Vaishak Belle
Reader in Logic and Learning
Leads the Belle Lab, researching logic and learning, including neuro-symbolic AI, probabilistic reasoning and explainable and responsible AI.
Vaishak Belle's lab at Edinburgh works on reasoning & neuro-symbolic AI, probabilistic & causal inference and reasoning & agents. 82 papers since 2023, 11 at top venues, mostly AAMAS and KR. 1 master's student. 18 alumni.
At a glance
- papers, 2023–2026
- 21/ year
- at T1 venues
- 13%
- PhD students
- 0
- MS students
- 1
- alumni listed
- 18
Papers by yearfrom the lab homepage, 2023–2026
- 24 papers2026 · T1 2 · collecting
- 25 papers2025 · T1 2
- 11 papers2024 · T1 2
- 22 papers2023 · T1 5
T1 venuesOtherVenues: AAMAS, KR, TMLR, Artificial Intelligence, Findings of ACL
Research focuspaper titles that use each topic's words
- Reasoning & neuro-symbolic AI21 papers
- logic14 papers
- Reasoning & agents10 papers
- Probabilistic & causal inference5 papers
- probabilistic reasoning1 paper
Paper list
From the lab homepage.
202624 papers
- ICLP, .A Counterfactual Cause in Situation Calculus.
- Neurosymbolic Artificial Intelligence, .A Neurosymbolic Approach to Counterfactual Fairness.
- HAR, .Addressing AI Sycophancy: A New Frontier in the Attention Economy.
- HAR, .Agentic Cognitive Depth: Operational Criteria for Evaluating LLM Agents.
- HAR, .Automating Hard Choices: Agentic AI’s Impact on Moral Identity and Intervention.
- NeSy, .DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs.
- ICML WorkshopDeepSWIP: Single-World Counterfactual Semantics for DeepProbLog.
- ICML WorkshopEfficient Counterfactual Reasoning in ProbLog via Single-World Intervention Programs.
- AAMAS (T1)Epistemic Modal Logic Meets Algebraic Model Counting.
- NeSy, .Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs.
- NeSy, .Interpretable Moral Decision-Making under Epistemic Uncertainty.
- HAR, .Knowledge Neglect in Agent Interactions (with Chatbots).
202525 papers
- Journal of Symbolic Computation, .A Propositional Encoding for First-Order Clausal Entailment over Infinitely Many Constants.
- JELIA, .A Uniform Language for Safety, Robustness and Explainability.
- ICAARTAbnormal Predicates: Learning Categorical Defaults from Probabilistic Rules
- HAR, .Breaking the Illusion: Revisiting LLM Anthropomorphism.
- HAR, .Can Decomposition Surpass Human Bias on the Wason Task?
- The Alan Turing Institute, . DOI: 10.5281/zenodo.16421296Doing AI Differently: Rethinking the Foundations of AI via the Humanities.
- Frontiers in Education, .Explainability in machine learning: a pedagogical perspective.
- NeSy, .Exploring Verification Frameworks for Social Choice Alignment.
- Findings of ACLHyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation.
- HAR, .Knowledge-Free and Knowledge-Based Theory of Mind Reasoning in Large Language Models.
- HAR, .Logic (in action) meets probability: a new dawn for robotics?
- Dagstuhl ReportsLogic and Neural Networks (Dagstuhl Seminar 25061).
202411 papers
- IJCLR, .An Algebraic Framework for Hierarchical Probabilistic Abstraction.
- NeSy, .Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships.
- NeSy, .Ethical Reward Machines.
- Leonardo, .Experiential AI: Between Arts and Explainable AI.
- IJCLR, .Implicit Learning as Reasoning: Language-Agnostic Semantics for Learning from Partial Observations. (Extended Abstract)
- Machine LearningLearning Explanatory Logical Rules in Non-linear Domains: A Neuro-Symbolic Approach.
- IJCLR, .MisCC: Misinformation Detection on Counterfactual Claims.
- AAMAS (T1)Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges.
- AAMAS (T1)Progression with probabilities in the situation calculus: representation and succinctness.
- IJCLR, .Reasoning about Neural Network Perception in the Situation Calculus. (Extended Abstract)
- NeSy, .ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models.
202322 papers
- AAMAS (T1)A Logic of Only-Believing over Arbitrary Probability Distributions.
- AAMAS (T1)Abstracting Noisy Robot Programs.
- AAMAS (T1)Actions, Continuous Distributions and Meta-Beliefs.
- Compendium of Neurosymbolic Artificial Intelligence, IOS Press, .Boolean Connectives and Deep Learning: Three Interpretations.
- KRConcerning Measures in a First-order Logic with Actions and Meta-beliefs.
- ICLP, .Counterfactual Explanations as Plans.
- ICLP, .Deep Inductive Logic Programming meets Reinforcement Learning.
- Artificial Intelligence (T1)Epistemic planning: Perspectives on the special issue. (opens in a new tab)
- Royal Society of Edinburgh - Resource Magazine, .Ethical AI: Issues and Opportunites. (opens in a new tab)
- JELIA, .Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond.
- Ethics and Information Technology, .Knowledge Representation and Acquisition for Ethical AI: Challenges and Opportunities.
- Natural Language Processing Journal, .Knowledge representation and acquisition in the era of large language models: Reflections on learning to reason via PAC-Semantics.
Where alumni went18 listed
Apply to EdinburghFall 2027
Not announced yet
Informatics PhD
- Round
- Rolling (funding consideration)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- none
Not yet confirmed for this intake.
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