R. Paul Wiegand's Research |

Home | Academics | Misc. Interests |

## Recent or Current Work## Applications of Machine Learning in GIS## Reasoning about Behavior Patterns Given Extremely Sparse ObservationsDr. Steve Prager and I consider the situationis where observations of entities operating within some spatially-embedded network are sparse and the goal is to learn as much as possible about their ## Spatial Generalization of Behavior PatternsDr. Prager and I have also investigated means of learning behavioral patterns on spatially-embedded networks in such a way as to generalize over " ## Connecting Historical Items## RICHES MOSAIC Interface (TM) ConnectionsThe RICHES Mosaic Interface (TM)i is an digital humanities project to provide multiple search methods for historical information relating to the Central Florida area, with a particular focus on visual methods for exploiting spatial and temporal relationships. Our group is working on ways of inducing topological relationships between historical items in the repository to allow teachers and historians to find implicit relationships between objects in the repository in terms of time, space, topic modeling, and keywords. the central Internet location for content created through the RICHESâ„˘ projects and links to sources on Central Florida available from other repositories around the state. RICHES Mosaic Interfaceâ„˘ combines temporal and spatial results with text analysis techniques to help you find hidden connections in the archive.## Parallel, Distributed Modeling & Simulation## Hybrid GPU, Distributed Linear Solvers for Sparse Matrix FEA MethodsDescription coming soon...## Distributed Constructive Simulation on HPC ResourcesDescription coming soon...## Past Projects: |

I worked with the Adaptive Systems Group at the Naval Center for Applied Research in Artificial Intelligence (NCARAI) and Bill Spears at the University of Wyoming to extend and generalize his physicomimetics framework for multiagent reactive control. Here an artificial physics system is constructed in order to develop a self-organizing team of agents capable of a wide variety of behaviors. It is particularly well-suited to behaviors that involves geometric formations. We developed a generalized, graph-based design method for constructing modular and scalable heterogeneous behaviors for teams of robots and applied it to a variety of multiagent problems, such as resource protection. Early work suggests that team formations resilliant to attrition and re-deployment of platforms for quite natural to develop using such representations. |

Coevolutionary algorithms behave in very complicated, often
quite counterintuitive ways. As a result, engineers using such
algorithms need directed investigations to help practitioners
understand what particular coevolutionary algorithms are good
at, what they are not, and why. In my research, my colleagues
and have chosen to examine a particular class of coevolutionary
algorithms useful to solving compositional,
or cooperative tasks, the multi-population symmetric
coevolutionary algorithm (MPS-CEA). |

One way to investigate the optimization potential of multi-population symmetric coevolutionary algorithms (MPS-CEAs) is simply to analyze its performance on such problems. Thomas Jansen and I have applied tools from randomized algorithm analysis to do exactly this, attempting to answer the question: "What is the expected number of evaluations the algorithm must make in order to find the global optimum?" We have mainly concentrated our attention on the CC (1+1) EA, a particular subclass of coevolutionary algorithms. We have also investigated some population-based approaches. |

Multi-population symmetric coevolutionary algorithms (MPS-CEAs) offer great potential for concurrent multiagent learning domains and are of special utility to games involving teams of multiple agents. Unfortunately, these algorithms exhibit pathologies resulting from their game-theoretic nature, and these pathologies interfere with finding solutions that correspond to optimal collaborations of interacting agents. |