Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • Exploiting Submodular Value Functions for Faster Dynamic Sensor SelectionYash Satsangi, Shimon Whiteson, and Frans A. Oliehoek. Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pp. 3356–3363, January 2015. DownloadAbstractA key challenge in the design of multi-sensor systems is the efficient allocation of scarce resources such as bandwidth, CPU cycles, and energy, leading to the dynamic sensor selection problem in which a subset of the available sensors must be selected at each timestep. While partially observable Markov decision processes (POMDPs) provide a natural decision-theoretic model for this problem, the computational cost of POMDP planning grows exponentially in the number of sensors, making it feasible only for small problems. We propose a new POMDP planning method that uses greedy maximization to greatly improve scalability in the number of sensors. We show that, under certain conditions, the value function of a dynamic sensor selection POMDP is submodular and use this result to bound the error introduced by performing greedy maximization. Experimental results on a real-world dataset from a multi-camera tracking system in a shopping mall show it achieves similar performance to existing methods but incurs only a fraction of the computational cost, leading to much better scalability in the number of cameras. BibTeX Entry@InProceedings{Satsangi15AAAI, author = {Yash Satsangi and Shimon Whiteson and Frans A. Oliehoek}, title = {Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection}, booktitle = AAAI15, month = jan, year = 2015, pages = {3356--3363}, url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9978}, abstract = { A key challenge in the design of multi-sensor systems is the efficient allocation of scarce resources such as bandwidth, CPU cycles, and energy, leading to the dynamic sensor selection problem in which a subset of the available sensors must be selected at each timestep. While partially observable Markov decision processes (POMDPs) provide a natural decision-theoretic model for this problem, the computational cost of POMDP planning grows exponentially in the number of sensors, making it feasible only for small problems. We propose a new POMDP planning method that uses greedy maximization to greatly improve scalability in the number of sensors. We show that, under certain conditions, the value function of a dynamic sensor selection POMDP is submodular and use this result to bound the error introduced by performing greedy maximization. Experimental results on a real-world dataset from a multi-camera tracking system in a shopping mall show it achieves similar performance to existing methods but incurs only a fraction of the computational cost, leading to much better scalability in the number of cameras. } }
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