Efficient Active Fusion for Decision-Making via VOI Approximation

Abstract

Active fusion is a process that purposively selects the most informative information from multiple sources as well as combines these information for achieving a reliable result efficiently. This paper presents a general mathematical framework based on Influence Diagrams (IDs) for active fusion and timely decision making. Within this framework, an approximation algorithm is proposed to efficiently compute nonmyopic value-of-information (VOI) for multiple sensory actions. Meanwhile a sensor selection algorithm is proposed to choose optimal sensory action sets efficiently. Both the experiments with synthetic data and real data from a real-world application demonstrate that the proposed framework together with the algorithms are well suited to applications where the decision must be made efficiently and timely from dynamically available information of diverse and disparate sources.