My research focuses on representation and inference formalism based on probabilistic and decision theoretical graphical models, such as Bayesian networks, decomposable Markov networks, influence diagrams, Markov decision processes, causal independence models, and multiply sectioned Bayesian networks. I study issues on representation, on acquisition by machine learning or elicitation, on effective inference mechanisms, and on multiagent systems. I develop prototype decision support systems for various applications.
Knowledge representation in multiagent graphical models
Uncertain knowledge representation
Probabilistic inference in multiagent graphical models
Probabilistic inference in Bayesian networks
Multiagent constraint satisfaction
Multiagent collaborative decision making
Causal modeling in uncertain knowledge acquisition
Privacy in multiagent systems
Causal independent models
Y. Xiang and K. Srinivasan, Privacy Preserving Existence Recognition and Construction of Hypertree Agent Organization. Journal of Autonomous Agents and Multi-Agent Systems, 2015, (DOI) 10.1007/s10458-015-9285-5.
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.
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.