Publications

Books

Y. Xiang and B. Chaib-draa (Eds.), Advances in Artificial Intelligence, Proc. 16th Conf. Canadian Society for Computational Studies of Intelligence (AI 2003), LNAI 2671, Springer, 2003.
Y. Xiang, Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press, 2002.
A review of the book is published by Alex M. Andrew in Kybernetes: The International Journal of Systems & Cybernetics, Vol.32, No.7/8, 2003.

Refereed journals

Y. Xiang and Q. Wang, Learning Tractable NAT-Modeled Bayesian Networks, Annals of Mathematics and Artificial Intelligence, online first, DOI: 10.1007/s10472-021-09748-0, 2021.
Y. Xiang and D. Loker, Trans-Causalizing NAT-Modeled Bayesian Networks, IEEE Transactions on Cybernetics, online first, DOI: 10.1109/TCYB.2020.3009929, 2020.
Y. Xiang and A. Alshememry, Privacy Sensitive Environment Re-Decomposition for Junction Tree Agent Organization Construction. Journal of Autonomous Agents and Multi-Agent Systems, Doi: 10.1007/s10458-019-09438-6, Vol. 34, Issue 1, Article 15, (published online: Jan 2020).
Y. Xiang, Direct causal structure extraction from pairwise interaction patterns in NAT modeling Bayesian networks. Int. J. Approximate Reasoning, https://doi.org/10.1016/j.ijar.2018.11.016, Vol 105, 175-193, 2019.
Y. Xiang and Q. Jiang, NAT Model Based Compression of Bayesian Network CPTs over Multi-Valued Variables. Computational Intelligence, Vol. 34, No. 1, 219-240, 2018.
J. Zhu, Z. Jiang, J. Lai, Y. Xiang, B. Baird, and E. McBean, Towards Efficient Use of an Unmanned Aerial Vehicle for Urban Flood Monitoring. Journal of Water Mangement Modeling, DOI: 10.14796/JWMM.C433, 2017.
Y. Xiang and Y. Jin, Efficient probabilistic inference in Bayesian networks with multi-valued NIN-AND tree local models. Int. J. Approximate Reasoning, http://dx.doi.org/10.1016/j.ijar.2017.04.009, Vol. 87, 67-89, 2017.
Y. Xiang and K. Srinivasan, Privacy Preserving Existence Recognition and Construction of Hypertree Agent Organization. Journal of Autonomous Agents and Multi-Agent Systems, Vol. 30, No. 2, 220-258, 2016.
Y. Xiang and F. Hanshar, Multiagent Decision Making in Collaborative Decision Networks by Utility Cluster Based Partial Evaluation. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 23, No. 2, 149-191, 2015.
Y. Xiang, Y. Mohamed, and W. Zhang, Distributed Constraint Satisfaction with Multiply Sectioned Constraint Networks. International Journal of Information and Decision Sciences, Vol.6, No.2, 127-152, 2014.
An extended version containing formal proofs
Y. Xiang and M. Truong, Acquisition of Causal Models for Local Distributions in Bayesian Networks. IEEE Transactions on Cybernetics, Vol.44, No.9, 1591-1604, 2014.
Y. Xiang, Non-impeding Noisy-AND Tree Causal Models Over Multi-valued Variables. International Journal of Approximate Reasoning, Vol.53, No. 7, 988-1002, 2012.
Y. Xiang and F. Hanshar, Multiagent Expedition with Graphical Models. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol.19, No.6, 939-976, 2011.
Y. Xiang, J. Smith and J. Kroes, Multiagent Bayesian Forecasting of Structural Time-Invariant Dynamic Systems with Graphical Models. International Journal of Approximate Reasoning, Vol.52, No. 7, 960-977, 2011.
Y. Xiang, Book Review: A. Darwiche, Modeling and Reasoning with Bayesian Networks, Artificial Intelligence, Vol. 174, No. 2, 147-151, 2010.
Y. Xiang and F. Hanshar, Comparison of Tightly and Loosely Coupled Decision Paradigms in Multiagent Expedition. International Journal of Approximate Reasoning, Vol.51, No. 5, 600-613, 2010.
X. An, Y. Xiang and N. Cercone, Dynamic Multiagent Probabilistic Inference. International Journal of Approximate Reasoning, Vol.48, No.1, 185-213, 2008.
Y. Xiang and N. Jia, Modeling Causal Reinforcement and Undermining for Efficient CPT Elicitation. IEEE Trans. Knowledge and Data Engineering, Vol.19, No.12, 1708-1718, 2007. (copyright notice)
J. Lee and Y. Xiang, Complexity Measurement of Fundamental Pseudo-independent Models. International Journal of Approximate Reasoning, Vol.46, No.2, 346-365, 2007.
Y. Xiang and J. Lee, Learning Decomposable Markov Networks in Pseudo-Independent Domains with Local Evaluation. Machine Learning, Vol.65, No.1, 199-227, 2006.
Y. Xiang, F.V. Jensen and X. Chen, Inference in Multiply Sectioned Bayesian Networks: Methods and Performance Comparison. IEEE Trans. Systems, Man, and Cybernetics, Vol.36, No.3, 546-558, 2006.
Y. Xiang and M. Janzen, A Computational Framework for Package Planning. Inter. J. Knowledge-Based and Intelligent Engineering Systems, Vol.10, No.2, 93-104, 2006.
Y. Xiang, J. Lee and N. Cercone, Towards Better Scoring Metrics For Pseudo-Independent Models . International Journal of Intelligent Systems, Vol.19, No.8, pages 749-768, 2004.
Y. Xiang, X. An and N. Cercone, Simulation of Graphical Models for Multiagent Probabilistic Inference . Simulation: Trans. Society for Modeling and Simulation, Vol.79, No.10, pages 545-567, 2003.
Y. Xiang and V. Lesser, A Constructive Graphical Model Approach For Knowledge-Based Systems: A Vehicle Monitoring Case Study. Computational Intelligence, Vol.19, No.3, pages 284-309, 2003.
Y. Xiang, Comparision of multiagent inference methods in multiply sectioned Bayesian networks. Inter. J. Approximate Reasoning, Vol.33, No.3, pages 235-254, August 2003.
Y. Xiang and V. Lesser, On the Role of Multiply Sectioned Bayesian Networks to Cooperative Multiagent Systems. IEEE Trans. Systems, Man, and Cybernetics-Part A, Vol.33, No.4, 489-501, 2003.
Y. Xiang, Cooperative triangulation in MSBNs without revealing subnet structures, Networks Vol.37, No.1, 53-65, 2001.
Y. Xiang, K.G. Olesen and F.V. Jensen, Practical Issues in Modeling Large Diagnostic Systems with Multiply Sectioned Bayesian Networks, International Journal of Pattern Recognition and Artificial Intelligence, Vol.14, No.1, 59-71, 2000.
Y. Xiang, Belief updating in multiply sectioned Bayesian networks without repeated local propagations, International Journal of Approximate Reasoning, 23: 1-21, 2000.
Y. Xiang and T. Chu, Parallel Learning of Belief Networks in Large and Difficult Domains, Data Mining and Knowledge Discovery, 3: 315-339, 1999.
Y. Xiang and T. Miller, A Well-Behaved Algorithm for Simulating Dependence Structures of Bayesian Networks, International Journal of Applied Mathematics, Vol.1, No.8, 923-932, 1999.
Y. Xiang, Verification of dag structures in cooperative belief network based multi-agent systems, Networks , 31: 183-191, 1998.
S.K.M. Wong, C.J. Butz, and Y. Xiang, Automated database schema design using mined data dependencies, J. Amer. Soci. Infor. Science, 49 (5):455-470, 1998.
Y. Xiang, S.K.M. Wong and N. Cercone. A `Microscopic' study of minimum entropy search in learning decomposable Markov networks. Machine Learning, Vol.26, No.1, 65-92, 1997.
Y. Xiang, A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication, Artificial Intelligence, Vol.87, No.1-2, p295-342, 1996.
Y. Xiang, S.K.M. Wong and N. Cercone. Quantification of uncertainty in classification rules discovered from databases. In Computational Intelligence , 11(2), pages 425-441, 1995.
J.Y. Zhu, W.B.H Cooke, Y. Xiang and M. Chen, Optimal traffic flow schedules in a semi-parallel tree network, Computers and Industrial Engineering, Vol.29, No.1-4, 461-465, 1995.
Y. Xiang, D. Poole and M. P. Beddoes, Multiply Sectioned Bayesian Networks and Junction Forests for Large Knowledge Based Systems, Computational Intelligence, Vol.9, No.2, 171-220, 1993.
Y. Xiang, B. Pant, A. Eisen, M.P. Beddoes, and D. Poole. Multiply sectioned Bayesian networks for neuromuscular diagnosis. Artificial Intelligence in Medicine , 5:293-314, 1993.
Y. Xiang, A. Eisen, M. MacNeil, and M.P. Beddoes, Quality Control in Nerve Conduction Studies with Coupled Knowledge Based System Approach, Muscle and Nerve, Vol.15, No.2, 180-187, 1992.
Y. Xiang and S. Yang, A New Method for Passive Clock Synchronization and Self-Positioning of Time Division Multiple Access Communication and Position Location System, J. of Communications, China , Vol. 9, 2: 89-93, 1988.
S. Yang and Y. Xiang, Z80 Assembly Subroutine for Speedy Evolution of 32 Bit Number, Micro-Computer, China, 4: 43-45, 1985.

Book Chapters

M. Roher and Y. Xiang, Mixing ICI and CSI Models for More Efficient Probabilistic Inference, C. Goutte and X. Zhu (Eds.): Canadian AI 2020, LNAI 12109, Springer, pages 451-463, 2020.
Y. Xiang and B. Baird, Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy, E. Bagheri and J.C.K. Cheung (Eds.): Canadian AI 2018, LNAI 10832, Springer, pages 3-16, 2018.
Y. Xiang and D. Loker, De-Causalizing NAT-Modeled Bayesian Networks for Inference Efficiency, E. Bagheri and J.C.K. Cheung (Eds.): Canadian AI 2018, LNAI 10832, Springer, pages 17-30, (the Best Paper Award), 2018.
Y. Xiang, Fault Tolerant Direct NAT Structure Extraction from Pairwise Causal Interaction Patterns, S. Moral, O. Pivert, D. Sanchez and N. Marin (Eds.): Scalable Uncertainty Management, LNCS 10564, Springer, pages 134-148, 2017.
Y. Xiang and Q. Jiang, Compression of General Bayesian Net CPTs, R. Khoury and C. Drummond (Eds.): Advances in Artificial Intelligence, Springer, 285-297, 2016.
Y. Xiang and Q. Liu, Compression of Bayesian Networks with NIN-AND Tree Modeling, L.C. vander Gaag and A.J. Feelders (Eds.): Probabilistic Graphical Models, pages 551-566, 2014.
Y. Xiang, Optimal Collaborative Design in Supply Chains, J. Wang (Ed.): Encyclopedia of Business Analytics and Optimization, IGI Global, pages 1698-1710, 2014.
Y. Xiang and K. Srinivasan, Construction of Privacy Preserving Hypertree Agent Organization as Distributed Maximum Spanning Tree Construction, O.R. Zaiane and S. Zilles (Eds.): Advances in Artificial Intelligence, LNAI7884, Springer-Verlag Berlin Heidelberg, pages 199-210, 2013.
Y. Xiang and K. Srinivasan, Boundary Set Based Existence Recognition and Construction of Hypertree Agent Organization, O.R. Zaiane and S. Zilles (Eds.): Advances in Artificial Intelligence, LNAI7884, Springer-Verlag Berlin Heidelberg, pages 187-198, 2013.
Y. Xiang and F. Hanshar, Multiagent Decision by Partial Evaluation, L. Kosseim and D. Inkpen (Eds.): Advances in Artificial Intelligence, LNAI7310, Springer-Verlag Berlin Heidelberg, pages 242-254, 2012.
Y. Xiang, M. Truong, J. Zhu, D. Stanley, and B. Nonnecke, Indirect Elicitation of NIN-AND Trees in Causal Model Acquisition, S. Benferhat and J. Grant (Eds.): Scalable Uncertainty Management, LNAI 6929, Springer-Verlag Berlin Heidelberg, pages 261-274, 2011.
Y. Xiang and F. Hanshar, Partial Evaluation for Planning in Multiagent Expedition, C. Butz and P. Lingras (Eds.), Advances in Artificial Intelligence, LNAI 6657, Springer, pages 420-432, 2011.
Y. Xiang, Y. Li, and J. Zhu, Towards Effective Elicitation of NIN-AND Tree Causal Models. L. Godo and A. Pugliese (Eds.): Scalable Uncertainty Management, LNAI 5785, Springer-Verlag Berlin Heidelberg, 282-296, 2009.
Y. Xiang, J. Zhu, and Y. Li, Enumerating Unlabeled and Root Labeled Trees for Causal Model Acquisition. Advances in Artificial Intelligence, LNAI 5549, Springer, 158-170, 2009.
Y. Xiang, Pseudo Independent Models and Decision Theoretic Knowledge Discovery. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining (2nd Ed), 1632-1638, Info. Sci. Ref., 2008.
Y. Xiang, Building Intelligent Sensor Networks With Multiagent Graphical Models. In G.P. Wren, N. Ichalkaranje and L.C. Jain (Eds), Intelligent Decision Making: An AI-Based Approach, pages 289-320, Springer-Verlag, 2008.
Y. Xiang and W. Zhang, Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks. In Z. Kobti and D. Wu (Eds.), Advances in Artificial Intelligence, LNAI 4509, pages 228-240, Springer-Verlag, 2007.
Y. Xiang and F. Hanshar, Planning in Multiagent Expedition with Collaborative Design Networks. In Z. Kobti and D. Wu (Eds.), Advances in Artificial Intelligence, LNAI 4509, pages 526-538, Springer-Verlag, 2007.
Y. Xiang and X. Chen, Lazy inference in multiply sectioned Bayesian networks using linked junction forests. In P. Lucas, J.A. Gamez and A. Salmeron (Eds.), Advances in Probabilistic Graphical Models, pages 175-190, 2007.
Y. Xiang and N. Jia, Modeling causal reinforcement and undermining with noisy-AND trees. In L. Lamontagne and M. Marchand (Eds.), Canadian AI 2006, LNAI 4013, pages 171-182, Springer-Verlag, 2006.
Y. Xiang, Pseudo Independent Models. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, pages 935-940, 2005.
Y. Xiang, J. Chen, and A. Deshmukh, A Decision-Theoretic Graphical Model For Collaborative Design On Supply Chains. In A.Y. Tawfik and S.D. Goodwin (Eds.), Advances in Artificial Intelligence, LNAI 3060, pages 355-369, 2004.
Y. Xiang and X. Chen, Interface Verification for Multagent Probabilistic Inference. In J.A. Gamez, S. Moral, and A. Salmeron (Eds.), Advances in Bayesian Networks, 19-38, 2004.
M. Janzen and Y. Xiang, Probabilistic reasoning for meal planning in intelligent fridges. In Y. Xiang and B. Chaib-draa (Eds.), Advances in Artificial Intelligence, LNAI 2671, pages 575-582, 2003.
Y. Xiang, C. Ye, and D. Stacey, Application of Bayesian networks to shopping assistance. In R. Cohen and B. Spencer (Eds.), Advances in Artificial Intelligence, LNAI 2338, pages 344-348, 2002.
Y. Xiang and J. Lee, Local score computation in learning belief networks. In E. Stroulia and S. Matwin (editors), Advances in Artificial Intelligence, pages 152-161. Springer, 2001.
Y. Xiang, J. Hu, N. Cercone and H. Hamilton, Learning Pseudo-Independent Models: Analytical and Experimental Results. H. Hamilton, (Ed.), Advances in in Artificial Intelligence, pages 227-239. Springer, 2000.
Y. Xiang, Temporally invariant junction tree for inference in dynamic Bayesian network. Invited contribution in M. Wooldridge and M. Veloso, editors, Artificial Intelligence Today: Recent Trends and Developoments, page 473-487, Springer, 1999.
Y. Xiang, Temporally invariant junction tree for inference in dynamic Bayesian network. R.E. Mercer and E. Neufeld, editors, Advances in Artificial Intelligence, pages 363-377. Springer, 1998.
Y. Xiang, Semantics of Multiply Sectioned Bayesian Networks for Cooperative Multi-agent Distributed Interpretation. G. McCalla, (Ed.), Advances in Artificial Intelligence , Springer, p213-226, 1996.
Y. Xiang, Distributed multi-agent probabilistic reasoning with Bayesian networks, Z.W. Ras and M. Zemankova (Eds.), Methodologies for Intelligent Systems, Springer-Verlag, 285-294, 1994.
Y. Xiang, S.K.M. Wong, and N. Cercone, Quantifying Uncertainty of Knowledge Discovered from Databases, W.P. Ziarko (Ed.), Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 63-73, 1994.
Y. Xiang, M.P. Beddoes and D. Poole, Can uncertainty management be realized in a finite totally ordered probability algebra? In M. Henrion, R.D. Shachter, L.N. Kanal, and J.F. Lemmer, editors, Uncertainty in Artificial Intelligence 5, pages 41-57. North-Holland, 1990.

Refereed conferences

Y. Xiang and W. Sun, Learning NAT-Modeled Bayesian Network Structures with Bayesian Approach, Proc. 35th Canadian Conf. on Artificial Intelligence, https://caiac.pubpub.org/pub/klsstfua, Toronto, 2022.
Y. Xiang and H. Zheng, Tractable Inference for Hybrid Bayesian Networks with NAT-Modeled Dynamic Discretization, The International FLAIRS Conference Proceedings. 35, (May 2022). DOI:https://doi.org/10.32473/flairs.v35i.130561.
Y. Xiang and A. Alshememry, Constructing junction tree agent organization with privacy, Proc. 20th Inter. Conf. on Autonomous Agents and Multiagent Systems, Online, Pages 1746-1748, May 3-7, 2021.
Y. Xiang and Q. Wang, Learning NAT-modeled Bayesian networks from data, Proc. 33th Inter. Florida Artificial Intelligence Research Society Conf., AAAI Press, pages 599-604, 2020.
Y. Xiang and A. Alshememry, Privacy sensitive construction of junction tree agent organization for multiagent graphical models, Proc. Machine Learning Research, Vol. 72: Inter. Conf. Probabilistic Graphical Models, Prague, Czech Republic. Pages 523-534, 2018.
Y. Xiang, Extraction of NAT Causal Structures Based on Bipartition, Proc. 30th Inter. Florida Artificial Intelligence Research Society Conf., AAAI Press, pages 754-759, 2017.
Y. Xiang and Q. Jiang, Compressing Bayes Net CPTs with Persistent Leaky Causes, Editors: A. Antonucci, G. Corani, and C.P. de Campos, JMLR Workshop and Conference Proceedings, Vol. 52, Proc. Eighth Inter. Conf. on Probabilistic Graphical Models, pages 535-546, 2016.
Y. Xiang and Y. Jin, Multiplicative Factorization of Multi-Valued NIN-AND Tree Models, Editors: Z. Markov and I. Russell, Proc. 29th Inter. Florida Artificial Intelligence Research Society Conf., AAAI Press, pages 680-685, 2016.
Y. Xiang, Bayesian Network Inference With NIN-AND Tree Models, Proc. 6th European Workshop on Probabilistic Graphical Models, pages 363-370, 2012.
J. Zhu, Y. Xiang and E. McBean, Operational hazard risk assessment using Bayesian networks, Proc. 13th Inter. Conf. Enterprise Information Systems, pages 135-139, 2011.
Y. Xiang, Acquisition and Computation Issues with NIN-AND Tree Models, P. Myllymaki, T. Roos and T. Jaakkola (Ed.), Proc. 5th European Workshop on Probabilistic Graphical Models, pages 281-289, 2010.
Y. Xiang, Generalized Non-impeding Noisy-AND Trees. Proc. 23th Inter. Florida Artificial Intelligence Research Society Conf., 555-560, 2010.
Y. Xiang, J. Smith, and J. Kroes, Multiagent Bayesian Forecasting of Time Series with Graphical Models. Proc. 22th Inter. Florida Artificial Intelligence Research Society Conf., 565-570, 2009.
Y. Xiang and F. Hanshar, Tightly and Loosely Coupled Decision Paradigms in Multiagent Expedition, Proc. 4th European Workshop on Probabilistic Graphical Models, pages 305-312, 2008.
Y. Xiang and W. Zhang, Distributed University Timetabling with Multiply Sectioned Constraint Networks. Proc. 21th Inter. Florida Artificial Intelligence Research Society Conf., pages 567-572, 2008.
Y. Xiang, Tractable Optimal Multiagent Collaborative Design. Proc. IEEE/WIC/ACM Inter. Conf. on Intelligent Agent Technology (IAT 2007), pages 257-260, 2007.
Y. Xiang, A Decision Theoretic View on Choosing Heuristics for Discovery of Graphical Models. Proc. 20th Inter. Florida Artificial Intelligence Research Society Conf., pages 170-175, 2007.
Y. Xiang, Optimal Design with Design Networks. Procs. 3rd European Workshop on Probabilistic Graphical Models, pages 309-316, Prague, Czech, 2006.
Y. Xiang and K. Zhang, Advance in Multiply Sectioned Bayesian Networks: Sensor Network Practitioners' Perspective. 11th IEEE Inter. Conf. on Emerging Technologies and Factory Automation, pages 590-593, 2006.
Y. Xiang and K. Zhang, Agent Interface Enhancement: Making Multiagent Graphical Models Accessible. Proc. 5th Inter. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS'06), pages 19-26, 2006.
Y. Xiang, J. Chen, and W.S. Havens, Optimal Design in Collaborative Design Network. Proc. 4th Inter. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS'05), pages 241-248, 2005.
J. Lee and Y. Xiang, Model complexity of pseudo-independent models. Proc. 18th Inter. Florida Artificial Intelligence Research Society Conf., pages 766-771, 2005.
Y. Xiang and X. Chen, Inference in multiply sectioned Bayesian networks with lazy propagation and linked jounction forests, Procs. 2nd European Workshop on Probabilistic Graphical Models, 217-224, 2004.
X. An, Y. Xiang, and N. Cercone, Cooperative computation of Markov boundaries for efficient observation in multiagent probabilistic inference, Procs. 2nd European Workshop on Probabilistic Graphical Models, 9-16, 2004.
X. An, Y. Xiang, and N. Cercone, Revising Markov Boundary for multiagent probabilistic inference, Procs. IEEE/WIC/ACM Inter. Conf. on Intelligent Agent Technology, 113-119, 2004.
Y. Xiang and M. Janzen, Package Planning with Graphical Models, Proc. 17th Inter. Florida Artificial Intelligence Research Society Conf., pages 874-879, 2004.
X. An, Y. Xiang, and N. Cercone, Probabilistic Reasoning in Dynamic Multiagent Systems, Proc. 10th Inter. Workshop on Non-Monotonic Reasoning, pages 16-24, 2004.
X. An, Y. Xiang, and N. Cercone, A Dynamic Environment Simulator, Proc. European Simulation and Modelling Conference, 187-191, 2003.
Y. Xiang, J. Lee, and N. Cercone, Parameterization of pseudo-independent models, Proc. 16th Florida Artificial Intelligence Research Society Conf., 521-525, 2003.
Y. Xiang and X. Chen, Cooperative Verification of Agent Interface, Procs. 1st European Workshop on Probabilistic Graphical Models, 194-203, 2002.
Y. Xiang, Comparing Alternative Methods for Inference in Multiply Sectioned Bayesian Networks. Proc. 15th Florida Artificial Intelligence Research Society Conf., 534-538, Pensacola, 2002.
Y. Xiang and X. An, Generating Dependence Structure of Multiply Sectioned Bayesian Networks. Proc. 14th Florida Artificial Intelligence Research Society Conf., 613-618, Key West, 2001.
Y. Xiang and C. Ye, A simple method to evaluate influence diagrams. Proc. 3rd Inter. Conf. on Cognitive Science, 602-606, 2001.
Y. Xiang and V. Lesser, Justifying multiply sectioned Bayesian networks. Proc. 6th Inter. Conf. on Multi-agent Systems, 349-356, Boston, 2000.
Y. Xiang and V. Lesser, A constructive Bayesian approach for vehicle monitoring. In Proc. 3rd Inter. Conf. on Information Fusion, ThC2 14--21, Paris, 2000.
H. Geng and Y. Xiang. Distributed multi-agent MSBN: Implementing verification. In Proc. 13th Inter. Florida Artificial Intelligence Research Society Conf., pages 293--297, Orlendo, 2000.
J.Y. Zhu, A. Deshmukh, Y. Xiang, and T. Middelkoop. Applying tree structure to aggregate scheduling in a flexible manufacture environment. Proc. 27th Inter. Conf. on Computers and Industrial Engineering, 2000.
Y. Xiang and F.V. Jensen, Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation, Proc. 15th Conf. on Uncertainty in Artificial Intelligence, 680-687, Stockholm, 1999.
Y. Huang and Y. Xiang, Learning Bayesian networks by learning decomposable Markov networks first, Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, 1704-1709, Edmonton, 1999.
H. Geng and Y. Xiang, Implementation of fully distributed inference in multiagent MSBN systems, Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, 1698-1703, Edmonton, 1999.
Y. Xiang, K.G. Olesen and F.V. Jensen, Some Practical Issues in Modeling Diagnostic Systems with Multiply Sectioned Bayesian Networks, 12th Florida Artificial Intelligence Research Symposium, 438-443, Orlendo, 1999.
Y. Xiang and H. Geng, Distributed Monitoring and Diagnosis with Multiply Sectioned Bayesian Networks, AAAI Spring symposium on AI in Equipment Service Maintenance and Support, 18-25, Sandford, 1999.
Y. Xiang, A characterization of single-link search in learning belief networks, in Proc. Pacific Rim Knowledge Acquisition Workshop, 218-233, Singapore, 1998.
J.Y. Zhu, W.B.H. Cooke and Y. Xiang, Application of Bayesian networks to quantified risk assessment, Proc. 5th Inter. Conf. Industrial Engineering and Management Science , 321-328, Beijing, 1998.
Y. Xiang, Towards understanding of pseudo-independent domains, Poster Proc. 10th Inter. Symposium on Methodologies for Intelligent Systems, 221-232, Charlotte, 1997.
T. Chu and Y. Xiang, Exploring parallelism in learning belief networks, Proc. 13th Conf. on Uncertainty in Artificial Intelligence, 90--98, Providence, 1997.
J. Hu and Y. Xiang, Learning belief networks in domains with recursively embedded pseudo independent submodels, Proc. 13th Conf. on Uncertainty in Artificial Intelligence, 258--265, Providence, 1997.
T. Chu and Y. Xiang, Parallel learning of belief networks, Proc. 10th Florida Artificial Intelligence Research Symposium, 192--197, Daytona Beach, 1997.
Y. Xiang, S.K.M Wong and N. Cercone. Critical Remarks on Single Link Search in Learning Belief Networks. In Proc. Uncertainty in Artificial Intelligence 96, 564-571, 1996.
Y. Xiang, Distributed structure verification in multiply sectioned Bayesian networks, Proc. Florida Artificial Intelligence Research Symposium , 295-299, 1996.
Y. Xiang, Distributed scheduling of multiagent communication, Proc. 1st International Conf. on Multi-agent Systems, San Francisco, p390-397, 1995.
Y. Xiang, Optimization of inter-subnet belief updating in multiply sectioned Bayesian networks, Proc. of 11th Conf. on Uncertainty in Artificial Intelligence, Montreal, p565-573, 1995.
S.K.M. Wong, C.J. Butz and Y. Xiang, A method for implementing a probabilistic model as a relational database, Proc. of 11th Conf. on Uncertainty in Artificial Intelligence, Montreal, p556-564, 1995.
S.K.M. Wong and Y. Xiang, Construction of a Markov Network from Data for Probabilistic Inference, Proc. Third International Workshop on Rough Sets and Soft Computing, San Jose, CA, 562-569, 1994.
S.K.M. Wong, Y. Xiang and X. Nie, Representation of Bayesian Networks as Relational Databases, 5th International Conference on Information Processing & Management of Uncertainty in Knowledge-based Systems, 159-165, 1994.
J. Jones, Y. Xiang and S. Joseph, Bayesian Probabilistic Reasoning in Design, Proc. IEEE Ninth Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, BC, 501-504, 1993.
Y. Xiang, D. Poole and M. P. Beddoes, Exploring Localization In Bayesian Networks For Large Expert Systems, Proc. Eighth Conference on Uncertainty in Artificial Intelligence, Stanford, CA, 344-351, 1992.
Y. Xiang, Computing the Lower-Bounded Composite Hypothesis by Belief Updating, Proc. Ninth Canadian Conference on Artificial Intelligence, Vancouver, BC, 98-105, 1992.
Y. Xiang, B. Pant, A. Eisen, M. P. Beddoes and D. Poole, PAINULIM: A Neuromuscular Diagnostic Aid Using Multiply Sectioned Bayesian Networks, Proc. ISMM International Conference on Mini and Microcomputers in Medicine and Healthcare, Long Beach, CA, 64-69, 1991.
Y. Xiang, M.P. Beddoes and D. Poole, Sequential Updating Conditional Probability in Bayesian Networks by Posterior Probability, Proc. 8th Biennial Conf. Canadian Society for Computational Studies of Intelligence, Ottawa, 21-27, 1990.
Y. Xiang, M.P. Beddoes and D. Poole, Can uncertainty management be realized in a finite totally ordered probability algebra?, Proc. 5th Workshop on Uncertainty in Artificial Intelligence, Windsor, 385-393, 1989.

Other publications

Y. Xiang and K. Grant, Preface for Special Issue on Uncertain Reasoning. J. Automated Reasoning, Vol. 45, 1-2, 2010.
Y. Xiang and S. Benferhat, Editorial for Special Issue on Uncertain Reasoning. Inter. J. Approximate Reasoning, Vol.33, No.3, 219-220, 2003.
E. Santos and Y. Xiang, Editorial-Advances on Uncertain Reasoning in Intelligent Systems. IEEE Trans. Systems, Man, and Cybernetics-Part B, Vol.32, No.1, 2-3, 2002.
Y. Xiang and W. Havens, Integrating Probabilistic Reasoning into the Echidna Constraint Logic Programming System, CSS-IS TR 92-08, Simon Fraser University, 1992.
Y. Xiang and Z. Yang, Development of the Time Division and Code Division Multiple Access Data Bus System for 325 Meter Tower Data Collection, Proc. Annual Conf. Instit. Atmospheric Physics, Chinese Academy Sciences, 31-32, 1986.
Y. Xiang and S. Yang, Multi-User Dynamic Simulation and Realization of Passive Clock Synchronization in Time Division Multiple Access Communication and Position System, 8442 Symposium of Chinese Aeronautical Academy, 1984.