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ORCIDVaishak Belle
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- affiliation: University of Edinburgh, UK
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2020 – today
- 2024
[j32]Ioannis Papantonis
, Vaishak Belle:
Principled diverse counterfactuals in multilinear models. Mach. Learn. 113(3): 1421-1443 (2024)- [j31]Andreas C. Bueff, Vaishak Belle:
Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach. Mach. Learn. 113(7): 4579-4614 (2024) - [c73]Daxin Liu, Vaishak Belle:
Progression with Probabilities in the Situation Calculus: Representation and Succinctness. AAMAS 2024: 1210-1218 - [c72]Jessica Ciupa, Vaishak Belle:
Ethical Reward Machine. NeSy (1) 2024: 180-194 - [c71]Weizhi Tang, Vaishak Belle:
ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models. NeSy (2) 2024: 245-257 - [c70]Dagmara Panas, Sohan Seth, Vaishak Belle:
Can Large Language Models Put 2 and 2 Together? Probing for Entailed Arithmetical Relationships. NeSy (2) 2024: 258-276 - [i49]Weizhi Tang, Vaishak Belle:
ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models. CoRR abs/2404.15515 (2024) - [i48]Dagmara Panas, Sohan Seth, Vaishak Belle:
Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships. CoRR abs/2404.19432 (2024) - [i47]Miguel Ángel Méndez Lucero, Enrique Bojorquez Gallardo, Vaishak Belle:
Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning. CoRR abs/2405.15789 (2024) - [i46]Weizhi Tang, Vaishak Belle:
Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models. CoRR abs/2406.04800 (2024) - [i45]Weizhi Tang, Vaishak Belle:
LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language Models. CoRR abs/2407.05434 (2024) - [i44]Vaishak Belle, Hana Chockler, Shannon Vallor, Kush R. Varshney, Joost Vennekens, Sander Beckers:
Trustworthiness and Responsibility in AI - Causality, Learning, and Verification (Dagstuhl Seminar 24121). Dagstuhl Reports 14(3): 75-91 (2024) - 2023
- [j30]Vaishak Belle, Thomas Bolander, Andreas Herzig, Bernhard Nebel:
Epistemic planning: Perspectives on the special issue. Artif. Intell. 316: 103842 (2023) - [j29]Vaishak Belle:
Knowledge representation and acquisition for ethical AI: challenges and opportunities. Ethics Inf. Technol. 25(1): 22 (2023) - [j28]Ionela G. Mocanu, Vaishak Belle:
Knowledge representation and acquisition in the era of large language models: Reflections on learning to reason via PAC-Semantics. Nat. Lang. Process. J. 5: 100036 (2023) - [j27]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. Theory Pract. Log. Program. 23(4): 730-747 (2023) - [j26]Vaishak Belle:
Toward A Logical Theory Of Fairness and Bias. Theory Pract. Log. Program. 23(4): 865-883 (2023) - [c69]Vaishak Belle, Michael Fisher, Alessandra Russo, Ekaterina Komendantskaya, Alistair Nottle:
Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges. AAMAS Workshops 2023: 180-200 - [c68]Qihui Feng, Daxin Liu, Vaishak Belle, Gerhard Lakemeyer:
A Logic of Only-Believing over Arbitrary Probability Distributions. AAMAS 2023: 355-363 - [c67]Vaishak Belle:
Actions, Continuous Distributions and Meta-Beliefs. AAMAS 2023: 418-426 - [c66]Till Hofmann, Vaishak Belle:
Abstracting Noisy Robot Programs. AAMAS 2023: 534-542 - [c65]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. AAMAS 2023: 2604-2606 - [c64]Sandrine Chausson, Ameer Saadat-Yazdi, Xue Li, Jeff Z. Pan, Vaishak Belle, Nadin Kökciyan, Björn Ross:
A Web-based Tool for Detecting Argument Validity and Novelty. AAMAS 2023: 3053-3055 - [c63]Daxin Liu, Qinfei Huang, Vaishak Belle, Gerhard Lakemeyer:
Verifying Belief-Based Programs via Symbolic Dynamic Programming. ECAI 2023: 1497-1504 - [c62]Ioannis Papantonis, Vaishak Belle:
Transparency in Sum-Product Network Decompilation. ECAI 2023: 1827-1834 - [c61]Ioannis Papantonis, Vaishak Belle:
Model Transparency: Why Do We Care? ICAART (3) 2023: 650-657 - [c60]Andreas C. Bueff, Vaishak Belle:
Logic + Reinforcement Learning + Deep Learning: A Survey. ICAART (3) 2023: 713-722 - [c59]Vaishak Belle:
Excursions in First-Order Logic and Probability: Infinitely Many Random Variables, Continuous Distributions, Recursive Programs and Beyond. JELIA 2023: 35-46 - [c58]Paulius Dilkas, Vaishak Belle:
Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures. KR 2023: 198-207 - [c57]Daxin Liu, Qihui Feng, Vaishak Belle, Gerhard Lakemeyer:
Concerning Measures in a First-order Logic with Actions and Meta-beliefs. KR 2023: 451-460 - [c56]Bénédicte Legastelois, Amy Rafferty, Paul Brennan, Hana Chockler, Ajitha Rajan, Vaishak Belle:
Challenges in Explaining Brain Tumor Detection. TAS 2023: 21:1-21:8 - [c55]Andreas C. Bueff, Vaishak Belle:
Deep Inductive Logic Programming meets Reinforcement Learning. ICLP 2023: 339-352 - [p2]Miguel Ángel Méndez Lucero, Vaishak Belle:
Boolean Connectives and Deep Learning: Three Interpretations. Compendium of Neurosymbolic Artificial Intelligence 2023: 100-113 - [i43]Ioannis Papantonis, Vaishak Belle:
Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration. CoRR abs/2304.14130 (2023) - [i42]Drew Hemment, Dave Murray-Rust, Vaishak Belle, Ruth Aylett, Matjaz Vidmar, Frank Broz:
Experiential AI: A transdisciplinary framework for legibility and agency in AI. CoRR abs/2306.00635 (2023) - [i41]Drew Hemment, Matjaz Vidmar, Daga Panas, Dave Murray-Rust, Vaishak Belle, Ruth Aylett:
Agency and legibility for artists through Experiential AI. CoRR abs/2306.02327 (2023) - [i40]Paulius Dilkas, Vaishak Belle:
Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures. CoRR abs/2306.04189 (2023) - [i39]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learnability with PAC Semantics for Multi-agent Beliefs. CoRR abs/2306.05490 (2023) - [i38]Vaishak Belle:
Toward A Logical Theory Of Fairness and Bias. CoRR abs/2306.13659 (2023) - [i37]Vaishak Belle:
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer? CoRR abs/2306.13660 (2023) - 2022
- [j25]Christian Muise, Vaishak Belle, Paolo Felli, Sheila A. McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg:
Efficient multi-agent epistemic planning: Teaching planners about nested belief. Artif. Intell. 302: 103605 (2022) - [j24]Vaishak Belle:
Analyzing generalized planning under nondeterminism. Artif. Intell. 307: 103696 (2022) - [j23]Miguel Ángel Méndez Lucero, Rafael-Michael Karampatsis, Enrique Bojorquez Gallardo, Vaishak Belle:
Signal Perceptron: On the Identifiability of Boolean Function Spaces and Beyond. Frontiers Artif. Intell. 5: 770254 (2022) - [j22]Gary Smith, Vaishak Belle, Ronald P. A. Petrick:
Intention Recognition With ProbLog. Frontiers Artif. Intell. 5: 806262 (2022) - [j21]Ionela Georgiana Mocanu, Zhenxu Yang, Vaishak Belle:
Breaking CAPTCHA with Capsule Networks. Neural Networks 154: 246-254 (2022) - [c54]Nick Hoernle, Rafael-Michael Karampatsis, Vaishak Belle, Kobi Gal:
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks. AAAI 2022: 5700-5709 - [c53]Ameer Saadat-Yazdi, Xue Li, Sandrine Chausson, Vaishak Belle, Björn Ross, Jeff Z. Pan, Nadin Kökciyan:
KEViN: A Knowledge Enhanced Validity and Novelty Classifier for Arguments. ArgMining@COLING 2022: 104-110 - [c52]Vaishak Belle:
Tractable Probabilistic Models for Ethical AI. ICCS 2022: 3-8 - [i36]Ioannis Papantonis, Vaishak Belle:
Principled Diverse Counterfactuals in Multilinear Models. CoRR abs/2201.06467 (2022) - [i35]Xin Du, Bénédicte Legastelois, Bhargavi Ganesh, Ajitha Rajan, Hana Chockler, Vaishak Belle, Stuart Anderson, Subramanian Ramamoorthy:
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities. CoRR abs/2201.11674 (2022) - [i34]Andreas C. Bueff, Ioannis Papantonis, Auste Simkute, Vaishak Belle:
Explainability in Machine Learning: a Pedagogical Perspective. CoRR abs/2202.10335 (2022) - [i33]Till Hofmann, Vaishak Belle:
Abstracting Noisy Robot Programs. CoRR abs/2204.03536 (2022) - [i32]Till Hofmann, Vaishak Belle:
Using Abstraction for Interpretable Robot Programs in Stochastic Domains. CoRR abs/2207.12763 (2022) - 2021
- [j20]Lewis Hammond, Vaishak Belle:
Learning tractable probabilistic models for moral responsibility and blame. Data Min. Knowl. Discov. 35(2): 621-659 (2021) - [j19]Andreas C. Bueff, Stefanie Speichert, Vaishak Belle:
Probabilistic Tractable Models in Mixed Discrete-Continuous Domains. Data Intell. 3(2): 228-260 (2021) - [j18]Vaishak Belle, Ioannis Papantonis:
Principles and Practice of Explainable Machine Learning. Frontiers Big Data 4: 688969 (2021) - [j17]Ioannis Papantonis, Vaishak Belle:
Closed-Form Results for Prior Constraints in Sum-Product Networks. Frontiers Artif. Intell. 4: 644062 (2021) - [j16]Michael Varley, Vaishak Belle:
Fairness in machine learning with tractable models. Knowl. Based Syst. 215: 106715 (2021) - [j15]Sándor Bartha, James Cheney, Vaishak Belle:
One down, 699 to go: or, synthesising compositional desugarings. Proc. ACM Program. Lang. 5(OOPSLA): 1-29 (2021) - [c51]Alexander Philipp Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learning Implicitly with Noisy Data in Linear Arithmetic. IJCAI 2021: 1410-1417 - [c50]Gary Smith, Ron P. A. Petrick, Vaishak Belle:
Intent Recognition in Smart Homes with ProbLog. PerCom Workshops 2021: 430-431 - [c49]Paulius Dilkas, Vaishak Belle:
Weighted Model Counting Without Parameter Variables. SAT 2021: 134-151 - [c48]Jonathan Feldstein, Vaishak Belle:
Lifted reasoning meets weighted model integration. UAI 2021: 322-332 - [c47]Paulius Dilkas, Vaishak Belle:
Weighted model counting with conditional weights for Bayesian networks. UAI 2021: 386-396 - [p1]Vaishak Belle:
Logic Meets Learning: From Aristotle to Neural Networks. Neuro-Symbolic Artificial Intelligence 2021: 78-102 - [i31]Sándor Bartha, James Cheney, Vaishak Belle:
One Down, 699 to Go: or, synthesising compositional desugarings. CoRR abs/2109.06114 (2021) - [i30]Christian Muise, Vaishak Belle, Paolo Felli, Sheila A. McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg:
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief. CoRR abs/2110.02480 (2021) - [i29]Nicholas Hoernle, Rafael-Michael Karampatsis, Vaishak Belle, Kobi Gal:
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks. CoRR abs/2111.01564 (2021) - 2020
- [j14]Vaishak Belle, Hector J. Levesque:
Regression and progression in stochastic domains. Artif. Intell. 281: 103247 (2020) - [j13]Laszlo Treszkai, Vaishak Belle:
A correctness result for synthesizing plans with loops in stochastic domains. Int. J. Approx. Reason. 119: 92-107 (2020) - [j12]Vaishak Belle, Luc De Raedt:
Semiring programming: A semantic framework for generalized sum product problems. Int. J. Approx. Reason. 126: 181-201 (2020) - [j11]Vaishak Belle:
Abstracting probabilistic models: Relations, constraints and beyond. Knowl. Based Syst. 199: 105976 (2020) - [c46]Amélie Levray, Vaishak Belle:
Learning Credal Sum-Product Networks. AKBC 2020 - [c45]Paulius Dilkas, Vaishak Belle:
Generating Random Logic Programs Using Constraint Programming. CP 2020: 828-845 - [c44]Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Polynomial-Time Implicit Learnability in SMT. ECAI 2020: 1152-1158 - [c43]Anton Fuxjaeger, Vaishak Belle:
Logical Interpretations of Autoencoders. ECAI 2020: 2481-2488 - [c42]Anton Fuxjaeger, Vaishak Belle:
Scaling up Probabilistic Inference in Linear and Non-linear Hybrid Domains by Leveraging Knowledge Compilation. ICAART (2) 2020: 347-355 - [c41]Vaishak Belle:
Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains. SUM 2020: 3-16 - [c40]Vaishak Belle:
Logic, Probability and Action: A Situation Calculus Perspective. SUM 2020: 52-67 - [i28]Vaishak Belle:
SMT + ILP. CoRR abs/2001.05208 (2020) - [i27]Ioannis Papantonis, Vaishak Belle:
Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations. CoRR abs/2001.10905 (2020) - [i26]Ioannis Papantonis, Vaishak Belle:
On Constraint Definability in Tractable Probabilistic Models. CoRR abs/2001.11349 (2020) - [i25]Paulius Dilkas, Vaishak Belle:
Generating Random Logic Programs Using Constraint Programming. CoRR abs/2006.01889 (2020) - [i24]Vaishak Belle:
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains. CoRR abs/2006.08480 (2020) - [i23]Vaishak Belle:
Logic, Probability and Action: A Situation Calculus Perspective. CoRR abs/2006.09868 (2020) - [i22]Vaishak Belle, Ioannis Papantonis:
Principles and Practice of Explainable Machine Learning. CoRR abs/2009.11698 (2020) - [i21]Alexander Philipp Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba:
Learning Implicitly with Noisy Data in Linear Arithmetic. CoRR abs/2010.12619 (2020)
2010 – 2019
- 2019
- [j10]Drew Hemment, Ruth Aylett, Vaishak Belle, Dave Murray-Rust, Ewa Luger, Jane Hillston, Michael Rovatsos, Frank Broz:
Experiential AI. AI Matters 5(1): 25-31 (2019) - [c39]Stefanie Speichert, Vaishak Belle:
Learning Probabilistic Logic Programs over Continuous Data. ILP 2019: 129-144 - [c38]Vaishak Belle, Brendan Juba:
Implicitly learning to reason in first-order logic. NeurIPS 2019: 3376-3386 - [i20]Amélie Levray, Vaishak Belle:
Learning Tractable Probabilistic Models in Open Worlds. CoRR abs/1901.05847 (2019) - [i19]Michael Varley, Vaishak Belle:
Fairness in Machine Learning with Tractable Models. CoRR abs/1905.07026 (2019) - [i18]Laszlo Treszkai, Vaishak Belle:
A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains. CoRR abs/1905.07028 (2019) - [i17]Vaishak Belle, Brendan Juba:
Implicitly Learning to Reason in First-Order Logic. CoRR abs/1906.10106 (2019) - [i16]Drew Hemment, Ruth Aylett, Vaishak Belle, Dave Murray-Rust, Ewa Luger, Jane Hillston, Michael Rovatsos, Frank Broz:
Experiential AI. CoRR abs/1908.02619 (2019) - [i15]Vaishak Belle:
The Quest for Interpretable and Responsible Artificial Intelligence. CoRR abs/1910.04527 (2019) - [i14]Anton Fuxjaeger, Vaishak Belle:
Logical Interpretations of Autoencoders. CoRR abs/1911.11629 (2019) - 2018
- [j9]Vaishak Belle, Hector J. Levesque:
Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems. Artif. Intell. 262: 189-221 (2018) - [c37]Vaishak Belle:
Probabilistic Planning by Probabilistic Programming. AAAI Workshops 2018: 654-657 - [c36]Vaishak Belle:
On Plans With Loops and Noise. AAMAS 2018: 1310-1317 - [c35]Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting:
Efficient Symbolic Integration for Probabilistic Inference. IJCAI 2018: 5031-5037 - [i13]Vaishak Belle:
Probabilistic Planning by Probabilistic Programming. CoRR abs/1801.08365 (2018) - [i12]Andreas C. Bueff, Stefanie Speichert, Vaishak Belle:
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks. CoRR abs/1807.05464 (2018) - [i11]Stefanie Speichert, Vaishak Belle:
Learning Probabilistic Logic Programs in Continuous Domains. CoRR abs/1807.05527 (2018) - [i10]Vaishak Belle:
On Plans With Loops and Noise. CoRR abs/1809.05309 (2018) - [i9]Vaishak Belle, Hector J. Levesque:
Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems. CoRR abs/1809.05314 (2018) - [i8]Vaishak Belle:
Abstracting Probabilistic Relational Models. CoRR abs/1810.02434 (2018) - [i7]Lewis Hammond, Vaishak Belle:
Deep Tractable Probabilistic Models for Moral Responsibility. CoRR abs/1810.03736 (2018) - [i6]Anton Fuxjaeger, Vaishak Belle:
Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation. CoRR abs/1811.12127 (2018) - 2017
- [j8]Davide Nitti, Vaishak Belle, Tinne De Laet, Luc De Raedt:
Planning in hybrid relational MDPs. Mach. Learn. 106(12): 1905-1932 (2017) - [c34]Vaishak Belle:
Open-Universe Weighted Model Counting: Extended Abstract. AAAI Workshops 2017 - [c33]Martin Mladenov, Vaishak Belle, Kristian Kersting:
The Symbolic Interior Point Method. AAAI 2017: 1199-1205 - [c32]Vaishak Belle:
Open-Universe Weighted Model Counting. AAAI 2017: 3701-3708 - [c31]Vaishak Belle, Gerhard Lakemeyer:
Reasoning about Probabilities in Unbounded First-Order Dynamical Domains. IJCAI 2017: 828-836 - [c30]Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc De Raedt:
Solving Probability Problems in Natural Language. IJCAI 2017: 3981-3987 - [c29]Vaishak Belle:
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds. IJCAI 2017: 5116-5120 - [c28]Vaishak Belle:
Weighted Model Counting With Function Symbols. UAI 2017 - 2016
- [j7]Vaishak Belle, Hector J. Levesque:
A Logical Theory of Localization. Stud Logica 104(4): 741-772 (2016) - [c27]Vaishak Belle:
Satisfiability and Model Counting in Open Universes. AAAI Workshop: Beyond NP 2016 - [c26]Vaishak Belle, Gerhard Lakemeyer, Hector J. Levesque:
A First-Order Logic of Probability and Only Knowing in Unbounded Domains. AAAI 2016: 893-899 - [c25]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Component Caching in Hybrid Domains with Piecewise Polynomial Densities. AAAI 2016: 3369-3375 - [c24]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report. IJCAI 2016: 4115-4119 - [c23]Vaishak Belle, Hector J. Levesque:
Foundations for Generalized Planning in Unbounded Stochastic Domains. KR 2016: 380-389 - [i5]Martin Mladenov, Vaishak Belle, Kristian Kersting:
The Symbolic Interior Point Method. CoRR abs/1605.08187 (2016) - [i4]Vaishak Belle, Luc De Raedt:
Semiring Programming: A Framework for Search, Inference and Learning. CoRR abs/1609.06954 (2016) - 2015
- [j6]Vaishak Belle, Gerhard Lakemeyer:
Semantical considerations on multiagent only knowing. Artif. Intell. 223: 1-26 (2015) - [j5]Vaishak Belle, Hector J. Levesque:
Robot location estimation in the situation calculus. J. Appl. Log. 13(4): 397-413 (2015) - [c22]Christian J. Muise, Vaishak Belle, Paolo Felli, Sheila A. McIlraith, Tim Miller, Adrian R. Pearce, Liz Sonenberg:
Planning Over Multi-Agent Epistemic States: A Classical Planning Approach. AAAI 2015: 3327-3334 - [c21]Guillaume Aucher, Vaishak Belle:
Multi-Agent Only Knowing on Planet Kripke. IJCAI 2015: 2713-2719 - [c20]Vaishak Belle, Gerhard Lakemeyer:
Only Knowing Meets Common Knowledge. IJCAI 2015: 2755-2761 - [c19]Vaishak Belle, Hector J. Levesque:
ALLEGRO: Belief-Based Programming in Stochastic Dynamical Domains. IJCAI 2015: 2762-2769 - [c18]Vaishak Belle, Andrea Passerini, Guy Van den Broeck:
Probabilistic Inference in Hybrid Domains by Weighted Model Integration. IJCAI 2015: 2770-2776 - [c17]Davide Nitti, Vaishak Belle, Luc De Raedt:
Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming. ECML/PKDD (2) 2015: 327-342 - [c16]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains. UAI 2015: 141-150 - 2014
- [j4]Vaishak Belle:
On the projection problem in active knowledge bases with incomplete information. AI Matters 1(2): 14-16 (2014) - [j3]Vaishak Belle, Gerhard Lakemeyer:
Multiagent Only Knowing in Dynamic Systems. J. Artif. Intell. Res. 49: 363-402 (2014) - [j2]Vaishak Belle:
Review of programming with higher-order logic by Dale Miller and Gopalan Nadathur. SIGACT News 45(2): 32-35 (2014) - [c15]Vaishak Belle, Hector J. Levesque:
PREGO: An Action Language for Belief-Based Cognitive Robotics in Continuous Domains. AAAI 2014: 989-995 - [c14]Christian J. Muise, Vaishak Belle, Sheila A. McIlraith:
Computing Contingent Plans via Fully Observable Non-Deterministic Planning. AAAI 2014: 2322-2329 - [c13]Vaishak Belle, Hector J. Levesque:
A Logical Theory of Robot Localization. AAAI Spring Symposia 2014 - [c12]Christian J. Muise, Sheila A. McIlraith, Vaishak Belle:
Non-Deterministic Planning With Conditional Effects. ICAPS 2014 - [c11]Vaishak Belle, Hector J. Levesque:
A logical theory of robot localization. AAMAS 2014: 349-356 - [c10]Vaishak Belle, Hector J. Levesque:
How to Progress Beliefs in Continuous Domains. KR 2014 - [c9]Vaishak Belle, Gerhard Lakemeyer:
On the Progression of Knowledge in Multiagent Systems. KR 2014 - [i3]Vaishak Belle, Hector J. Levesque:
Robot Location Estimation in the Situation Calculus. CoRR abs/1402.7276 (2014) - 2013
- [c8]Vaishak Belle, Hector J. Levesque:
Reasoning about Continuous Uncertainty in the Situation Calculus. IJCAI 2013: 732-738 - [c7]Vaishak Belle, Hector J. Levesque:
Reasoning about Probabilities in Dynamic Systems using Goal Regression. UAI 2013 - [i2]Vaishak Belle, Hector J. Levesque:
Reasoning about Probabilities in Dynamic Systems using Goal Regression. CoRR abs/1309.6816 (2013) - 2012
- [b1]Vaishak Belle:
On the projection problem in active knowledge bases with incomplete information. RWTH Aachen University, 2012, pp. 1-200 - 2011
- [j1]Vaishak Belle:
Review of from zero to infinity: what makes numbers interesting by Constance Reid. SIGACT News 42(2): 10-11 (2011) - [c6]Vaishak Belle, Gerhard Lakemeyer:
A Semantical Account of Progression in the Presence of Uncertainty. AAAI 2011: 165-170 - [c5]Vaishak Belle, Gerhard Lakemeyer:
On Progression and Query Evaluation in First-Order Knowledge Bases with Function Symbols. IJCAI 2011: 744-749 - 2010
- [c4]Vaishak Belle, Gerhard Lakemeyer:
Reasoning about Imperfect Information Games in the Epistemic Situation Calculus. AAAI 2010: 255-260 - [c3]Vaishak Belle:
Multi-Agent Only-Knowing Revisited. AlgoSyn 2010: 16 - [c2]Vaishak Belle, Gerhard Lakemeyer:
Multi-Agent Only-Knowing Revisited. KR 2010 - [i1]Vaishak Belle, Gerhard Lakemeyer:
Multi-Agent Only-Knowing Revisited. CoRR abs/1009.2041 (2010)
2000 – 2009
- 2008
- [c1]Vaishak Belle, Thomas Deselaers, Stefan Schiffer:
Randomized trees for real-time one-step face detection and recognition. ICPR 2008: 1-4
Coauthor Index
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last updated on 2024-10-07 22:20 CEST by the dblp team
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