Office: 214 Olsson Hall
Office Phone: (434) 924-1039
US Mail:Department of Computer Science
Simulation is one of the most widely used applications of computing, and often to good effect. We predict weather, analyze corrosion, predict CO2 absorption, study social behavior, conduct what-if analyses...the list goes on. You'd be hard pressed to name a discipline that doesn't use simulation. However, researchers and policymakers bemoan the fact that different simulation-based studies often yield different results (See the NIH MIDAS studies of smallpox spread for a fine example). What results can policymakers believe? How can they know they can believe them? What can simulation designers and users do to increase confidence in their simulation outcomes? My research is aimed at addressing these questions, and others (e.g. multi-resolution modeling and model composability) whose outcomes are likely to benefit from answers to the questions posed here.
In a general sense my M&S research interests can be classified as follows: 1) Managing model uncertainty (in model structure, parameter choices and output interpretation) for the purpose of enabling user control of and understanding of simulation outcomes, 2) Exploiting model uncertainty (for the purpose of enabling desirable simulation behavior outcomes, which can facilitate interoperability and composability), 3) Designing model exploration tools (for the purpose of understanding sources of observed simulation behaviors) , 4) Integrating simulations with real world systems, including other software systems, about which we may possess only limited understanding, and 5) Conducting model outcome similarity analysis. (Simulation execution is often expensive. Methods other than repeated executions of a simulation, e.g. Monte Carlo suites, are needed to quickly identify families of likely outcomes. Related research would be identification of attractors in chaotic systems likely to determine model outcomes. Weather prediction currently uses Monte Carlo suites.)
Specific on-going research projects with a talented team of PhD students and undergraduates includes:
A substantial amount of effort is expended on adapting existing simulations to meet new requirements. While adaptability is an issue for software in general, it is particularly interesting in the area of simulation: simulations often reflect a large degree of uncertainty, they often employ stochastics and their use is generally aimed more at increasing insight than performing any particular exact function. These attributes lend themselves to specialization when considering adaptation to new requirements. Our research exploits these attributes – in any software, but simulations in particular – in order to make the adaptation process more efficient. We have designed formal languages for expressing the flexibilities that accompany uncertainty and randomness, and we are developing agile optimization methods for searching the resulting design spaces. We have applied our work in high energy physics, global warming studies, corrosion modeling and combustion models to demonstrate its efficacy. Learn more about our COERCE research program and our modeling and simulation technology initiative.
Unexpected model outputs can reflect valid behaviors arising from seemingly unrelated phenomena, or they can reflect errors in model assumptions, design or implementation. We are exploring analysis techniques to richly improve methods for exploring and understanding the behavior of models containing uncertainty. Increased insight gained from analysis will contribute to reduced uncertainty which in turn will increase researcher and policymaker confidence in model results and predictions. The COERCE research team has published an exploratory approach using semi-automated search that allows a user to test hypotheses about unexpected behaviors as a simulated phenomenon is driven towards a condition of interest. Our current work builds upon this work and adds an integrated multidimensional analysis capability of a model and its outputs. The multidimensional analysis combines uncertainty representation, causality analysis, and static and dynamic program slicing to gather insight in a disciplined manner into the interactions within the model that cause unexpected model behavior.
Public policy officials are increasingly turning to modeling and simulation as a means to support important policy decisions. For example, with increased concern about bioterrorism, health officials have actively commissioned the creation of epidemic models in order to better prepare and plan for intervention. Significant amounts of both aleatory and epistemic uncertainty in the models as well as uncertainty about what scenario should be modeled have led to the employment of a high level of both explicit and implicit assumptions in models. If uncertainties are not carefully managed, the end-user does not have a good idea of the overall validity of the model. Consequently inaccurate results may be used to make decisions affecting millions of people and billions of dollars. In order to address this problem, we have analyzed the assumptions made in typical SEIR epidemiology models in order to establish the extent of the uncertainty that exists. Our goal was to make a case for engaging in better practices for managing uncertainty in simulations.
Other ongoing research includes:
Emitters are complex. The challenge of describing their behavior for later analysis is increasingly difficult. Analysts tend to use natural (spoken) language to bridge the gap between the technology available to them for capturing emitter behavior and the complexities of modern emitters. Since computers cannot process natural language descriptions, these descriptions are unavailable to later users who require the speed of computer processing. Emitter behavior should be described formally, i.e., without recourse to natural language. We are developing a technology for formally capturing the complete dynamic behavior of emitters. The principal challenge is to develop an approach that is sufficiently powerful to capture emitter dynamic behavior completely and yet sufficiently intuitive to be used by people who are not computer programmers. Our approach is based on technologies arising from disparate areas: music composition, domain specific languages, and static analysis tools. Music composition technology is relevant to the proposed effort because a major part of the work of both the analyst and the composer is the definition, modification, and layering of waveform sequences.
Isotach! See here.
Ever checked out the cost of assistive technologies for the blind? Our goal is to make open source, open platform assistive technologies freely available. Our current focus is on applications that support restaurant menu browsing and meal selection, and product and personal item identification using a Google Android. Key challenges include item identification algorithms and user preference management.
Gore, R, Reynolds, P, INSIGHT : A Methodology for Understanding Unexpected Behaviors in Agent-Based Models, Journal of Simulation, To Appear.
Liuti, S, Honkanen, H, Carnahan, J, Loitiere,Y, Reynolds, P, New Avenue to the Parton Distribution Functions: Self-Organizing Maps, Physical Review D To Appear.
Gore, R, Reynolds, P, Program Slice Distribution Functions, ACM/IEEEWinter Simulation Conference, To Appear (Dec, 2009).
Gore, R, Reynolds, P, Causal Program Slicing To Understand Program Behavior, IEEE/SCS Workshop on Principles of Advanced and Distributed Simulation, June 2009.
Gore, R, Reynolds, P, Applying Causal Inference to Understand Emergent Behavior, ACM/IEEE Winter Simulation Conference, Dec, 2008.
Tang, L, and Reynolds, P. “Exploiting User Insight for Efficient Simulation Adaptation” 2008 VMASC Capstone M&S Conference
Spiegel M., Gore,R, and Reynolds, P. “Quantifying and Analyzing Uncertainty in Simulations to Enable User Understanding” 2008 VMASC Capstone M&S Conference. Won Best Paper/Presentation Award in its track.
Tang, L. Reynolds, P,“Agile Optimization for Coercion” ACM/IEEE Winter Simulation Conference, Dec, 2007.
Gore,R, Reynolds, P. “An Exploration-Based Taxonomy For Emergent Behavior Analysis In Simulations” ACM/IEEE Winter Simulation Conference, Dec, 2007.
Carnahan, J, Policastro, S, Carson, E, Reynolds, P, Kelly, R. “Using Flexible Points in a Developing Simulation of Selective Dissolution In Alloys” ACM/IEEE Winter Simulation Conference, Dec, 2007.
Gore, R, Reynolds, P, Tang, L. Brogan, D. “Explanation Exploration: Exploring Emergent Behavior” ACM/IEEE Workshop on Principles of Advanced and Distributed Simulation, June, 2007.
Carnahan,J., Reynolds, P. “Requirements for DDDAS Flexible Point Support” 2006 ACM/IEEE Winter Simulation Conference
Spiegel,M., Reynolds, P., Brogan, D.,“Grand Challenge Case Studies in a Simulation Curriculum” 2006 ACM/IEEE Winter Simulation Conference.
Reynolds, P., Spiegel, M, Liu, X, Gore, R, “Validating Evolving Simulations in COERCE” ICCS, 2007.
David C. Brogan, Paul F. Reynolds, Robert G. Bartholet, Joseph C. Carnahan and Yannick Loitiere, "Semi-Automated Simulation Transformation for DDDAS" Workshop on DDDAS. in Proceedings of the International Conference on Computational Science, pp. 721-728, 2005.
Michael Spiegel, Paul F. Reynolds, and David C. Brogan "A Case Study of Context Assumptions for Simulation Composability and Reusability" Proceedings of the Winter Simulation Conference (WSC), pp. 437-444, 2005.
Joseph C. Carnahan, Paul F. Reynolds, and David C. Brogan "Simulation-Specific Characteristics and Software Reuse" Proceedings of the Winter Simulation Conference (WSC), pp. 2492-2499, 2005.
Robert G. Bartholet, David C. Brogan, Paul F. Reynolds "The Computational Complexity of Component Selection in Simulation Reuse" Proceedings of the Winter Simulation Conference (WSC), pp. 2472-2481, 2005
Giordano, J., Reynolds, P. and Brogan, D., “Exploring The Constraints of Human Behavior Representation,” 2004 ACM/IEEE Winter Simulation Conference.
Reynolds, P., Milner, C., Highley, T., Scalable Personalized Learning, Frontiers in Education, October, 2004.
Carnahan,J., Reynolds, P. and Brogan, D., “Visualizing Coercible Simulations,” 2004 ACM/IEEE Winter Simulation Conference.
Fox, M., Brogan, D. and Reynolds, P., “Approximating Component Selection,” 2004 ACM/IEEE Winter Simulation Conference.
Giordano, J. and Reynolds, P., "WARSIM 2000: A Case for Technological Subject Matter Experts," Journal of Defense Modeling and Simulation, Fall, 2004.
Carnahan,J., Reynolds, P. and Brogan, D.,“Language Support for Identifying Flexible Points in Coercible Simulations” Fall ’04 Simulation Interoperability Wkshp
Waziruddin, S., Brogan, D., Reynolds, P. “Selecting Optimization Techniques in Support of Simulation Transformation” Fall ’04 Simulation Interoperability Wkshp.
Bartholet,R., Carnahan,J., Reynolds, P., Brogan, D.,“Semantics, Scope, or Scale: Simulation Composability Versus Component-Based Software Design” Fall ’04 Simulation Interoperability Workshop
Morse, K., Petty, M., Reynolds, P., Waite, W., and Zimmerman, P., Findings and Recommendations from the 2003 Composable Mission Space Environments Workshop, Spring Simulation Interoperability Workshop, 2004 (WON SIWzie Award)
Tavakoli, A., Chu, D., and Reynolds, P., Richer Semantic Representations of Simulations Using DAML/OWL, Spring Simulation Interoperability Workshop, 2004
Loitiµere,Y., Brogan, D., and Reynolds, P., Simulation Coercion Applied to Multiagent DDDAS, ICCS, June 2004.
Waziruddin, S., Brogan, D., and Reynolds, P. "The Process for Coercing Simulations." Simulation Interoperability Workshop (SIW, Fall 2003 (nominated for SIWzie award)
Carnahan, J., Reynolds, P. and Brogan,D., “An Experiment in Simulation Coercion,” I/ITSEC, December, 2003.
Drewry, D., Reynolds, P. and Emanuel, W,, “An Optimization-Based Multi-Resolution Simulation Methodology,” ACM/IEEE 2002 Winter Simulation Conference, Dec, 2002.
P.F. Reynolds, Jr., “Using Space-Time Constraints to Guide Model Interoperability,” Spring ’02 Simulation Interoperability Workshop, Orlando, April, 2002.
C. Williams, P.F. Reynolds, Jr., and B.R. de Supinski, “Delta Coherence Protocols,” IEEE Concurrency, Spring, 2000.
R. Srinivasa, P.F. Reynolds, Jr., and C. Williams, “ISORULE,” IASTED99, Sept 1999.
A. Natrajan, and P.F. Reynolds, Jr., “Resolving Concurrent Interactions” 3rd Distributed Simulation-Real Time Conference, Aug 1999, Washington DC
S. Srinivasan and P.F. Reynolds, Jr. “Elastic Time,” ACM Trans on Modeling and Computer Simulation, 1998.
P.F. Reynolds, Jr., S. Srinivasan, and A. Natrajan, "Consistency Maintenance in Multi-Resolution Simulations," ACM Trans on Modeling and Computer Simulation, July, 1997.
P.F. Reynolds, Jr., C.C. Williams and Raymond R. Wagner, Jr., "Isotach Networks," IEEE Trans on Parallel and Distributed Systems, April 1997.