Steve Brophy’s research while affiliated with Concordia University Ann Arbor and other places

At a tactical level, insurgents planning attacks with Improvised Explosive Devices (IEDs) are constrained in their choice of target by the specific location of their safe house or weapons cache, the geographic context in which they operate, and the pattern of potential targets as it presents itself at a given time. Geographic profiling in law-enforcement already takes advantage of similar constraints to identify possible origin locations of serial offenders. We show how geographic profiling of past IED events can significantly enhance our ability to identify areas at risk for future attacks. Specifically, we introduce three tightly coupled swarming pattern analysis models (profiling, clustering, forecasting) that refine each others' conclusions dynamically and point to systematic evaluation experiments that confirm the research hypothesis.

Reasoning about agents that we observe in the world is challenging. Our available information is often limited to observations of the agent's external behavior in the past and present. To understand these actions, we need to deduce the agent's internal state, which includes not only rational elements (such as intentions and plans), but also emotive ones (such as fear). In addition, we often want to predict the agent's future actions, which are constrained not only by these inward characteristics, but also by the dynamics of the agent's interaction with its environment. BEE (Behavior Evolution and Extrapolation) uses a faster-than-real-time agent-based model of the environment to characterize agents' internal state by evolution against observed behavior, and then predict their future behavior, taking into account the dynamics of their interaction with the environment.

Reasoning about agents that we observe in the world is challeng-ing. Our available information is often limited to observations of the agent's external behavior in the past and present. To under-stand these actions, we need to deduce the agent's internal state, which includes not only rational elements (such as intentions and plans), but also emotive ones (such as fear). In addition, we often want to predict the agent's future actions, which are constrained not only by these inward characteristics, but also by the dynamics of the agent's interaction with its environment. BEE (Behavior Evolution and Extrapolation) uses a faster-than-real-time agent-based model of the environment to characterize agents' internal state by evolution against observed behavior, and then predict their future behavior, taking into account the dynamics of their interaction with the environment.

Sometimes it is desirable to measure the difference between the spatial trajectories of two or more agents. The naïve measure (the sum of Euclidean distances between locations at successive timesteps) increases with the lengths of the trajectories, which is not suitable for some applications. This paper explains the problem that motivates such a comparison, describes the design of the comparison that we are using, and gives an example of its ap-plication.

Reasoning about agents that we observe in the world is challenging. Our available information is often limited to observations of the agent's external behavior. To under-stand these actions, we need to deduce the agent's internal state, which includes not only rational elements (such as in-tentions and plans), but also emotive ones (such as fear). In addition, we often want to predict the agent's future actions, which are constrained not only by these inward characteris-tics, but also by the dynamics of the agent's interaction with its environment. BEE (Behavior Evolution and Extrapola-tion) uses a faster-than-real-time agent-based model of the environment to characterize agents' internal state by evolu-tion against observed behavior, and then predict their future behavior, taking into account the dynamics of their interac-tion with the environment.