DISTRIBUTED INFERENCE AND NETWORKS
See the article about this paper in Science Daily, dated 14 Jan 2009.
J. M. Agosta, Jaideep Chandrashekar Anti-worm Dynamics in Distributed Detection Adaptive and Resilient Computing Security Workshop (ARCS06), August 2006. PDF
Denver Dash, Branislav Kveton, J. M. Agosta, Eve Schooler, Jaideep Chandrashekar, Abraham Bachrach and Alex Newman, ”When gossip is good:” Distributed Probabilistic Inference For Detection Of Slow Network Intrusions. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, AAAI Press, Menlo Park, California, 2006.
J. M. Agosta, Distributed decision agents for managability and security Business Network Fellowship Statement of Purpose, Santa Fe Institute, March 2006. PDF
Denver Dash, J. M. Agosta, Jaideep Chandrashekar, Eve Schooler, A Distributed Host-based Worm Detection System. Proceedings of the ACM SIGCOMM Workshop on Large Scale Attack Defense (LSAD06), September, 2006.
Denver Dash, J. M. Agosta, Abraham Bachrach, Branislav Kveton, Alex Newman, Eve Schooler, Learning robust generative models for distributed anomaly detection. Intelligence Beyond the Desktop, In conjunction with the Nineteenth annual conference on Neural Information Processing Systems (NIPS), 2005.
J. M. Agosta, Abraham Bachrach, Denver Dash, Branislav Kveton, Alex Newman, Eve Schooler, Distributed inference to detect a network attack. Adaptive and Resilient Computing Security Workshop (ARCS05), 2005. PDF
J. M. Agosta, S. Crosby, Network Integrity by Inference in Distributed Systems NIPS workshop, Robust Communication Dynamics in Complex Networks Abstract Whistler, BC, Canada. December 12-13, 2003.
J. M. Agosta, Hormuzd Khosravi,
Authenticated Distributed Detection And Inference
US Patent 7,921,453. Filed 12/2006, issued 4/2011.
Alex P Newman, Toby Kohlenberg, J. M. Agosta. Inappropriate Access Detector Based On System Segmentation Faults, Filed 7/2006 Pending.
Simon Crosby, J. M. Agosta and Denver Dash, A Method For Distributed Sequential Hypothesis Testing In Autonomic Computing Systems. US Patent 7,603,461 B2. Filed 12/2004, Issued 10/2009.
Simon Crosby, J. M. Agosta and Denver Dash. A Method for Collaborative Attack Detection in Networked Computer Systems. Filed 11/2004 Pending.
DIAGNOSIS AND RECOGNITION
Omar Zia Khan, Pascal Poupart and J. M. Agosta, Iterative Model Refinement of Recommender MDPs based on Expert Feedback European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013, (ECMLPKDD13), Prague, Czechoslovakia, September 23-27, 2013. PDF
Omar Zia Khan, Pascal Poupart and J. M. Agosta, Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints Neural Information Processing Systems 2011 (NIPS 11), Granada, Spain, December 12-15, 2011. PDF
Agosta, J. M., Omar Zia Khan and Pascal Poupart, Evaluation Results for a Query-Based Diagnostics Application The Fifth European Workshop on Probabilistic Graphical Models (PGM 10) Helsinki, Finland, September 13-15, 2010. PDF
Agosta, J. M. and Thomas Gardos, Bayes Network “Smart Diagnostics” in Intel Technology Journal “Toward The Proactive Enterprise”, Vol 8, Issue 4 (November 17, 2004) pp.361-372. PDF
Agosta, J. M. and Jonathan S. Katz, The use of Evidence Conflict to extend Diagnostic Models in Kai Goebel and Piero Bonissone, Cochairs Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction, Papers from the 2002 AAAI Spring Symposium, AAAI Technical Report SS-02-03, p.9
Agosta, J.M. and Jonathan Weiss, Active Fusion for Diagnosis, Guided by Mutual Information Measures, Proceedings of the 2nd International Conference on Information Fusion, (Sunnyvale, CA, July 1999), pp. 337-344
Millán, E., Agosta, J.M., Perez de la Cruz, J.-L.: Bayesian Student Modelling and the Problem of Parameter Specification. British Journal of Educational Technology 32, 2 (2001) 171-181
Agosta, J. M, “ Approximating the Noisy-Or Model by Naive Bayes” in Peter Haddawy and Steve Hanks, cochairs, Interactive and Mixed-Initiative Decision-Theoretic Systems, Papers from the 1998 AAAI Spring Symposium AAAI Technical Report SS-98-03, p.1
Agosta, J. M. Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response. Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, (San Mateo: Morgan Kaufmann, 1996) pp. 11-19.
Agosta, J. M. Diagramming Influences: The use of Influence Diagrams and Data Flow diagrams in organizational decision-making (DRAFT, October 1994)
Agosta, J. M. and John S. Breese Causal Probability Networks in the process of product support IEEE CAIA '92 Workshop on Artificial Intelligence for Customer Service and Support (Monterey, CA 1992)
Agosta, J. M., 'Conditionally Inter-Causally Independent' node distributions, a property of 'noisy-or' models in B. D'Ambrosio, editor, Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, (San Mateo: Morgan Kaufmann, 1991), pp 9-16
Agosta, J. M., An example of a Bayes network of relations among visual features, in Su-Shing Chen, editor, Stochastic and neural methods in signal processing, image processing and computer vision, (Bellingham, WA: SPIE proceeding series, vol. 1569, 1991)
Agosta, J. M., The structure of Bayes networks for visual recognition, in T.S. Levitt L. N. Kanal and J. F. Lemmer editors, Uncertainty in Artificial Intelligence 4,(New York: Elsevier Science Publishers, 1990) p.397-405
T.S. Levitt, J. M. Agosta and T. O. Binford, Model-Based Influence Diagrams for Machine Vision, in M. Henrion and R. Shachter, editors, Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, (Windsor, Ontario, 18-20 August, 1989) pp. 233-244
DECISION THEORETIC PLANNING
Agosta, J. M.Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response in Eric Horvitz and Finn Jensen, editors, Proceedings of the Twelfth conference on Uncertainty in Artificial Intelligence, (Portland OR, Reed College, 1996)
Agosta, J. M., Representation of Deliberation and Execution Time in Influence Diagrams in Craig Boutilier & Moises Goldszmidt, cochairs, Extending Theories of Action: Formal Theory & Practical Applications, Papers from the 1995 AAAI Symposium AAAI Technical Report SS-95-07, p.1
Agosta, J. M. and Roberto Desimone, Some Questions about Interpreting Plans as Multi-Stage Influence Diagrams in Steve Hanks, Program chair , Decision-Theoretic Planning, Papers from the 1994 Spring AAAI Symposium, AAAI Technical Report SS-94-06, p. 279
AUTOMATED EMERGENCY PLANNING
Agosta, J. M. and David Wilkins Emergency Planning for Marine Oil Spill Incidents, IEEE Expert, (December 1996) pp. 6-8
Agosta, J. M., Formulation and Implementation of an Equipment Configuration Problem with the SIPE2 Generative Planner in Adele Howe, chair, Integrated Planning Applications, Papers from the 1995 AAAI Symposium, AAAI Technical Report SS-95-04, p.1
Desimone, R.V. and J. M. Agosta. Oil Spill Response Simulation: The Application of AI Planning Technology, Proceedings of the 1994 Simulation MultiConference, Simulation for Emergency Management track, (La Jolla, CA, April 1994)
Agosta, J. M., Norman R. Nielsen and Gerry Andeen, Fast Training of Neural Networks for Load Forecasting, Proceedings of the American Power Conference, (Chicago, IL: 9-11 April 1996 )
Agosta, J. M. Soft Logic and Flexible Reasoning: The Real World Computing Project in Japan (Menlo Park, CA: SRI International Business Intelligence Program, Datalog D94-1812, 1994)