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. >

Authors

• Gregory D. Hager

Keywords

• Decision making
• Sensor phenomena and characterization
• Parameter estimation
• Data models
• Parametric statistics
• Computer science
• Convergence
• Constraint theory
• Sensor fusion
• Decision-making
• Model Parameters
• Parameter Estimates
• Sensor Data
• Assumption Of Distribution
• Parameter Vector
• Size Parameters
• Solution Space
• Set Of Observations
• Vector Data
• Subintervals
• Bisection
• Decision Criteria
• Unique Class
• System Constraints
• Decision-making Problems
• Sensor Model
• Real Vector
• Form Of Constraints
• Extensive Mapping