David Poole - Publications

The following are the published books and papers by David Poole, by year.

2023

David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, third edition, Cambridge University Press, 2023.
Matthew Dirks, David Turner, and David Poole. Spectral sensor fusion for prediction of Li and Zr in rocks: Neural network and PLS methods. Chemometrics and Intelligent Laboratory Systems, Volume 240, 2023, 104915, doi: https://doi.org/10.1016/j.chemolab.2023.104915.
Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole: Knowledge Hypergraph Embedding Meets Relational Algebra, Journal of Machine Learning Research, 24(105):1−34, 2023.
Matthew Dirks, David Poole Auto-encoder neural network incorporating x-ray fluorescence fundamental parameters with machine learning, X-Ray Spectrometry 52(3) 142-150

2022

Matthew Dirks and David Poole, "Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruit", Chemometrics and Intelligent Laboratory Systems Volume 231, DOI: 10.1016/j.chemolab.2022.104685
David Poole and Frank Wood, "Probabilistic Programming Languages: Independent Choices and Deterministic Systems", in Geffner, H., Dechter, R., and Halpern, J.Y. (eds.) Probabilistic and Causal Inference: The Works of Judea Pearl, ACM Books 2022, pp. 691-712

2021

Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting and Sriraam Natarajan Structure learning for relational logistic regression: an ensemble approach Data Mining and Knowledge Discovery (2021). https://doi.org/10.1007/s10618-021-00770-8.
Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole Knowledge Hypergraph Embedding Meets Relational Algebra. arXiv:2102.09557 [cs.LG]
Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, and David Poole Introduction to Lifted Inference, MIT Press, 2021. Including the following chapters:
- Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, and David Poole, ``Statistical Relational AI: Representation, Inference and Learning''
- Angelika Kimmig and David Poole ``Modeling and Reasoning with Statistical Relational Representations''
- Seyed Mehran Kazemi, Guy Van den Broeck, and David Poole ``Search-Based Exact Lifted Inference''
- Wannes Meert, Jaesik Choi, Jacek Kisynski, Hung Bui, Guy Van den Broeck, Adnan Darwiche, Rodrigo de Salvo Braz, and David Poole, ``Lifted Aggregation and Skolemization for Directed Models''
- Seyed Mehran Kazemi, Guy Van den Broeck, and David Poole, ``First-order Knowledge Compilation''
- Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck, and David Poole ``Domain Liftability''

2020

Gioachino Roberti, Jacob McGregor, Sharon Lam, David Bigelow, Blake Boyko, Chris Ahern, Victoria Wang, Bryan Barnhart, Clinton Smyth, David Poole, and Stephen Richard INSPIRE standards as a framework for artificial intelligence applications: a landslide example, Nat. Hazards Earth Syst. Sci., 20, 3455–3483,https://doi.org/10.5194/nhess-20-3455-2020, 2020.
Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole Knowledge Hypergraphs: Prediction Beyond Binary Relations, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI) Main track. Pages 2191-2197. https://doi.org/10.24963/ijcai.2020/303 And Paper with Supplementary Material.
Ainaz Hajimoradlou, Gioachino Roberti, and David Poole Predicting Landslides Using Locally Aligned Convolutional Neural Networks Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Main track. Pages 3342-3348. https://doi.org/10.24963/ijcai.2020/462. Dataset .

David Poole, Ali Mohammad Mehr, Wan Shing Martin Wang , Conditioning on "and nothing else": Simple Models of Missing Data between Naive Bayes and Logistic Regression, ICML Workshop on the Art of Learning with Missing Values (Artemiss)
Chenliang Zhou, Dominic Kuang, Jingru Liu, Hanbo Yang, Zijia Zhang, Alan Mackworth and David Poole, AISpace2: An Interactive Visualization Tool for Learning and Teaching Artificial Intelligence, EAAI-20: The 10th Symposium on Educational Advances in Artificial Intelligence, New York, February.

2019

Bahare Fatemi, Siamak Ravanbakhsh, and David Poole, Improved Knowledge Graph Embedding using Background Taxonomic Information, Proc. Thirty-Third AAAI Conference on Artificial Intelligence, January 2019.
Bahare Fatemi, Perouz Taslakian, David Vazquez and David Poole. Knowledge Hypergraphs: Prediction Beyond Binary Relations, arXiv:1906.00137 [cs.LG]. In New Trends in Representation Learning with Knowledge Graphs ECML PKDD Workshop 2019 and StaRAI workshop at AAAI-2020.
Matthew Dirks and David Poole, Incorporating Domain Knowledge About XRF Spectra into Neural Networks, Workshop on Perception as Generative Reasoning, NeurIPS 2019, Vancouver.

2018

Seyed Mehran Kazemi and David Poole, SimplE Embedding for Link Prediction in Knowledge Graphs, in Proc. Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018). Also a previous version in Eighth International Workshop on Statistical Relational AI, July 2018.
Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting and Sriraam Natarajan Structure Learning for Relational Logistic Regression:An Ensemble Approach in Proc. 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018).
Sanjana Bajracharya, Giuseppe Carenini, Brent Chamberlain, Kai Di Chen, Daniel Klein, David Poole and Gunilla Oberg, Interactive Visualization for Group Decision-Analysis, International Journal of Information Technology & Decision Making, Vol. 17, No. 06, pp. 1839-1864 (2018) doi: 10.1142/S0219622018500384 preprint.
Bahare Fatemi, Seyed Mehran Kazemi, David Poole Record Linkage to Match Customer Names: A Probabilistic Approach, in Eighth International Workshop on Statistical Relational AI Stockholm, July 2018.
Seyed Mehran Kazemi and David Poole, Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models in Frontiers in Robotics and AI, Vol 5, 2018. Preprint.
Seyed Mehran Kazemi and David Poole, RelNN: A Deep Neural Model for Relational Learning, in Proc. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18).

2017

David Poole and Alan Mackworth, Artificial Intelligence: foundations of computational agents, second edition, Cambridge University Press, 2017.
David Buchman and David Poole, Why Rules are Complex: Real-Valued Probabilistic Logic Programs are not Fully Expressive in 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017. Winner of best student paper award at UAI-2017.
Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck and David Poole, Domain Recursion for Lifted Inference with Existential Quantifiers, in UAI Workshop on Statistical Relational AI, August 2017.
Seyed Mehran Kazemi, Bahare Fatemi, Alexandra Kim, Zilun Peng, Moumita Roy Tora, Xing Zeng, Matthew Dirks and David Poole, Comparing Aggregators for Relational Probabilistic Models, in UAI Workshop on Statistical Relational AI, August 2017.
David Buchman and David Poole. Negative probabilities in probabilistic logic programs in International Journal of Approximate Reasoning, Volume 83, April 2017, Pages 43-59

2016

Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck and David Poole, 2016. New Liftable classes for first-order probabilistic inference, in Advances in Neural Information Processing Systems 29 (NIPS 2016). Extended version, video.
Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole, Statistical Relational Artificial Intelligence: Logic, Probability, and Computation, Morgan & Claypool Publishers, March, 2016. DOI: 10.2200/S00692ED1V01Y201601AIM032
Bahare Fatemi, Seyed Mehran Kazemi and David Poole A Learning Algorithm for Relational Logistic Regression: Preliminary Results, IJCAI Statistical Relational AI Workshop, New York, July 2016.
Seyed Mehran Kazemi and David Poole, Why is Compiling Lifted Inference into a Low-Level Language so Effective?, IJCAI Statistical Relational AI Workshop, New York, July 2016.
Thomas Lukasiewicz, Maria Vanina Martinez, David Poole and Gerardo I. Simari, Probabilistic Models over Weighted Orderings: Fixed-Parameter Tractable Variable Elimination, in Proc. 15th International Conference on Principles of Knowledge Representation and Reasoning (KR-2016), Cape Town, South Africa, April, 2016.
Seyed Mehran Kazemi and David Poole, Knowledge Compilation for Lifted Probabilistic Inference: Compiling to a Low-Level Language, short paper in Proc. 15th International Conference on Principles of Knowledge Representation and Reasoning (KR-2016), Cape Town, South Africa, April, 2016.
David Buchman and David Poole, Negation Without Negation in Probabilistic Logic Programming, short paper in Proc. 15th International Conference on Principles of Knowledge Representation and Reasoning (KR-2016), Cape Town, South Africa, April, 2016.
Matthew Dirks, Andrew Csinger, Andrew Bamber and David Poole Representation, Reasoning, and Learning for a Relational Influence Diagram Applied to a Real-Time Geological Domain in 29th Canadian Conference on Artificial Intelligence.
Seyed Mehran Kazemi and David Poole, Lazy Arithmetic Circuits, in AAAI-16 Workshop on Beyond NP, Phoenix, Arizona, USA, February 2016.
Andrew Bamber, Andrew Csinger, David Poole, High capacity cascade-type mineral sorting machine and method, United States Patent US 9,314,823 B2, April 2016.

2015

David Buchman and David Poole, Representing Aggregators in Relational Probabilistic Models, in Proc. Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15). And the paper with appendix.

2014

David Poole, David Buchman, Seyed Mehran Kazemi, Kristian Kersting and Sriraam Natarajan Population Size Extrapolation in Relational Probabilistic Modelling in U. Straccia and A. Cali (Eds.): Proc. 8th International Conference on Scalable Uncertainty Management (SUM 2014), LNAI 8720, pp. 292–305, 2014.
Seyed Mehran Kazemi and David Poole, Elimination Ordering in Lifted First-Order Probabilistic Inference, in Proc. Twenty-Eighth AAAI Conference (AAAI-2014), Québec City, pp. 863-870.
Seyed Mehran Kazemi, David Buchman, Kristian Kersting, Sriraam Natarajan, and David Poole, Relational Logistic Regression, in Proc. 14th International Conference on Principles of Knowledge Representation and Reasoning (KR-2014). Also Relational Logistic Regression: the Directed Analog of Markov Logic Networks in Proc. AAAI-2014 Statistical Relational AI Workshop.
Matthew Dirks, Andrew Csinger, Andrew Bamber and David Poole. Representation, Reasoning and Inference for a Relational Open-World Influence Diagram Applied to a Real-Time Geological Domain, in Proc. AAAI-2014 Statistical Relational AI Workshop.
Brent C. Chamberlain, Giuseppe Carenini, Gunilla Öberg, David Poole, and Hamed Taheri, A Decision Support System for the Design and Evaluation of Sustainable Wastewater Solutions, IEEE Transactions on Computers, Special Issues on Computational Sustainability, 63(1), 2014.

2013

Chia-Li Kuo and David Poole, On Integrating Ontologies with Relational Probabilistic Models, in Proc. AAAI-2013 StarAI Workshop.
Chia-Li Kuo, David Buchman, Arzoo Katiyar and David Poole, Probabilistic Reasoning with Undefined Properties in Ontologically-Based Belief Networks (with appendix), in Proc. 23rd International Joint Conference on Artificial Intelligence (IJCAI-2013), Beijing, August, pp. 2532-2539.
David Poole and Mark Crowley, Cyclic Causal Models with Discrete Variables: Markov Chain Equilibrium Semantics and Sample Ordering, in Proc. 23rd International Joint Conference on Artificial Intelligence (IJCAI-2013), Beijing, August, pp. 1060-1068.
David Poole, Foundations of model construction in feature-based semantic science, Journal of Logic and Computation 23 (5): 1081-1096 (2013); doi: 10.1093/logcom/exs046.

2012

David Poole, David Buchman, Sriraam Natarajan, and Kristian Kersting, Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases, Proc. UAI-2012 Statistical Relational AI (StarAI) Workshop.
Michael Chiang and David Poole, A Search Algorithm for Latent Variable Models with Unbounded Domains, Proc. Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12).
Chamberlain, B., Zarei, A., Taheri, H., Poole, D., Carenini, G. and Öberg, G. Designing Sustainable Wastewater Systems: Generating Design Alternatives. 3rd International Conference on Computational Sustainability, Copenhagen, Denmark, July 2012.
Chamberlain, B., Taheri, H., Carenini, G., Poole, D., and Öberg, G. Designing Sustainable Wastewater Systems: Visual, Interactive Preference Elicitation. 3rd International Conference on Computational Sustainability, Copenhagen, Denmark, July 2012.
Stephen Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter Flach, Katsumi Inoue and Ashwin Srinivasan, ILP turns 20: Biography and future challenges, Machine Learning, 86:3-23.
Michael Chiang and David Poole, Reference Classes and Relational Learning, International Journal of Approximate Reasoning, Volume 53, Issue 3, Pages 326–346 DOI: 10.1016/j.ijar.2011.05.002, April 2012.
David Buchman, Mark Schmid, Shakir Mohamed, David Poole and Nando de Freitas. On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models, Proc. AI and Statistics 2012.

2011

Mark Crowley and David Poole, Policy Gradient Planning for Environmental Decision Making with Existing Simulators, Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI-11), special track on Computational Sustainability and AI, San Francisco, August 2011.
David Poole, AILog 2, in de Raedt et al. (Eds) Probabilistic Prolog Systems, ALP Issue, June 2011.
David Poole, Logic, Probability and Computation: Foundations and Issues of Statistical Relational AI, invited paper, 11th International Conference on Logic Programming and Nonmonotonic Reasoning, Vancouver, May 2011.
David Poole, Probabilistic Relational Learning and Inductive Logic Programming at a Global Scale, Proc. 20th International Conference on Inductive Logic Programming (ILP 2010), 2010, LNAI 6489
David Poole, Fahiem Bacchus, Jacek Kisynski, Towards Completely Lifted Search-based Probabilistic Inference, arXiv:1107.4035 [cs.AI].

2010

David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010.
David Poole, Towards a Logic of Feature-Based Semantic Science Theories, short paper in Proceedings of the Twelfth International Conference on the Principles of Knowledge Representation and Reasoning (KR 2010), pages 587-589.
David Poole, Probabilistic Programming Languages: Independent Choices and Deterministic Systems, in Heuristics, Probability and Causality: A Tribute to Judea Pearl, edited by R. Dechter, H. Geffner and J.Y. Halpern, College Publications, 2010, pages 253-269.
Rita Sharma, David Poole and Clinton Smyth, A Framework for Ontologically-Grounded Probabilistic Matching, International Journal of Approximate Reasoning, 51(2), 240-262, January 2010.
M. Fox, D. Poole, editors, Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), Atlanta, USA, July 11 - 15 2010. AAAI Press.

2009

David Poole, Clinton Smyth and Rita Sharma, Ontology Design for Scientific Theories That Make Probabilistic Predictions, IEEE Intelligent Systems, Special Issue on Semantic Scientific Knowledge Integration - Jan/Feb 2009, pages 27-36
Jacek Kisynski and David Poole, Lifted Aggregation in Directed First-order Probabilistic Models, Proc. Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09), Pasadena, California, 2009, pages 1922-1929.
Jacek Kisynski and David Poole, Constraint Processing in Lifted Probabilistic Inference, Proc. 25th Conference on Uncertainty in AI, (UAI-2009), Montreal, Canada, June 2009, pages 293-302.
Mark Crowley, John Nelson and David Poole, Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal Decision Making, Proc. 25th Conference on Uncertainty in AI, (UAI-2009), Montreal, Canada, June 2009, pages 126-134.
David Poole, Towards an abductive foundation of semantic science, ICJAI'09 Workshop on Abductiv e and Inductive Knowledge Development (AIAI-09), July 2009.

2008

Saleema Amershi, Giuseppe Carenini, Cristina Conati, Alan K. Mackworth and David Poole, Pedagogy and Usability in Interactive Algorithm Visualizations Designing and Evaluating CIspace, Interacting with Computers, Volume 20, Issue 1, January 2008, Pages 64-96.
David Poole, Clinton Smyth, Rita Sharma "Semantic Science: Ontologies, Data and Probabilistic Theories" in Paulo C.G. da Costa, Claudia d'Amato, Nicola Fanizzi, Kathryn B. Laskey, Ken Laskey, Thomas Lukasiewicz, Matthias Nickles, and Mike Pool (Eds.), Uncertainty Reasoning for the Semantic Web I, Springer LNAI/LNCS, 2008.
David Poole, "The Independent Choice Logic and Beyond", in Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton (eds), Probabilistic Inductive Logic Programming: Theory and Application, LNAI 4911, 2008.
Brian Knoll, Jacek Kisynski, Giuseppe Carenini, Cristina Conati, Alan Mackworth and David Poole, "AIspace: Interactive Tools for Learning Artificial Intelligence". In Proceedings of the AAAI 2008 AI Education Colloquium. July 2008.
David Poole, Clinton Smyth and Rita Sharma, Semantic Science and Machine-Accessible Scientific Theories, AAAI Spring Symposium on Semantic Science Knowledge Integration, Stanford, March 2008.
Lionel E. Jackon, Jr., Clinton P. Smyth and David Poole, HazardMatch: An application of artificial intelligence to landslide susceptibility mapping, Howe Sound area, British Columbia, in J. Locat, D. Perret, D. Turmel, D. Demers et S. Leroueil (eds.) Proceedings of the 4th Canadian Conference on Geohazards : From Causes to Management. Presse de l’Université Laval, Québec, 2008.

2007

David Poole, "Logical Generative Models for Probabilistic Reasoning about Existence, Roles and Identity", 22nd AAAI Conference on AI (AAAI-07), July 2007.
Mark Crowley, Brent Boerlage and David Poole, ``Adding Local Constraints to Bayesian Networks'', 20th Canadian Conference on Artificial Intelligence, May 2007.
Clinton Smyth, David Poole and Rita Sharma, ``Semantic e-Science and Geology'', AAAI-07 Semantic e-Science workshop, 2007.
Rita Sharma, David Poole and Clinton Smyth, ``A System of Ontologically-Grounded Probabilistic Matching'', Uncertainty in AI, Applications Workshop, July 2007.
Michael Chiang and David Poole, ``Dynamic Predicate Construction for Learning Relational Concepts'', Work-in-Progress Proceedings of the 2007 International Conference on Inductive Logic Programming, June 2007.

2006

David Poole and Alan Mackworth, ``Dimensions of Complexity of Intelligent Agents'', International Symposium on Practical Cognitive Agents and Robots, Perth, November 2006.
David Poole, Agents, Decisions, Beliefs, Preferences, Science and Politics, AAAI Fellows Symposium, Cambridge, July 2006.

2005

Rita Sharma and David Poole, ``Probabilistic Reasoning with Hierarchically Structured Variables'', Proc. Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05), Edinburgh, August 2005.
Peter Carbonetto, Jacek Kisynski, Nando de Freitas and David Poole. ``Nonparametric Bayesian Logic'', Proc. 21st Conference on Uncertainty in Artificial Intelligence (UAI 2005), Edinburgh July 2005.
David Poole and Clinton Smyth, ``Type Uncertainty in Ontologically-Grounded Qualitative Probabilistic Matching'', Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU-2005), Barcelona, July 2005.
Saleema Amershi, Nicole Arksey, Giuseppe Carenini, Cristina Conati, Alan Mackworth, Heather Maclaren, and David Poole, ``Designing CIspace: Pedagogy and Usability in a Learning Environment for AI'', Tenth Annual Conference on Innovation and Technology in Computer Science Education, Lisbon, Portugal, June 27-29, 2005.
Rita Sharma and David Poole, ``Probability and Equality: A Probabilistic Model of Identity Uncertainty'', Eighteenth Canadian Conference on Artificial Intellegence, Victoria, May 2005. Extended version, UBC CS Technical Report TR-2005-02.

2004

Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger Hoos, and David Poole "CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements", Journal of AI Research, Volume 21, pages 135-191, 2004. Winner of IJCAII-JAIR Best Paper Prize, 2009.
Craig Boutilier, Ronen I. Brafman, Carmel Domshlak, Holger Hoos, and David Poole, Preference-Based Constrained Optimization with CP-nets,Computational Intelligence, 20(2), Special Issue on Preferencesin Artificial Intelligence and Constraint Processing, 137-157, May 2004.
Nando de Freitas, Richard Dearden, Frank Hutter, Ruben Morales-Menendez, Jim Mutch and David Poole. Diagnosis by a waiter and a Mars explorer. Invited paper for Proceedings of the IEEE, special issue on sequential state estimation, 94(3), 455-468, March 2004..
Clinton Smyth and David Poole, Qualitative Probabilistic Matching with Hierarchical Descriptions, Ninth International Conference on the Principles of Knowledge Representation and Reasoning (KR-2004), June 2004.

2003

David Poole and Nevin Lianwen Zhang, ``Exploiting contextual independence in probabilistic inference'', Journal of Artificial Intelligence Research, 18, 263-313, 2003.
David Poole, First-order probabilistic inference, Proc, IJCAI-03, Acapulco, August 2003, pp. 985-991.
Rita Sharma and David Poole, Efficient Inference in Large Discrete Domains in UAI-2003, Acapulco, August 2003, pp. 535-542.
Ruben Morales-Menendez, Nando de Freitas, and David Poole, Estimation and control of industrial processes with particle filters, American Control Conference, Denver Colorado, June 2003.
Giuseppe Carenini, Jocelyn Smith and David Poole, Towards more Conversational and Collaborative Recommender Systems, Proc. 2003 International Conference of Intelligent User Interfaces (IUI-2003), Miami, January 2003.

2002

Ruben Morales-Menendez, Nando de Freitas, and David Poole, Real-time monitoring of complex industrial processes with particle filters, Proc. Neural Information Processing Systems (NIPS), Vancouver, December 2002.

2001

Rita Sharma and David Poole, Symmetric Collaborative Filtering using the Noisy Sensor Model in Proc. UAI-2001, Seattle, 2001
D. Poole, ``Logical Argumentation, Abduction and Bayesian Decision Theory: A Bayesian Approach to Logical Arguments and its Application to Legal Evidential Reasoning'', Cardozo Law Review, 22 (5-6), October 2001. Reprinted in Tillers and MacCrimmon (Ed.) Dynamics of Judicial Proof: Computation, Logic, and Common Sense Springer-Verlag, 2002.

2000

David Poole, "Learning, Bayesian Probability, Graphical Models, and Abduction", Peter Flach and Antonis Kakas, editors, Abduction and Induction: essays on their relation and integration, Kluwer, 2000, 153--168.
David Poole, ``Abducing Through Negation as Failure: Stable models within the independent choice logic '' Journal of Logic Programming, Vol 44, Pages 5-35, 2000.
David Poole, ``Logic, Knowledge Representation and Bayesian Decision Theory, Invited paper, First International Conference on Computational Logic (CL2000), London, July 2000.
Peter Gorniak and David Poole, Predicting Future User Actions by Observing Unmodified Applications, Seventeenth National Conference on Artificial Intelligence (AAAI-2000), August 2000.
Peter Gorniak and David Poole, Building a Stochastic Dynamic Model of Application Use, roc. Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000), July 2000.
David Poole, Logical Argumentation, Abduction and Bayesian Decision Theory: A Bayesian Approach to Logical Arguments and its Application to Legal Evidential Reasoning, invited talk, Cardozo Conference on AI and Judicial Proof, New York, Aprol 2000.

1999

Nevin Lianwen Zhang and David Poole, On the role of context-specific independence in Probabilistic Reasoning , Proc. Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, pages 1288--1293, August 1999.
Craig Boutilier, Ronen Brafman, Holger Hoos, and David Poole, ``Reasoning With Conditional Ceteris Paribus Preference Statements'', in Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Stockholm, Sweden 1999.
Michael C. Horsch and David Poole, ``Estimating the Value of Computation in Flexible Information Refinement'', in Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Stockholm, Sweden 1999.

1998

David Poole, Alan Mackworth, and Randy Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, 1998.
David Poole, ``Decision Theory, the Situation Calculus and Conditional Plans'', Linköping Electronic Articles in Computer and Information Science, Vol 3 (1998):nr 8. http://www.ep.liu.se/ea/cis/1998/008/ June 15, 1998. The Electronic Transactions on Artificial Intelligence.
David Poole, ``Context-specific approximation in probabilistic inference'', Proc. Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), Madison, Wisconsin, pages 447-454, July 1998.
Michael C. Horsch and David Poole, ``An Anytime Algorithm for Decision Making under Uncertainty'', Proc. Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), Madison, Wisconsin, pages 246-255, July 1998.

1997

David Poole, ``The Independent Choice Logic for modelling multiple agents under uncertainty'', Artificial Intelligence, 94(1-2), special issue on economic principles of multi-agent systems, pages 7-56, 1997.
David Poole, ``Probabilistic Partial Evaluation: Exploiting rule structure in probabilistic inference'', Proc. Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), Nagoya, Japan, August 1997, pp. 1284-1291.
David Poole, ``Multi-agent actions under uncertainty: situation caclulus, discrete time, plans and policies'', IJCAI-97 Workshop on Nonmonotonic Reasoning, Action, and Change, Nagoya, Japan, August 1997, pp. 139-158.
C. Boutilier, R. Brafman, C. Geib and David Poole, ``A Constraint-Based Approach to Preference Elicitation and Decision Making'', AAAI Spring Symposium on Qualitative Decision Theory, Stanford, March 1997.
David Poole, ``Who chooses the assumptions?'', in P. O'Rorke (Ed.) Abduction, AAAI/MIT Press, forthcoming, 1997. (This book was never published, but here is a draft of the paper I wrote).

1996

David Poole, ``Probabilistic conflicts in a search algorithm for estimating posterior probabilities in Bayesian networks'', Artificial Intelligence, 88, 69-100, 1996.
Nevin Lianwen Zhang and David Poole, ``Exploiting Causal Independence in Bayesian Network Inference'', Journal of Artificial Intelligence Research, 5, 301-328, 1996.
Andrew Csinger and David Poole, ``Adapting Decision Theory for Multimedia Presentation Design: User Modelling for Intent-based Authoring'', Proc. Twelfth European Conference on Artificial Intelligence (ECAI-96), Budapest, August 1996.
Craig Boutilier and David Poole, ``Computing Optimal Policies for Partially Observable Decision Processes using Compact Representations'', Proc. Thirteenth National Conference on Artificial Intelligence (AAAI-96), Portland, Oregon, August 1996.
David Poole, ``A Framework for Decision-Theoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility'', Proc. Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Portland, Oregon, August 1996.
Michael Horsch and David Poole, "Flexible Policy Construction by Information Refinement", Proc. Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Portland Oregon, August 1996.
Andrew Csinger and David Poole, ``User Models and Perceptual Salience: Formal Abduction for Model Recognition and Presentation Design'', Proc. Fifth International Conference on User Modeling, Kona, HI, 51-58, 1996.
M. Horsch and David Poole, ``Flexible Construction of Decision Functions using Information Refinement'', Proc AAAI Fall Symposium on Felxible Compuation in Intelligent Systems: Results, Issues and Opportunities, MIT, Cambridge, Mass, November 1996, 68-72.

1995

Runping Qi and David Poole, ``A New Method for Influence Diagram Evaluation'', Computational Intelligence, 11(3), 498-528, 1995.
Andrew Csinger, Kelly S. Booth, David Poole, ``AI Meets Authoring: User Models for Intelligent Multimedia'', Artificial Intelligence Review, special issue on user modelling, 8, 447-468, 1995.
David Poole, ``Logic Programming for Robot Control'', Proc. Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, August 1995, 150-157.
David Poole, ``Exploiting the Rule Structure for Decision Making within the Independent Choice Logic'', Proceedings of the Eleventh Conference on Uncertainty in AI (UAI-95), Montreal, August 1995, 454-463.
David Poole, ``Sensing and Acting in the Independent Choice Logic'', Working Notes AAAI Spring Symposium 1995 -- Extending Theories of Actions: Formal Theory and Practical Applications, Stanford, March 1995, 163-168.

1994

Ramon Lopez de Mantaras and David Poole (Eds), Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94),Morgan Kaufmann, 1994.
David Poole, ``Representing Diagnosis Knowledge'', Annals of Mathematics and Artificial Intelligence, 11, 33-50, 1994.
Nevin Lianwen Zhang, R. Qi and David Poole, ``A Computational Theory of Decision Networks'', International Journal of Approximate Reasoning, 11(2), 83-158, 1994.
Nevin Lianwen Zhang and David Poole, ``Intercausal independence and heterogeneous factorization'', Proceedings of the Tenth Conference on Uncertainty in AI (UAI-94), Seattle, July 1994, 606-614.
Runping Qi, Nevin Lianwen Zhang and David Poole, ``Solving Asymmetric Decision Problems with Influence Diagrams'', Proceedings of the Tenth Conference on Uncertainty in AI (UAI-94), Seattle, July 1994, 491-497.
Nevin Lianwen Zhang and David Poole, ``A simple approach to Bayesian network computations'', Proceedings of the Tenth Biennial Canadian Artificial Intelligence Conference (AI-94), Banff, May 1994, 171-178.
David Poole, ``Default Logic'', in D. M. Gabbay, C. J. Hogger J. A. Robinson (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, Volume 3, Oxford University Press, 1994, 189-215.
David Poole and K. Kanazawa, ``A decision-theoretic abductive basis for planning'', Proc. AAAI Spring Symposium on Decision-Theoretic Planning, Stanford University, March 1994, 232-239.

1993

David Poole, ``Probabilistic Horn abduction and Bayesian networks'', Artificial Intelligence, 64(1), 81-129, 1993.
Yang Xiang, David Poole and M. P. Beddoes, ``Multiply sectioned Bayesian networks and junction forests for large knowledge-based systems'', Computational Intelligence 9(2), 171-220, May 1993.
Yang Xiang, B. Pant, A. Eisen, M. P. Beddoes and David Poole, ``Multiply Sectioned Bayesian Networks for Neuromuscular Diagnosis'', Artificial Intelligence in Medicine, 5(4) 293-314, 1993.
David Poole, ``Logic Programming, Abduction and Probability: a top-down anytime algorithm for estimating prior and posterior probabilities'', New Generation Computing, 11(3-4), 377-400, 1993.
David Poole, ``Probabilistic Conflicts'', Fourth International Workshop on the Principles of Diagnosis, pp. 178-186, Aberystwyth, Wales, September 1993.
David Poole, ``Average-case analysis of a search algorithm for estimating prior and posterior probabilities in Bayesian networks with extreme probabilities'', Proc. Thirteenth International Joint Conference on Artificial Intelligence (IJCAI), pages 606-612, France, August 1993.
Andrew Csinger and David Poole, ``Hypothetically Speaking: Default Reasoning and Discourse Structure'', Proc. Thirteenth International Joint Conference on Artificial Intelligence (IJCAI), pages 1179-1184, France, August 1993.
David Poole, ``The Use of Conflicts in Searching Bayesian Networks'', Proceedings of the Ninth Conference on Uncertainty in AI (UAI-93), Washington D.C., pages 359-367, July 1993.
N.L. Zhang, R. Qi and David Poole, ``Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams'', Proceedings of the Ninth Conference on Uncertainty in AI (UAI-93), Washington D.C., pages 400-407, July 1993.
N.L. Zhang, R. Qi and David Poole, ``Minimizing Decision Table Sizes in Stepwise-decomposable Influence Diagrams'', Proc. Fourth International Workshop on Artificial Intelligence and Statistics, Ft. Lauderdale, Florida, pages 423-432, January 1993.

1992

David Poole, ``Efficient Model-based Diagnosis: Searching in Bayesian Networks'', Proceedings of the Third International Workshop on Principles of Diagnosis, Rosario, Washington, October 1992.
N.L. Zhang and David Poole, ``Stepwise-Decomposable Influence Diagrams'', Proceedings of the Third International Conference on the Principles of Knowledge Representation and Reasoning (KR-1992), Cambridge, Mass., pages 141-152, October 1992.
R. Qi and David Poole, ``Two Algorithms for Decision Tree Search'', Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence, Seoul, Korea, pages 121-127, September 1992.
R. Qi and David Poole, ``A Framework for U-graph Based Path Planning'', Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence, Seoul, Korea, pages 287-293, September 1992.
Y. Xiang, David Poole and M. P. Beddoes, ``Exploring Locality in Bayesian Networks for Large Expert Systems'', Proceedings of the Eighth Conference on Uncertainty in AI (UAI-92), pages 344-351, Stanford, July 1992.
L. Zhang and David Poole, ``Sidestepping the Triangulation Problem in Bayesian Net Computation'', Proceedings of the Eighth Conference on Uncertainty in AI (UAI-92), pages 360-367, Stanford, July 1992.
David Poole, ``Logic Programming, Abduction and Probability'', Proceedings of the International Conference on Fifth Generation Computer Systems (FGCS'92), pages 530-538, Tokyo, June 1992.
David Poole, ``Decision-theoretic Defaults'', Proceedings of the Ninth Biennial Canadian Artificial Intelligence Conference (CSCSI-92), pages 190-197, Vancouver, May 1992.

1991

David Poole, ``The effect of knowledge on belief: conditioning, specificity and the lottery paradox in default reasoning'', Artificial Intelligence, 49, 281-307, 1991. Republished in R. J. Brachman, H. J. Levesque and R. Reiter (Eds.), Knowledge Representation, MIT Press, 1991.
David Poole, ``Compiling a default reasoning system into Prolog'', New Generation Computing, 9(1), 3-38, 1991.
Y. Xiang, B. Pant, A. Eisen, M.P.Beddoes, David Poole, ``PAINULIM: A Neuromuscular Diagnostic Aid Using Multiply Sectioned Bayesian Networks'', Proc. of the ISMM International Conference on Mini and Microcomputers in Medicine and Healthcare, Long Beach, CA, Dec. 1991, pp. 64-69.
David Poole and G. Provan, ``Use and Granularity in Consistency-based Diagnosis'', Proc. Second International Workshop on the Principles of Diagnosis, Milan, October 1991, pp. 1-10.
David Poole, ``Representing Diagnostic Knowledge for Probabilistic Horn Abduction'', Proc. Twelfth International Joint Conference on Artificial Intelligence (IJCAI), Sydney, Australia, August 1991, pp. 1129-1135. Reprinted in W. Hamscher, L. Console and J. de Kleer (Eds.), Readings in Model-based Diagnosis, Morgan Kaufmann,, 1992.
N. Helft, K. Inoue and David Poole, ``Answer Extraction in Circumscription'', Proc. Twelfth International Joint Conference on Artificial Intelligence (IJCAI), Sydney, Australia, August 1991, pp. 426-431.
David Poole, ``Representing Bayesian Networks within Probabilistic Horn Abduction'', Proceedings of the Seventh Conference on Uncertainty in AI (UAI-91), Los Angeles, July 1991, pp. 271-278.
R. Qi and David Poole, ``Path Planning under Uncertainty'', Proceedings of the Seventh Conference on Uncertainty in AI (UAI-91), Los Angeles, July 1991, pp. 287-294.
G. Provan and David Poole, ``The Utility of Consistency-Based Diagnostic Techniques'', Proc. Second International Conference on Principles of Knowledge Representation and Reasoning (KR-1991), Cambridge, Mass., April 1991, pp. 461-472.

1990

David Poole, ``A methodology for using a default and abductive reasoning system'', International Journal of Intelligent Systems, 1990, 5(5), 521-548, 1990.
David Poole and G. Provan, ``What is an optimal diagnosis?'', Proceedings of the Sixth Conference on Uncertainty in AI (UAI-90), Boston, July 1990, pp. 46-53. Revised version published as ``What is the Most Likely Diagnosis?'', in P. P. Bonissone, M. Henrion, L. N. Kanal and J. Lemmer (Eds.) Uncertainty in Artificial Intelligence VI, Elsevier, North Holland, 1991.
M. Horsch and David Poole, ``A dynamic approach to probabilistic inference using Bayesian networks'', Proceedings of the Sixth Conference on Uncertainty in AI (UAI-90), Boston, July 1990, pp. 155-161.
David Poole, ``Dialectics and Specificity: Conditioning in Logic-based hypothetical reasoning'',Proceedings of the Eighth Biennial Conference of the Canadian Society for Computational Studies of Intelligence (CSCSI-90), Ottawa, May 1990, pp. 69-76. () Revised version in Proceedings of the Third International Workshop on Nonmonotonic Reasoning, California, June 1990, pp. 201-208.
Y. Xiang, M. P. Beddoes and David Poole, ``Sequential Updating of conditional probability in Bayesian networks by posterior probability'' Proceedings of the Eighth Biennial Conference of the Canadian Society for Computational Studies of Intelligence (CSCSI-90), Ottawa, May 1990, pp. 21-27.
David Poole, ``Formal Logic'', in Standards and Review Manual for Certification in Knowledge Engineering, Handbook of Theory and Practice, International Association for Knowledge Engineers, Systemsware Corporation (Publisher), July 1990.
David Poole, ``Hypo-deductive reasoning for abduction, default reasoning and design'', Proc. AAAI Spring Symposium on Automated Abduction, Stanford University, March 1990.
N. Helft, K. Inoue and David Poole, ``Extracting answers in circumscription'', ICOT Technical Memorandum TM-855, ICOT, 1989. Also in First Workshop on Defeasible Reasoning and Uncertainty Management Systems, Marseille, February, 1990.

1989

David Poole, ``Explanation and Prediction: An Architecture for Default and Abductive Reasoning'',Computational Intelligence 5(2), 97-110, 1989.
A. Csinger and David Poole, ``From Utterance to Belief via Presupposition: Default Reasoning in User-Modelling'', Proc. Conference on Knowledge Based Computer Systems -- KBCS-89, 408-419, Bombay, India, December 1989. Reprinted in S. Ramani, R. Chandrasekar and K. S. R. Anjaneyulu (Eds.) Knowledge Based Computer systems, Lecture Notes in AI, Volume 444, Springer Verlag, 1989.
D. L. Poole, ``Normality and Faults in Logic-Based Diagnosis'', Proceedings Eleventh International Joint Conference on Artificial Intelligence (IJCAI), Detroit, August 1989, pp. 1304-1310. Reprinted in W. Hamscher, L. Console and J. de Kleer (Eds.), Readings in Model-based Diagnosis, Morgan Kaufmann, forthcoming, 1992.
Y. Xiang, M. P. Beddoes and David Poole, ``Can uncertainty management be realized in a finite totally ordered probability algebra?'', Fifth Workshop on Uncertainty in Artificial Intelligence (UAI-89), Windsor Ontario, August 1989, pp. 385-393. Revised version in Uncertainty in Artificial Intelligence: Volume V, M. Henrion, R. Shacter, L. N. Kanal and J. Lemmer (Eds), Elsevier, North-Holland, 41-57, 1990.
D. L. Poole, ``What the Lottery Paradox tells us about default reasoning'', Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning (KR-1989), Toronto, May 1989, pp. 333-340.
David Poole, ``Minimalist AI and the Theorist conjecture'', AAAI Workshop on Defeasible Reasoning with Specificity and Multiple Inheritance, St. Louis, April 1989.

1988

David Poole, ``A Logical Framework for Default Reasoning'', Artificial Intelligence, 36(1), 27-47, 1988.
D. L. Poole, ``Representing Knowledge for Logic-based Diagnosis'', Proc. International Conference on Fifth Generation Computer Systems 1988, Tokyo, November 1988, pp. 1282-1290.
E. M. Neufeld and D. L. Poole, ``Probabilistic Semantics and Defaults'', Proceedings of the Fourth Workshop Uncertainty in Artificial Intelligence (UAI-88), University of Minnesota, August 1988, pp. 275-282. Revised version as E. M. Neufeld, David Poole and R. Aleliunas, ``Probabilistic Semantics and Defaults'', in R. Shacter et. al. (eds.) Uncertainty in Artificial Intelligence, IV, North Holland, 121-131, 1990.
E. M. Neufeld and David Poole, ``Combining Logic and Probability'', peer commentary on P. Cheeseman, ``Inquiry into Computer Understanding'' (Edited by M. McLeish), Computational Intelligence, 4(1), 98-99. 1988.
D. L. Poole and E.M.Neufeld, ``Sound Probabilistic Inference in Prolog: An executable specification of influence graphs'', invited paper, Proc. First International Symposium on Artificial Intelligence, Monterrey, Mexico, pp. 37-54, October 1988.

1987

D. L. Poole, ``Variables in Hypotheses'', Proceedings Tenth International Joint Conference on Artificial Intelligence (IJCAI), Milano, August 1987, pp. 905-908. (scan)
E. M. Neufeld and D. L. Poole, ``Towards solving the multiple extension problem: combining defaults and probabilities'', Proceedings Third AAAI Workshop on Reasoning with Uncertainty (UAI), Seattle, July 1987, pp. 305-312. Revised version in Uncertainty in Artificial Intelligence, III, North Holland, 1989.
D. L. Poole, R. Goebel and R. Aleliunas, ``Theorist : a logical reasoning system for defaults and diagnosis'', in N. Cercone and G. McCalla (Eds.) The Knowledge Frontier: Essays in the Representation of Knowledge, Springer Varlag, New York, 1987, pp. 331-352.
David Poole, ``The Use of Logic'', peer commentary on D. McDermott, ``Taking Issues: A Critique of Pure Reason'', (Edited by H. Levesque), Computational Intelligence, 3(3), 205-206, 1987.

1986

D. L. Poole and R. G. Goebel, ``Gracefully Adding Negation and Disjunction to Prolog'', Proc. Third International Logic Programming Conference, Springer-Verlag Lecture Notes in Computer Science 225, pp. 635-641, 1986.
R. Goebel, K. Furukawa and D. L. Poole, ``Using Definite Clauses and Integrity Constraints as the Basis for a Theory Formation Approach to Diagnostic Reasoning'', Proc. Third International Logic Programming Conference, Springer-Verlag Lecture Notes in Computer Science 225, pp. 211-222, 1986.

1985

D. L. Poole, `` On the Comparison of Theories: Preferring the Most Specific Explanation'', Proceedings Ninth International Joint Conference on Artificial Intelligence (IJCAI), Los Angeles, August 1985, pp. 144-147. (a scan)
M. L. Jones and D. L. Poole, ``An Expert System for Educational Diagnosis Based on Default Logic'', Proc. Fifth International Workshop on Expert Systems and Applications, Avignon, France, May 1985, pp. 673-683.
D. L. Poole and R. G. Goebel, ``On Eliminating loops in Prolog'', SIGPLAN Notices, Vol 20, No 8, August 1985, pp. 38-40,

1984

D. L. Poole, ``A Logical System for Default Reasoning'', Proc. AAAI Workshop on Non-Monotonic Reasoning, NY Oct 1984, pp. 373-384.
D. L. Poole, ``Making `Clausal' Theorem Provers `Non-Clausal' '', Proc. Canadian Society for Computational Studies of Intelligence National Conference (CSCSI-84), London, May 1984, pp. 124-125.

1980

D. L. Poole, ``A Taxonomy of Control in Production Systems'', Proc. Australian Computer Science Conference, Canberra, Jan 1980.

Last updated 2016-02-29 - David Poole, poole@cs.ubc.ca