Technology Category
Reasoning under Uncertainty: Stochastic OptimizationSearch and Optimization: Mixed Discrete/Continuous SearchPlanning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
A Probabilistic Approach to Trajectory-Based Optimal Experimental Design
🤖 AI Summary
This work addresses the challenges of discrete path optimization and black-box utility evaluation in trajectory-based optimal experimental design by modeling trajectories as stochastic variables governed by a parameterized Markov policy. This formulation recasts path optimization as a stochastic optimization problem over policy parameters, thereby introducing probabilistic modeling and Markov decision policies into trajectory-based experimental design for the first time. The approach enables effective exploration of the tail regions of the utility function and is applicable to both linear and nonlinear inverse problems. By integrating a static navigation mesh, a parameterized policy, and black-box utility evaluations, the method demonstrates strong performance in canonical parameter identification tasks, highlighting its broad applicability in model-driven optimal experimental design.
Technology Category
Application Category
📝 Abstract
We present a novel probabilistic approach for optimal path experimental design. In this approach a discrete path optimization problem is defined on a static navigation mesh, and trajectories are modeled as random variables governed by a parametric Markov policy. The discrete path optimization problem is then replaced with an equivalent stochastic optimization problem over the policy parameters, resulting in an optimal probability model that samples estimates of the optimal discrete path. This approach enables exploration of the utility function's distribution tail and treats the utility function of the design as a black box, making it applicable to linear and nonlinear inverse problems and beyond experimental design. Numerical verification and analysis are carried out by using a parameter identification problem widely used in model-based optimal experimental design.
Problem
Research questions and friction points this paper is trying to address.
optimal experimental design
trajectory-based
discrete path optimization
inverse problems
utility function
Innovation
Methods, ideas, or system contributions that make the work stand out.
probabilistic trajectory optimization
optimal experimental design
Markov policy
stochastic optimization
black-box utility
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