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Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection

Yash 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, pp. 3356–3363, January 2015.

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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.

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},
    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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