018; Jeff Z. Pan; Vaishak Belle; Nadin Kökciyan; Björn Ross (AAMAS, 2023)

A Logic of Only-Believing over Arbitrary Probability Distributions. - Qihui Feng; Daxin Liu 002; Vaishak Belle; Gerhard Lakemeyer (AAMAS, 2023)

Abstracting Noisy Robot Programs. - Till Hofmann; Vaishak Belle (AAMAS, 2023)

Verifying Belief-Based Programs via Symbolic Dynamic Programming. - Daxin Liu 002; Qinfei Huang; Vaishak Belle; Gerhard Lakemeyer (ECAI, 2023)

Transparency in Sum-Product Network Decompilation. - Ioannis Papantonis; Vaishak Belle (ECAI, 2023)

Logic + Reinforcement Learning + Deep Learning: A Survey. - Andreas C. Bueff; Vaishak Belle (ICAART (3), 2023)

Model Transparency: Why Do We Care? - Ioannis Papantonis; Vaishak Belle (ICAART (3), 2023)

Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond. - Vaishak Belle (JELIA, 2023)

Concerning Measures in a First-order Logic with Actions and Meta-beliefs. - Daxin Liu 002; Qihui Feng; Vaishak Belle; Gerhard Lakemeyer (KR, 2023)

Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures. - Paulius Dilkas; Vaishak Belle (KR, 2023)

Challenges in Explaining Brain Tumor Detection. - Bénédicte Legastelois; Amy Rafferty; Paul Brennan; Hana Chockler; Ajitha Rajan; Vaishak Belle (TAS, 2023)

Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration. - Ioannis Papantonis; Vaishak Belle (CoRR, 2023)

Experiential AI: A transdisciplinary framework for legibility and agency in AI. - Drew Hemment; Dave Murray-Rust; Vaishak Belle; Ruth Aylett; Matjaz Vidmar; Frank Broz (CoRR, 2023)

Agency and legibility for artists through Experiential AI. - Drew Hemment; Matjaz Vidmar; Daga Panas; Dave Murray-Rust; Vaishak Belle; Ruth Aylett (CoRR, 2023)

Statistical relational learning and neuro-symbolic AI: what does first-order logic offer? - Vaishak Belle (CoRR, 2023)

Deep Inductive Logic Programming meets Reinforcement Learning. - Andreas C. Bueff; Vaishak Belle (ICLP, 2023)

Knowledge representation and acquisition in the era of large language models: Reflections on learning to reason via PAC-Semantics. - Ionela G. Mocanu; Vaishak Belle (Nat. Lang. Process. J., 2023)

Boolean Connectives and Deep Learning: Three Interpretations. - Miguel Ángel Méndez Lucero; Vaishak Belle (Compendium of Neurosymbolic Artificial Intelligence, 2023)

Epistemic planning: Perspectives on the special issue. - Vaishak Belle; Thomas Bolander; Andreas Herzig; Bernhard Nebel (Artif. Intell., 2023)

Knowledge representation and acquisition for ethical AI: challenges and opportunities. - Vaishak Belle (Ethics Inf. Technol., 2023)

Toward A Logical Theory Of Fairness and Bias. - Vaishak Belle (Theory Pract. Log. Program., 2023)

Learnability with PAC Semantics for Multi-agent Beliefs. - Ionela G. Mocanu; Vaishak Belle; Brendan Juba (Theory Pract. Log. Program., 2023)

Progression with Probabilities in the Situation Calculus: Representation and Succinctness. - Daxin Liu 002; Vaishak Belle (AAMAS, 2024)

Ethical Reward Machine. - Jessica Ciupa; Vaishak Belle (NeSy (1), 2024)

Can Large Language Models Put 2 and 2 Together? Probing for Entailed Arithmetical Relationships. - Dagmara Panas; Sohan Seth; Vaishak Belle (NeSy (2), 2024)

ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models. - Weizhi Tang; Vaishak Belle (NeSy (2), 2024)

Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models. - Weizhi Tang; Vaishak Belle (CoRR, 2024)

LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language Models. - Weizhi Tang; Vaishak Belle (CoRR, 2024)

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning. - Alec F. Diallo; Vaishak Belle; Paul Patras (CoRR, 2024)

Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning. - Jonathan Feldstein; Paulius Dilkas; Vaishak Belle; Efthymia Tsamoura (CoRR, 2024)

Practical Considerations for Agentic LLM Systems. - Chris Sypherd; Vaishak Belle (CoRR, 2024)

Trustworthiness and Responsibility in AI - Causality, Learning, and Verification (Dagstuhl Seminar 24121). - Vaishak Belle; Hana Chockler; Shannon Vallor; Kush R. Varshney; Joost Vennekens; Sander Beckers (Dagstuhl Reports, 2024)

Principled diverse counterfactuals in multilinear models. - Ioannis Papantonis; Vaishak Belle (Mach. Learn., 2024)

Counterfactual Explanations as Plans. - Vaishak Belle (ICLP, 2024)

Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach. - Andreas C. Bueff; Vaishak Belle (Mach. Learn., 2024)

HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation. - Weizhi Tang; Yixuan Li 003; Chris Sypherd; Elizabeth Polgreen; Vaishak Belle (ACL (Findings), 2025)

Abnormal Predicates: Learning Categorical Defaults from Probabilistic Rules. - Rose Azad Khan; Vaishak Belle (ICAART (3), 2025)

Semantic Objective Functions: A Distribution-Aware Method for Adding Logical Constraints in Deep Learning. - Miguel Ángel Méndez Lucero; Enrique Bojorquez Gallardo; Vaishak Belle (ICAART (3), 2025)

Tractable Generative Modelling of Cosmological Numerical Simulations. - Amit Parag; Vaishak Belle (ICAART (3), 2025)

Towards Developing Ethical Reasoners: Integrating Probabilistic Reasoning and Decision-Making for Complex AI Systems. - Nijesh Upreti; Jessica Ciupa; Vaishak Belle (ICAART (1), 2025)

What Is a Counterfactual Cause in Action Theories? - Daxin Liu 002; Vaishak Belle (AAMAS, 2025)

A Uniform Language for Safety, Robustness and Explainability. - Vaishak Belle; Pablo Barceló (JELIA (1), 2025)

A propositional encoding for first-order clausal entailment over infinitely many constants. - Vaishak Belle (J. Symb. Comput., 2025)

An Algebraic Framework for Hierarchical Probabilistic Abstraction. - Nijesh Upreti; Vaishak Belle (CoRR, 2025)

Neuro-symbolic Weak Supervision: Theory and Semantics. - Nijesh Upreti; Vaishak Belle (CoRR, 2025)

Program Semantic Inequivalence Game with Large Language Models. - Antonio Valerio Miceli Barone; Vaishak Belle; Ali Payani (CoRR, 2025)

Incorporating Token Usage into Prompting Strategy Evaluation. - Chris Sypherd; Sergei Petrov; Sonny George; Vaishak Belle (CoRR, 2025)

Lyria: A General LLM-Driven Genetic Algorithm Framework for Problem Solving. - Weizhi Tang; Kwabena Nuamah; Vaishak Belle (CoRR, 2025)