Dr. Marco Valtorta

I am a Professor at the Department of Computer Science and Engineering of the University of South Carolina. Here is my Curriculum Vitae in pdf (updated 2024-08-30). an NSF-style biosketch in MS-Word format, and a narrative biography in pdf. Here are my ResearchGate profile and my Google Scholar profile.

Storey Innovation Center
Department of Computer Science and Engineering
550 Assembly Street
University of South Carolina
Columbia, SC 29208
mgv@cse.sc.edu
Phone: +1 (803) 777-4641; Fax: +1 (803) 777-3767; Office: 2269

Education

Laurea: Electrical Engineering, Politecnico di Milano, 1980
MA: Computer Science, Duke University, 1983
PhD: Computer Science, Duke University, 1987

Research Projects, Selected Publications, Technical Reports, Talks

Orientation from Fall 2021, recorded by Dr. Jijun Tang

A Listing of Resources for Graduate Students (Fall 2024)

Fall 2024 Courses

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 330 (Programming Language Structures)

Spring 2024 Courses

CSCE 531 (Compiler Construction)

CSCE 582 (= STAT 582) (Bayesian Networks and Decision Graphs)

Fall 2023 Courses

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 330 (Programming Language Structures)

Spring 2023 Courses

CSCE 531 (Compiler Construction)

CSCE 582 (= STAT 582) (Bayesian Networks and Decision Graphs)

Fall 2022 Courses

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 330 (Programming Language Structures)

Spring 2022 Courses

CSCE 531 (Compiler Construction)

CSCE 582 (Bayesian Networks and Decision Graphs)

Fall 2021 Courses

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 590 (Topics in Information Technology: Functional Programming)

Spring 2021 Courses

CSCE 531 (Compiler Construction)

CSCE 582 (Bayesian Networks and Decision Graphs)

Fall 2020 Courses

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 590 (Topics in Information Technology: Functional Programming)

Spring 2020 Courses

CSCE 531 (Compiler Construction)

CSCE 582 (Bayesian Networks and Decision Graphs)

Fall 2019 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

Spring 2019 Courses

CSCE 790 Section 1 (Advanced Topics in Probabilistic Graphical Models)

Fall 2018 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

Spring 2018 Courses

CSCE 317 (Computer Systems Engineering)

Fall 2017 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 330 (Programming Language Structures)

Spring 2017 Courses

CSCE 580 (Artificial Intelligence)

CSCE 317 (Computer Systems Engineering)

Fall 2016 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 330 (Programming Language Structures)

Summer 2016 Courses

CSCE 390 (Professional Issues in Computer Science and Engineering)

Spring 2016 Courses

CSCE 582 (Bayesian Networks and Decision Graphs)

CSCE 317 (Computer Systems Engineering)

Fall 2015 Courses

CSCE 190 (Computing in the Modern World)

CSCE 330 (Programming Language Structures)

CSCE 390 (Professional Issues in Computer Science and Engineering)

Spring 2015 Courses

CSCE 580 (Artificial Intelligence)

CSCE 317 (Computer Systems Engineering)

Fall 2014 Courses

CSCE 190 (Computing in the Modern World)

CSCE 330 (Programming Language Structures)

CSCE 390 (Professional Issues in Computer Science and Engineering)

Spring 2014 Courses

CSCE 580 (Artificial Intelligence)

CSCE 582 (Bayesian Networks and Decision Graphs): pdf flyer

CSCE 582 (Bayesian Networks and Decision Graphs)

Fall 2013 Courses

CSCE 330 (Programming Language Structures)

CSCE 390 (Professional Issues in Computer Science and Engineering)

Spring 2013 Courses

CSCE 531 (Compiler Construction)

CSCE 781 (Knowledge Systems)

Fall 2012 Courses

CSCE 330 (Programming Language Structures)

CSCE 580 (Artificial Intelligence)

Spring 2012 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 582 (Bayesian Networks and Decision Graphs): pdf flyer

CSCE 582 (Bayesian Networks and Decision Graphs)

Fall 2011 Courses

CSCE 330 (Programming Language Structures)

CSCE 580 (Artificial Intelligence)

Spring 2011 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 781 (Knowledge Systems)

Fall 2010 Courses

CSCE 582 (Bayesian Networks and Decision Graphs): pdf flyer

CSCE 582 (Bayesian Networks and Decision Graphs)

CSCE 330 (Programming Language Structures)

Spring 2010 Courses

CSCE 190 (Computing in the Modern World)

CSCE 390 (Professional Issues in Computer Science and Engineering)

CSCE 531 (Compiler Construction)

Fall 2009 Courses

CSCE 580 (Artificial Intelligence)

CSCE 330 (Programming Language Structures)

Spring 2009 Courses

CSCE 211 (Digital Logic Design)

CSCE 582 (Bayesian Networks and Decision Graphs)

CSCE 582 (Bayesian Networks and Decision Graphs): MS-Word flyer
CSCE 582 (Bayesian Networks and Decision Graphs): announcement

Fall 2008 Courses

CSCE 580 (Artificial Intelligence)

Spring 2008 Courses

CSCE 531 (Compiler Construction)

Spring 2008 Bayesian Networks Reading Club

Bayesian Network Reading Club Information

Fall 2007 Courses

CSCE 330 (Programming Language Structures)

Spring 2007 Courses

CSCE 531 (Compiler Construction)

Fall 2006 Courses

CSCE 330 (Programming Language Structures)

Fall 2006 Seminar on the integration of logical and probabilistic reasoning

Seminar Information

Spring 2006 Courses

CSCE 330 (Programming Language Structures)

CSCE 582 (Bayesian Networks and Decision Graphs)

Fall 2005 Courses

CSCE 330 (Programming Language Structures)

Spring 2005 Courses

CSCE 330 (Programming Language Structures)

CSCE 350 (Data Structures and Algorithms)

Fall 2004 Bayesian Networks Reading Club

Bayesian Network Reading Club Information

Fall 2004 Courses

CSCE 330 (Programming Language Structures)

Spring 2004 Courses

CSCE 330 (Programming Language Structures)

CSCE 350 (Data Structures and Algorithms)

Fall 2003 Courses

CSCE 330 (Programming Language Structures)

CSCE 582 (Bayesian Networks and Decision Graphs)

Spring 2003 Courses

CSCE 330 (Programming Language Structures)

CSCE 580 (Artificial Intelligence)

Fall 2002 Courses

CSCE 330 (Programming Language Structures)

CSCE 582 (Bayesian Networks and Decision Graphs)

Spring 2002 Courses

CSCE 580 (Artificial Intelligence)

CSCE 531 (Compiler Construction)

Fall 2001 Courses

CSCE 531 (Compiler Construction) (Link to Spring 2002 edition!)

Spring 2001 Courses

CSCE 146 (Introduction to Algorithmic Programming II)

CSCE 768 (Pattern Recognition and Classification)

Fall 2000 Courses

CSCE 330 (Programming Language Structures) (E-mail if interested in course materials.) CSCE 330 (Programming Language Structures)

CSCE 590 (Foundations of Bayesian Networks)

Fall 1999 and Spring 2000

During the Academic Year 1999-2000, I was on sabbatical. I spent October 1999, March 2000, and parts of May and June 2000 at the Department of Computer Science at the University of Aalborg, as a guest of the Decision Support Systems Unit.

Notes on three lectures on data mining

Spring 1999 Courses

CSCI 330 (Programming Language Structures) (E-mail if interested in course materials.)

Fall 1998 Courses

CSCI 220 (Data Structures and Algorithms)

CSCI 509 (Foundations of Bayesian Networks)

Spring 1998 Courses

CSCI 786 (Knowledge-Based Systems)

Fall 1997 Courses

CSCI 146 (Introduction to Algorithmic Design II)

Spring 1997 Courses

CSCI 330 (Programming Language Structures)

Research Interests:

Probabilistic Graphical Models (esp. Bayesian Networks)
Decision Support Systems
Artificial Intelligence
Knowledge-based systems
Heuristics
Computational complexity

Honors

Lilly Teaching Fellow, Academic Year 1993-94
Advisor of the Year, College of Science and Mathematics, University of South Carolina, 1997

Current Research Projects

Hypergraph-Based Causal Modeling

Agency: Office of Naval Research (ONR). Award Amoint: $100,000. Period: September 1, 2017 to August 31, 2018 Role: co-PI (with PI Linyuan Lu, Mathematics Department).

Co-Arg: Causal Argumentation System with Crowd Elicitation

Agency: Intelligence Advanced Research Project Agency (IARPA); subaward through George Mason University (GMU). Award Amount: $342,331 (subaward only). Period: January 2017 to June 2021. Role: PI of the subward; Prime (GMU) PI: Gheorghe Tecuci.

Recent Research Projects

Integrating and extending techniques for identification of latent variables in graphical models

Elisabeth S. Allman and John A. Rhodes (Dept. of Mathematics, U. Alaska, Fairbanks), Elena Stanghellini (Dept. of Statistics, U. Perugia), and Marco Valtorta. Sponsored by AIM (American Institute of Mathematics) as a SQuaRE (Structured Quartet Research Ensemble) Awarded on December 6, 2010; ended in 2014. Latent (unobservable) variables are common in statistical modeling, often representing an unknown or unmeasurable cause that affects several observable variables. If one makes no assumptions about its nature, however, even a single latent variable in a model may lead to a complete non-identifiability of the model parameters. This means that essentially nothing can be inferred quantitatively about relationships between pairs of variables. However, recent independent works of Stanghellini-Vantaggi and Allman-Matias-Rhodes have shown that for some graphical models even a seemingly mild assumption that the hidden variable be discrete, with a bounded number of states, ensures identifiability. In an extreme case, causal relationships between two observed variables may be unidentifiable without such an assumption, but become identifiable with one. Our goal is to further elucidate the impact on identifiability of the combinatorial structure of the graph, the algebraic structure of the related probability distribution, and finiteness assumptions for hidden variables. The causal Bayesian networks that are heavily used in artificial intelligence represent one class of models to which these techniques should extend, and for which there are many important applications. The do-calculus of Pearl, and algorithmic techniques developed by Tian, and further studied and modified by others, including Valtorta, provides a complete system for obtaining rational expression identifying causal effects between observed variables with no parametric assumptions on the model. However, one typically finds many effects are unidentiable in the presence of latent variables. We extended these results under assumptions of finiteness of the state space for latent variables. See (Allman, Rhodes, Stanghellini, Valtorta, 2015) in the list of publications below.

Combining Facts and Expert Opinion in Analytical Models via Logical and Probabilistic Reasoning

Marco Valtorta was project manager and co-PI; Mike Huhns is co-PI. HNC/Fair Isaac (San Diego, California) is a subcontractor (John Byrnes, co-PI). BALER (Bayesian And Logical Engine for Reasoning) combines facts and expert opinion in analytical models via logical and probabilistic reasoning. It supports the optimal management of uncertainty, inconsistency and disagreement in collaborative intelligence analysis. BALER's strategy is to leverage the complementary strengths of artificial intelligence techniques rooted in logic, probability, and statistics that have heretofore remained disparate both in theory and in practice, in order to build tools that operate robustly and efficiently on large, noisy input from knowledge bases, partial network constructions, and various evidence sources including unstructured data. BALER will provide quantification of uncertainty, proper treatment of dependent and independent sources of information, meaningful reasoning over inconsistent information, and prioritization of new data reporting based on relevance. BALER will support collaboration by highlighting areas of agreement and disagreement, and evidence and assumptions that contribute to disagreement. This work is funded by the Disruptive Technology Office Collaboration and Analyst System Effectiveness (CASE) Program, contract FA8750-06- C-0194 issued by Air Force Research Laboratory (AFRL). The views and conclusions are those of the authors, not of the US Government or its agencies.

Prior and Tacit Knowledge System for Novel Intelligence from Massive Data

Mike Huhns and Marco Valtorta, co-PIs for the University of South Carolina subcontractor. Other partners in the first phase (December 2002-August 2004) were: Global Info Tek (John Cheng and Ray Emami), KRM, Inc (Larry Kerschberg), University of Connecticut (Gene Santos, Jr. and Qunhua Zhao). Sponsored by ARDA. First phase: December 2002-August 2004. OmniSeer supports intelligence analysts in the handling of massive amounts of data, the construction of scenarios, and the management of hypotheses. OmniSeer models analysts with dynamic user models that capture an analyst's context, interests, and preferences, thus enabling more efficient and effective information retrieval. OmniSeer explicitly represents the prior and tacit knowledge of analysts, thus enabling transfer and reuse of such knowledge. Both the user and cognitive models employ a Bayesian network fragment representation, which supports principled probabilistic reasoning and analysis. An independent evaluation of OmniSeer was carried out at NIST in May 2004 and will be used to guide further development. A second phase started in August 2004 and lasted approximately two years. The partner (prime) in the second phase was Georgia Tech Research Institute (Bob Simpson and Betty Whitaker).

USC and SPAWAR (Charleston) cooperation for the SPAWAR Critical Infrastructure Protection Center (CIPC)

Joe Johnson (Physics Department), PI; Marco Valtorta, co-PI. May 2004-September 2004. As part of this collaboration, USC delivered (1) a geographical information system model of several critical infrastructures, and (2) a model of the interdependency of critical infrastructure.

TargetShare: Autonomous Negotiating Teams

Mike Huhns, Project Director, Marco Valtorta, Juan Vargas, Jose Vidal, co-PIs. Sponsored by DARPA. Click here for further information on this DARPA-sponsored project.

Dynamic Decision Support for Command, Control, and Communication in the Context of Tactical Defense

Advances in communication and sensor technologies have brought about huge increases in the types and amounts of information available for battle management. Shortcomings in the ability to integrate and arbitrate missing and conflicting information and the current inability to correlate and reason about vast amounts of information in real time are an impediment to providing a coherent overview of unfolding events. Integrating disparate information, such as voice, video images, and tactical displays, that has varying degrees of reliability is a first step towards battle management. Such integration can be accomplished through the application and further development of Bayesian network and intelligent agent methods. Bayesian networks provide a sound basis for a robust and potentially very efficient solution to the problems posed by incomplete/unreliable data and have proven suitable to the problem of integrating disparate types of data. Other aspects of managing different types of data can be addressed through intelligent agents. Click here for further information on this ONR-sponsored project and on the Decision Analysis Group (DAG), which is jointly led with Professor John Rose. Click here for a picture of DAG members, taken on June 14, 2000. Standing, from left to right, are Young-Gyun Kim, Marco Valtorta, Billy Turkett, and John Rose; sitting, from left to right, are Miguel Barrientos, Clif Presser, and Wayne Smith. All, except Billy, either have been or were members of the DAG group from its inception. Click here and here for other pictures of DAG members.

Analysis of Agricultural Loan Defaults: Development of Credit/Loan Analysis Models

I applied machine learning techniques to build Bayesian Networks to model defaults in loans made to farmers.

This work, which is joint with Leszek Piatkiewicz of Pembroke State University, and is connected to a larger project at S.C. State University, is funded by the U.S. Department of Agriculture.

Conflicts in Diagnostic Bayesian Networks

I address the brittleness problem in diagnostic expert systems by automatically constructing straw models from Bayesian Network Models for Diagnosis. The straw models are normally less likely than the base model given a case to be diagnosed. When one of the straw models becomes more likely than the base model, the user is alerted of a possible misuse of the diagnostic expert system.

This work is joint with Young-Gyun Kim, a graduate student.

Algorithms for the Construction of Bayesian Networks

I am concerned with the efficient learning of Bayesian networks from data.

Jointly with Moninder Singh (a former graduate student at the University of South Carolina, then at the University of Pennsylvania, and now at IBM T.J. Watson Research Center), I designed and implemented a hybrid algorithm called CB, which has been used in various applications. The program was reworked and extended by Mark Bloemeke (former graduate student, now at Rytek).

A study of the complexity of abstraction in qualitative diagnosis

I analyzed the complexity of a technique that uses simplified models for the identification of malfunctions in physical systems, such as conventional power plants.

This project was based on my longstanding interest in heuristic search, draws on my four-year association with the ESPRIT QUIC project and is supported by grant from CISE. This was joint work with Giorgio Tornielli of CISE and Rita Childress, a graduate student. Edward Yu (graduate student) and I have carried out some experimental work on model-based diagnosis using constraint logic programming.

Refinement of Knowledge Bases

Knowledge-base refinement is concerned with the improvement of performance of expert systems through the modification of the knowledge-base they use. My dissertation is the first attempt to apply algorithmic complexity analysis to rule-base refinement, which has otherwise been approached only empirically. Some extensions of this work have been carried out jointly with Dahai Zang, a graduate student. I am studying refinement and validation of belief networks as motivated by problems arising from the development of MUNIN, a large knowledge-based system. I have formally defined several problems and found them to be NP-Complete. This indicates that automatic refinement and validation of belief networks from cases is intractable, in both the Dempster-Shafer and the Bayesian schemes. The relevance of these results for the practice of expert system construction has been assessed. I intend to address the relation of my complexity results to the PAC learning model and to a statistical technique of David Spiegelhalter.

Parallel programs for the construction of Markov networks

Randy Mechling, a graduate student, and I implemented and tested an algorithm for the construction of Bayesian networks on a hypercube.

Computational, Knowledge Engineering, and Statistical Aspects of the Dempster-Shafer Theory of Evidence.

Shijie Wang (a graduate student) and I rewrote a large knowledge-base of rules into a network and are studying the associated computational and knowledge engineering aspects. Stephen Durham, Jeffrey Smolka (Statistics Department) and I studied the statistical consistency of Dempster's rule in diagnostic trees.

Prediction in physical systems using qualitative physics

One of the tasks approached using qualitative reasoning is prediction. David Hibler (a doctoral candidate with a substantial academic background in Physics) proposed a thought experiment approach to prediction that emphasizes the use of multiple simplified models of physical phenomena.

Studies in heuristics for A*

This research addressed the computation of heuristics for a base problem from relaxed subproblems. It includes work with Ishaq Zahid of the Citadel on the Knight's Tour Puzzle and with Othar Hansson and Andy Mayer of UC Berkeley on the extension of a result of mine on the computational complexity of heuristics for A*

Selected Publications

Uncertain Reasoning:

Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "Learning LWF Chain Graphs: A Markov Blanket Discovery Approach." Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI-20),Toronto, Canada, August 3-6, 2020. (Paper available at http://www.auai.org/uai2020/proceedings/446_main_paper.pdf.)

Mohammad Ali Javidian, Zhiyu Wang, Linyuan Lu, and Marco Valtorta. "On a Hypergraph Probabilistic Graphical Model." Annals of Mathematics and Artificial Intelligence, 88 (2020), 1003-1030. DOI: https://doi.org/10.1007/s10472-020-09701-7, available at https://rdcu.be/b5R2a. (ArXiv preprint of earlier version available at https://arxiv.org/abs/1811.08372)

Emad Alsuwat, Hatim Alsuwat, Marco Valtorta, and Csilla Farkas. "Adversarial Data Poising Attacks agaist the PC Learning Algorithm." International Journal of General Systems, 49:1, 3-31, DOI: 10.1080/03081079.2019.1630401, 2020.

Mohammad Ali Javidian, Marco Valtorta, and Pooyan Jamshidi. "Order-Independent Structure Learning of Multivariate Regression Chain Graphs." Proceedings of the 13th International Conference on Scalable Uncertainty Management (SUM 2019), pp. 324-338, Compiegne, France, December 16-18 2019. (Extended version available at https://arxiv.org/abs/1910.01067.)

Hatim Alsuwat, Emad Alsuwat, Marco Valtorta, John Rose, and Csilla Farkas. "Modeling Concept Drift in the Context of Discrete Bayesian Networks." Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, pages 214-224. DOI: 10.5220/0008384702140224, Vienna, Austria, September 17-19, 2019.

Emad Alsuwat, Hatim Alsuwat, John Rose, Marco Valtorta, and Csilla Farkas. "Detecting Adversarial Attacks in the Context of Bayesian Networks." 33rd Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec'19), Charleston, SC, USA - July 15-17, 2019

Mohammad Ali Javidian, Pooyan Jamshidi, and Marco Valtorta. "Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis." AAAI Spring 2019 Symposium: "Beyond Curve Fitting--Causation, Counterfactuals, and Imagination-Based AI" (WHY-19), Palo Alto, CA, March 2019, seven pages. (Locally published proceedings; available at https://why19.causalai.net/papers.html.)

Zhiyu Wang, Mohammad Ali Javidian, Linyuan Lu, and Marco Valtorta. "The Causal Interpretation of Bayesian Hypergraphs." AAAI Spring 2019 Symposium: "Beyond Curve Fitting--Causation, Counterfactuals, and Imagination-Based AI" (WHY-19), Palo Alto, CA, March 2019, seven pages. (Locally published proceedings; available at https://why19.causalai.net/papers.html.)

Mohammad Ali Javidian and Marco Valtorta. "Finding Minimal Separators in LWF Chain Graphs." Proceedings of Machine Learning Research Volume 72: International Conference on Probabilistic Graphical Models (PGM-18), pp. 193-200 (Vaclav Kratochvil and Milan Studeny, editors). Prague, Czech Republic, September 11-14, 2018.

Mohammad Ali Javidian and Marco Valtorta. "On the Properties of MVR Chain Graphs." Workshop at the International Conference on Probabilistic Graphical Models (PGM-18), pp.13-24 (Vaclav Kratochvil and Milan Studeny, editors). Prague, Czech Republic, September 11-14, 2018. (Locally published proceedings; available at http://pgm2018.utia.cz/data/workshopproceedings.pdf.)

Emad Alsuwat, Hatim Alsuwat, Marco Valtorta, and Csilla Farkas. "Cyber Attacks against the PC Learning Algorithm." Second International Workshop on A.I. and Security at ECML-18, 16 pages. Dublin, September 10-14, 2018. (Locally published proceedings; available at http://iwaise2018.it.nuigalway.ie/wp-content/uploads/2018/09/proceedings-second-international.pdf.)

Mohammad Ali Javidian and Marco Valtorta. "Finding Minimal Separators in Ancestral Graphs." Seventh Causal Inference Workshop at the 34th Conference on Artificial Intelligence (UAI-18), 6 pages (Bryant Chen, Panos Toulis, and Alexander Volfovsky, editors), Monterey, CA, August 6-10 2018. (Available at https://sites.google.com/view/causaluai2018/papers.)

Emad Alsuwat, Marco Valtorta, and Csilla Farkas. "How to Generate the Network You Want with the PC Learning Algorithm." Proceedings of the 11th Workshop on Uncertainty Processing (WUPES'18), 12 pages. (Vaclav Kratochvil and Jirina Vejnarova, editors.) Trebon, Czech Republic, June 6-9, 2018. (Available at http://wupes.utia.cas.cz/proceedings/proceedings.pdf.)

"Parameter Identifiability of Discrete Bayesian Networks with Hidden Variables" (with Elizabeth S. Allman, John A. Rhodes, and Elena Stanghellini)
Journal of Causal Inference, 3, 2 (2015), 189-205 (online version: December 2014). (local copy in pdf format) (electronic edition, local copy, in pdf format)

"Instantiation to Support the Integration of Logical and Probabilistic Knowledge" (with Jingsong Wang)
Presented at the First Workshop on Grounding and Transformations for Theories with Variables (GTTV-2011), Vancouver, Canada, May 16, 2011. (Click here for a copy of the workshop paper in pdf format.)

"On the Combination of Logical and Probabilistic Models for Information Analysis" (with Jingsong Wang)
Applied Intelligence, 36, 2 (2012), 472-497. (Click here for a local copy in pdf format.)

"Agent-encapsulated Bayesian Networks and the Rumor Problem" (with Mark Bloemeke and Scott Langevin)
Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS-10), Volume 1, Toronto, Canada, May 10-14, 2010, pp. 1553-1555. (Click here for a local copy in pdf format.) (Click here for a longer version of this paper in pdf format.)

"Extending Polynomial Chaos to Include Interval Analysis" (with Antonello Monti and Ferdinanda Ponci)
IEEE Transactions on Instrumentation and Measurement, Vol.59, Number 1 (January 2010), pp.48-55. (Click here for a local copy in pdf format.)

"On the Completeness of an Identifiability Algorithm for Semi-Markovian Models" (with Yimin Huang)
Annals of Mathematics and Artificial Intelligence, Vol. 54, Issue 4 (2009), pp.363-408. (Click here for a local copy in pdf format.) (Click here for a preprint in pdf format.)

"Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results" (with Scott Langevin)
Proceedings of the Second International Conference on Scalable Uncertainty Management (SUM-08), Naples, Italy, October 1-3, 2008, pp. 284-297. (Proceedings edited by Sergio Greco and Thomas Lukasiewicz and published as Lecture Notes in Artificial Intelligence vol. 5291 (LNAI 5291), Springer, ISBN-13 978-30540-87992-3, 2008.) (Click here for a preprint in pdf format.)

"Identifiability in Causal Bayesian Networks: A Gentle Introduction" (with Yimin Huang)
Cybernetics and Systems, 39, 4 (May 2008), pp.425-442. (local copy) (Click here for a preprint in pdf format.) (Click here for galley proofs in pdf format.)

"Logical and Probabilistic Reasoning to Support Information Analysis in Uncertain Domains" (with John Byrnes and Michael Huhns)
Proceedings of the Third Workshop on Combining Probabilty and Logic (Progic-07), Canterbury, England, September 5-7, 2007. (Click here for a preprint in pdf format.)

"Pearl's Calculus of Intervention is Complete" (with Yimin Huang)
Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence (UAI-06), Cambridge, MA, July 13-16, 2006, pp.217-224. This paper won the Best Student Paper award, sharing it with Ilya Shpitser and Judea Pearl's "Identification of Conditional Interventional Distributions." (Click here for the pdf version.)

"Identifiability in Causal Bayesian Networks: A Sound and Complete Algorithm" (with Yimin Huang)
Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06) Boston, MA, July 16-20, 2006, pp. 1149-1154. (Click here for the pdf version.)

"Sequential and Parallel Algorithms for Causal Explanation with Background Knowledge" (with Bhaskara Reddy Moole)
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 12, Suppl. (October 2004), 101-122. (Click here for the pdf version of an almost final draft)

"A Prototypical System for Soft Evidential Update" (with Young-Gyun Kim and Jiri Vomlel)
Applied Intelligence, 21, 1 (July-August 2004), 81-97, 2004. (Click here for gzipped postscript version of an almost final draft.) (Click here for pdf version.)

"Causal Explanation with Background Knowledge" (with Bhaskara Reddy Moole)
Proceedings of the First Indian International Conference on Artificial Intelligence, Hyderabad, India, December 18-20, 2003. (Click here for pdf version of an almost final draft.)

"Soft Evidential Update for Probabilistic Multiagent Systems" (with Young-Gyun Kim and Jiri Vomlel)
International Journal of Approximate Reasoning, 29, 1 (January 2002), 71-106. (Click here for pdf version.) (Click here for postscript version of an almost final draft.)

"Probability and Agents" (with Michael H. Huhns)
IEEE Internet Computing, 6, 6 (November-December 2001), 77-79. (Click here for pdf version of an almost final draft.)

"A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian Networks and Its Complexity" (with Mark Bloemeke)
In: G.F. Cooper and S. Moral (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Fourteenth Conference. San Francisco, CA: Morgan-Kaufmann, 1998, 16-23. (Click here for postscript version.) (Click here for pdf version.)

"On the Detection of Conflicts in Diagnostic Bayesian Networks Using Abstraction" (with Young-Gyun Kim)
In: Ph. Besnard and S. Hanks (eds.), Uncertainty in Artificial Intelligence: Proceedings of the Eleventh Conference. San Francisco, CA: Morgan-Kaufmann, 1995, 362-367. (Click here for postscript version.) (Click here for pdf version.)

"Refinement of Uncertain Rule Bases via Reduction" (with Charles X.F. Ling)
International Journal of Approximate Reasoning, 13, 2 (August 1995), 95-126. (Click here for postscript version.) (Click here for pdf version.)

"Construction of Bayesian Belief Networks from Data: a Brief Survey and an Efficient Algorithm" (with Moninder Singh)
International Journal of Approximate Reasoning, 12, 2 (February 1995), 111-131. (Click here for postscript version.) (Click here for pdf version.)

Moninder Singh and Marco Valtorta. "An Algorithm for the Construction of Bayesian Networks from Data." Proceedings of the 9th International Conference on Uncertainty in Artificial Intelligence (UAI-93), 259-265, 1993. (Click here for pdf version.)

"On the Complexity of Belief Network Synthesis and Refinement" (with Donald W. Loveland)
International Journal of Approximate Reasoning, 7, 3-4 (October-November 1992), 121-148. (Click here for postscript version without figures.) (Click here for pdf version.)

Shijie Wang and Marco Valtorta. "On the Conversion of Rule Bases into Belief Networks." Proceedings of the 1992 ACM/SIGAPP Symposium on Applied Computing: Technological Challeges of the 1990s (SAC-92). 363-368, Kansas City, Missouri, 1992. (local copy).

"Statistical Consistency with Dempster's Rule on Diagnostic Trees Having Uncertain Performance Parameters" (with Stephen D. Durham and Jeffery S. Smolka)
International Journal of Approximate Reasoning, 6, 1 (January 1992), 67-81. (Click here for pdf version.)

"Some results on the computational complexity of refining rule strengths"
International Journal of Approximate Reasoning, 5, 2 (March 1991), p.123-148. (Click here for postscript version without figures.)

"A prototype belief network-based expert system shell" (with Shijie Wang)
Proceedings of the Third International Conference on Industrial and Engineering Applications of AI and Expert Systems, 1990, pp. 509-518. (local copy)

Applications of Bayesian Networks:

Michael Huhns, Marco Valtorta, and Jingsong Wang, "Design Principles for Ontological Support of Bayesian Evidence Management," in: Obrst L, Janssen T, Ceusters W. (eds.), Semantic Technologies, Ontologies, and Information Sharing for Intelligence Analysis, IOS Press, Amsterdam, pp 163-178, 2010 (local copy).

"Towards a Method for Data Accuracy Assessment Utilizing a Bayesian Network Learning Algorithm." (with Valerie Sessions)
Journal of Data and Information Quality, 1, 3 (December 2009), Article 14 (34 pages). (Click here for local copy of the paper)

"Ontological Support for Bayesian Evidence Management" (with Michael Huhns)
Proceedings of the Second International Ontology for the Intelligence Community Conference (OIC-2007) (CD-ROM), Columbia, MD, November 28-29, 2007, pp.47-52. (Click here for pdf version from the conference digital library); (Click here for complete proceedings, edited by Kathleen Stewart Hornsby); (Click here for local copy of the paper)

"The Effects of Data Quality on Machine Learning Algorithms" (with Valerie Sessions)
Proceedings of the 11th International Conference on Data Quality (ICIQ-06) pp.485-498, Cambridge, MA, November 10-12. This paper won the Best Academic Paper award. (Click here for local copy of the paper)

"PAID: A Probabilistic Agent-Based Intrusion Detection System" (with Vaibhav Gowadia and Csilla Farkas)
Computers and Security, 24, 7 (October 2005), 529-545. (Click here for an almost final version in pdf)

"Extending Heuer's Analysis of Competing Hypotheses Method to Support Complex Decision Analysis" (with Jiangbo Dang, Hrishikesh Goradia, Jingshan Huang, and Michael Huhns)
Proceedings of the 2005 International Conference on Intelligence Analysis (IA-05) (CD-ROM), Washington, D.C., May 2-4, 2005, 2 pages. (Click here for pdf version from the conference digital library); (Click here for local copy of the paper) (Click here for a copy of the Technical Report version of the paper in pdf) (Click here for a copy of the Technical Report version of the paper in doc)

"Building Bayesian Network Models in Medicine: the MENTOR Experience" (with Subramani Mani and Suzanne McDermott)
Applied Intelligence, 22, 2 (March/April 2005), pp.93-108 (in press). (local copy) (Click here for an almost final version in gzipped postscript) (Click here for an almost final version in pdf)

"OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services" (corresponding author, with John Cheng, Ray Emami, Larry Kerschberg, Eugene Santos, Jr., Qunhua Zhao, Nien Nguyen, Hua Wang, Michael Huhns, Jiangbo Dang, Hrishikesh Goradia, Jingshan Huang, and Sharon XI)
Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS38) (CD-ROM), Big Island, Hawaii, January 3-6, 2005, 10 pages. (Click here for pdf version from the conference digital library) (Click here for local copy of the paper)

"Agent Based Intrusion Detection with Soft Evidence" (with Vaibhav Gowadia and Csilla Farkas)
(Click here for MSWord version.)
Proceedings of the 14th Information Resources Association (IRMA) International Conference, Philadelphia, PA, May 18, 2003, pp. 140-143.

"Building a Bayesian Network Model of Heart Disease (Extended Abstract)" (with Jayanta K. Ghosh)
Proceedings of the 38th Annual ACM Southeastern Conference (ACMSE00), Clemson, SC, April 7-8, 2000, pp.239-240. (Click here for postscript version.)
(Full version available as Technical Report TR9911, Department of Computer Science, University of South Carolina, November 1999.)

"MENTOR: A Bayesian Model for Prediction of Mental Retardation in Newborns" (with Subramani Mani and Suzanne McDermott)
Research in Developmental Disabilities, 18 (1997), 5, 303-318 (local copy).

Heuristic Search:

"Tie-Breaking Rules for 4xn Warnsdorff's Tours" (with M. Ishaq Zahid)
Congressus Numerantium, 95 (1993), 75-86.

"Warnsdorff's Tours of a Knight" (with M. Ishaq Zahid)
Journal of Recreational Mathematics, 25 (1993), 4, 263-275.

"A New Result on the Complexity of Heuristic Estimates for the A* Algorithm" (with Othar Hansson and Andrew Mayer)
Artificial Intelligence, 55, 1 (May 1992), 129-143. (Click here for postscript version.) (Click here for pdf version.)

"A result on the computational complexity of heuristic estimates for the A* algorithm"
Information Sciences, vol. 34, 1984, p. 47-59. (Click here for pdf version.)

Knowledge Engineering:

"A Lightweight Tool for Automatically Extracting Causal Relationships from Text" (with Stephen V. Cole, Matthew D. Royal, Michael N. Huhns, and John B. Bowles)
Proceedings of IEEE SoutheastCon, pp.125-129, 2006. (Click here for pdf version.)

"Polynomial Time Model-Based Diagnosis with the Critical Set Algorithm" (with Rita L. Childress)
Working Notes of the Fourth International Workshop on Principles of Diagnosis (DX-93), (1993), pp.166-177. (Click here for postscript version.) (Click here for pdf version.)

"Knowledge Base Refinement: A Bibliography."
Applied Intelligence, 1, 1 (July 1991), pp.87-94.

"The Graduate Course Advisor: A Multi-Phase Rule-Based Expert System" (with Bruce T. Smith and Donald W. Loveland)
Proceedings of the IEEE Workshop on Principles of Knowledge-Based Systems, November 3-4, 1984, pp.53-57. (Click here for pdf version.)

"Detecting ambiguity: An example in knowledge evaluation" (with Donald W. Loveland)
Proceedings of the 8th International Joint Conference on Artificial Intelligence , 1983, pp.182-184 (Reprinted in Gupta, U. (ed) Validating and Verifying Knowledge-Based Systems, Los Alamitos, CA: IEEE Computer Society Press, 1991.

Data used in the Mentor Project (zipped)

Expert-modified network build in the Mentor project

Networks for Section 5 of "Probabilistic Agents Systems and the Rumor Problem" (zipped)