Dr. John D. Lowrance has been a member of SRI's Artificial Intelligence Center since 1980. He has led and participated in basic and applied research programs in perception, foundations for expert systems, uncertainty calculi for knowledge-based systems, knowledge-based planning methodologies, intelligent simulation, the integration of multi-source knowledge, representations of knowledge, link analysis, and the design and implementation of AI support tools and programming languages. Dr. Lowrance's Ph.D. dissertation introduced the AI community to evidential reasoning, a methodology for representing and reasoning from evidence (i.e., information that is potentially uncertain, incomplete, and inaccurate). He is the former Assistant Director of SRI's AI Center and currently is the Director of that Center's Representation and Reasoning Program. His application-oriented research has developed approaches to multi-sensor integration, knowledge-based simulations, analysis of intelligence data, logistics planning, medical diagnosis, sonar data interpretation, vehicle tracking, forensic accounting, target systems analysis, and management decision aids. In addition, he was the principle architect of Grasper (a programming language that supports interactive graph-processing) and Gister (an evidential-reasoning and argument construction tool). Dr. Lowrance's most recent work is aimed at making evidential reasoning accessible to real world analysts and decision makers. As such, he has been the technical and managerial lead in the development of SEAS (a tool to aid intelligence analysts in recording, understanding, and comparing analytic arguments), along with Angler (a tool to promote divergent and convergent thinking), and LAW (a link analysis tool that finds close matches for graphically specified patterns). Dr. Lowrance received his A.B. in Computer Science and Mathematics from Indiana University, and M.S. and Ph.D. in Computer and Information Science from the University of Massachusetts. Dr. Lowrance has numerous publications in conference proceedings, journals, and books since 1974.

	Angler Angler is a tool that helps intelligence/policy professionals explore, understand, and overcome cognitive biases, and collaboratively expand their joint cognitive vision through use of divergent & convergent thinking techniques (such as brainstorming and clustering).
	Gister: An Evidential Reasoning System SRI pioneered evidential reasoning for drawing conclusions from multiple sources of evidential information about dynamic real-world situations. We have developed formal foundations for reasoning under uncertainty covering both probabilistic models (i.e., Bayesian and Dempster-Shafer) and possibilistic models (i.e., propositional logic and fuzzy logic) and have incorporated all of these techniques into a single uncertain reasoning tool, Gister.
	Grasper: An Interactive Network Editor and Graphical Database Grasper is a system for viewing and manipulating graph-structured information, and for building graph-based user interfaces for application programs.
	LAW: Link Analysis Workbench LAW is a system that helps intelligence professionals define and match patterns within large, incomplete, and noisy sets of relational data.
	SEAS: Structured Evidential Argumentation System SEAS is a tool to aid analysts in reasoning about potential opportunities/crises. It records analytic thinking in structured arguments, provides a collaborative environment in which multiple analysts can simultaneously contribute to arguments, and retains a coproate memory of the evolution of analytic thinking over time.

The following publications are selected by the author. They are listed in reverse chronological order.

• John D. Lowrance. Graphical Manipulation of Evidence in Structured Arguments. Oxford Journal of Law, Probability and Risk, vol. 6, pp. 225-240, 2007.  [PDF, Details]

• Andres Rodriguez, Thomas Boyce, John Lowrance, Eric Yeh. Angler: Collaboratively Expanding your Cognitive Horizon, in International Conference on Intelligence Analysis, 2005.  [PDF, Details]

• Murray, K. and Lowrance, J. and Appelt, D. and Rodriguez, A. Estimating Similarity among Collaboration Contributions, in Third International Conference on Knowledge Capture, 2005.  [PDF, Details]

• Wolverton, M. and Harrison, I. and Lowrance, J. and Rodriguez, A. and Thomere, J. Supporting the Pattern Development Cycle in Intelligence Gathering, in Proceedings of the International Conference on Intelligence Analysis (IA’05), 2005.  [Details]

• Lowrance, John D., Harrison, Ian W., and Rodriguez, Andres C. Capturing Analytic Thought. Proceeding of the First International Conference on Knowledge Capture, pp. 84-91, October 2001.  [Details]

• Paley, S.M., Lowrance, J.D., and Karp, P.D. A Generic Knowledge-Base Browser and Editor, in Proceedings of the 1997 National Conference on Artificial Intelligence, 1997.  [PS, Details]

• Karp, P. D. and Lowrance, J. D. and Strat, T. M. and Wilkins, D. E. The Grasper-CL Graph Management System. LISP and Symbolic Computation, vol. 7, pp. 245-282, 1994.  [PS, Details]

• Ruspini, Enrique H. and Lowrance, John D. and Strat, Thomas M. Understanding Evidential Reasoning. International Journal of Approximate Reasoning, vol. 6, no. 3, pp. 401-424, May 1992.  [Details]

• Lowrance, John D. and Wilkins, David E. Plan Evaluation under Uncertainity. in Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control, Morgan Kaufmann Publishers Inc., San Mateo, CA, Nov 1990.  [PS, Details]

• Lowrance, John D. and Garvey, Thomas D. and Strat, Thomas M. A Framework for Evidential-Reasoning Systems. in Uncertain Reasoning, Morgan Kaufman Publishers, Inc., 1990.  [Details]

• Strat, Thomas M. and Lowrance, John D. Explaining Evidential Analyses. International Journal of Approximate Reasoning, vol. 3, no. 4, pp. 299-353, Jul 1989.  [Details]

• Lowrance, John D. Automated Argument Construction. Journal of Statistical Planning and Inference, vol. 20, pp. 369-387, 1988.  [Details]

• Lowrance, John D. Dependency-Graph Models of Evidential Support, PhD Thesis. Department of Computer and Information Science, University of Massachusetts, Amherst, MA, Sep 1982.  [Details]

• Lowrance, John D. and Friedman, Daniel P. Hendrix's Model for Simultaneous Actions and Continuous Processes: An Introduction and Implementation. International Journal of Man-Machine Studies, vol. 9, pp. 537-581, 1977.  [Details]