Task-Directed Computation of Qualitative Decisions from Sensor Data

This article describes a novel approach to sensor - based decision making based on formulating and solving large systems of parametric constraints. The constraints describe both a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique that performs decision-directed model recovery is developed. This method is straightforward to apply, is easily parallelized, and convergence can be demonstrated under very reasonable structural and statistical assumptions. This approach is demonstrated on several different decision-making problems involving manipulation and categorization of objects observed with a range scanner. The experiments indicate that simultaneous solution of both model constraints and decision criteria can lead to efficient and effective decision making, even when the observed data does not strongly determine a data model.