The road below has no forks, nor is it fog-covered, but you still can’t predict what lies ahead. Making decisions under uncertainty involves more than being presented with multiple options and choosing the best one. The problem is much more complex because the forks and options are not readily seen.
Inevitably, plans go wrong. Plans for robots and plans for humans. It’s impossible to predict all the ways plans may go wrong—or how to fix them. Nilufer Onder works to create algorithms to address and fix plans—from construction management to the Mars rover and microarchitecture. Her research spans interdisciplinary areas where uncertainty is prevalent.
Simulator Verification: Searching for a Base Truth
Simulators are large, complex pieces of code. Simulation developers continually modify the code to adapt to ever-changing technology. Onder and her team from Michigan Tech, including Zhenlin Wang and Soner Onder, developed a graphical structure to automatically derive verification constraints from simulator traces. SFTAGs (state-flow temporal analysis graphs) take into account stochastic paths and durations taken by events that are being simulated.
Constructing Parallel Plans
Automatic generation of robust plans that operate in realistic domains involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal set of goals to work on. Creating plans that consider all of these features is a computationally complex problem addressed with the planner CPOAO (concurrent probabilistic oversubscribed planning using AO). CPOAO includes novel domain independent heuristics and pruning techniques to reduce the search space.
Risk-Informed Project Management
The construction industry is the largest single production activity in the US economy–accounting for nearly 10 percent of the gross national product. Contingencies commonly cause delays and added costs in construction projects. Onder’s work involves providing automated techniques to avoid and respond to contingencies.
Together with Amlan Mukherjee, a researcher in civil and environmental engineering at Michigan Tech, Onder created a learning environment for construction management students to predict and address change. “Students take a construction plan and overlay it with events that cause delays. Then we ask students to react to the scenarios,” she explains.
Onder’s team developed ICDMA (interactive construction decision-making aid) which uses AI-planning technology to predict the paths a project can take.
Student Persistence in Engineering and Computer Science
Careers in engineering and computer science usually promise a well-paying and -respected job. However, approximately 55 percent of US students leave these fields within six years, choosing a non-STEM field or leaving higher education altogether. Onder’s group investigates the complex issues surrounding student persistence, including who influences career choices, what factors affect changing majors, and the under-representation issues involved in staying in a major.