Constraint solving methods and sensor-based decision-making

The author describes a novel approach to sensor-based decision-making that involves formulating and solving large systems of parametric constraints. The constraints describe a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique performs the minimal model recovery required to reach a decision. The approach was demonstrated on two different problems, graspability and categorization, using range data and a superellipsoid data model. The experiments indicated that simultaneous solution of both data constraints and decision criteria can lead to be efficient and effective decision-making. even when the observed data was imprecise and incomplete