Learning the expected utility of sensors and algorithms

Lindner, John F.; Murphy, Robin R.; Nitz, E.

doi:10.1109/mfi.1994.398401

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…The quantification of domain knowledge still relies on the competence of the knowledge engineer; while this is no different than other implementations, either DS or Bayesian, it certainly calls for additional research. Our preliminary efforts [26] indicate that such knowledge can be learned.…”

Section: Discussionmentioning

confidence: 88%

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Dempster-Shafer theory for sensor fusion in autonomous mobile robots

Murphy

1998

IEEE Trans. Robot. Automat.

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184

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This article discusses Dempster-Shafer (DS) theory in terms of its utility for sensor fusion for autonomous mobile robots. It exploits two little used components of DS theory: the weight of conflict metric and the enlargement of the frame of discernment. The weight of conflict is used to measure the amount of consensus between different sensors. A lack of consensus leads the robot to either compensate within certain limits or investigate the problem further, adding robustness to the robot's operation. Enlarging the frame of discernment allows a modular decomposition of evidence. This decomposition offers the advantages of perceptual abstraction, and permits expert knowledge about the domain to be embedded in the frames of discernment, simplifying the construction and maintenance of the knowledge base. Six experiments using this Dempster-Shafer framework are presented. Data from four types of sensor data were collected by a mobile robot and fused with the Sensor Fusion Effects (SFX) architecture.

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Section: Discussionmentioning

confidence: 88%

“…Since enlargement functions represent changes in the assumptions about the contribution of evidence across two frames of discernment, they are generally heuristic in nature [27]. SFX requires the knowledge engineer to encode the function; however, work by Lindner, Murphy, and Nitz [26] considers how to learn these rules.…”

Section: A Representation Of Evidencementioning

confidence: 99%

Dempster-Shafer theory for sensor fusion in autonomous mobile robots

Murphy

1998

IEEE Trans. Robot. Automat.

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184

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“…Lindner et al [32] estimate the expected utility of each sensor to predict the minimum cost subset of sensors for a mobile robot application. Kristensen [29] develops a Bayesian decision tree to solve the problem of choosing proper sensing actions from a family of candidates.…”

Section: A Sensor Selection Criteriamentioning

confidence: 99%

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

Liao

Wallace

2009

IEEE Trans. Syst., Man, Cybern. A

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Abstract-As sensors become more complex and prevalent, they present their own issues of cost effectiveness and timeliness. It becomes increasingly important to select sensor sets that provide the most information at the least cost and in the most timely and efficient manner. Two typical sensor selection problems appear in a wide range of applications. The first type involves selecting a sensor set that provides the maximum information gain within a budget limit. The other type involves selecting a sensor set that optimizes the tradeoff between information gain and cost. Unfortunately, both require extensive computations due to the exponential search space of sensor subsets. This paper proposes efficient sensor selection algorithms for solving both of these sensor selection problems. The relationships between the sensors and the hypotheses that the sensors aim to assess are modeled with Bayesian networks, and the information gain (benefit) of the sensors with respect to the hypotheses is evaluated by mutual information. We first prove that mutual information is a submodular function in a relaxed condition, which provides theoretical support for the proposed algorithms. For the budget-limit case, we introduce a greedy algorithm that has a constant factor of (1 − 1/e) guarantee to the optimal performance. A partitioning procedure is proposed to improve the computational efficiency of the algorithms by efficiently computing mutual information as well as reducing the search space. For the optimal-tradeoff case, a submodular-supermodular procedure is exploited in the proposed algorithm to choose the sensor set that achieves the optimal tradeoff between the benefit and cost in a polynomial-time complexity.

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“…Clementine [14] and CSL [24] both learn sensor utilities, including which sensor to use for what information. LIVE [21] learns a model of the environment, as well as the costs of applying actions in that environment.…”

Section: Introductionmentioning

confidence: 99%

Learning situation-dependent costs

Haigh

Veloso

1998

Proceedings of the Second International Conference on Autonomous Agents - AGENTS '98

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Real world robot tasks are so complex that it is hard to hand-tune all of the domain knowledge, especially to model the dynamics of the environment. Several research efforts focufl on applying machine learning to map learning, sensor/action mapping, and vision. The work presented in this paper explores machine learning techniques for robot planning. The goal is to use real robotic navigational execution as a data source for learning.Our system collects execution traces, and extracts relevant information to improve the efficiency of generated plnns, In this article, we present the representation of the path plnnner nnd the navigation modules, and describe the execution trace. We show how training data is extracted from the execution trace,We introduce the concept of situation-dependent costs, where situational features can be attached to the costs used hy the p&h planner. In this way, the planner can generate pAdIf that are appropriate for a given situation. We present experimental results from a simulated, controlled environment as well as from data collected from the actual robot.

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Learning the expected utility of sensors and algorithms

Cited by 8 publications

References 5 publications

Dempster-Shafer theory for sensor fusion in autonomous mobile robots

Dempster-Shafer theory for sensor fusion in autonomous mobile robots

Approximate Nonmyopic Sensor Selection via Submodularity and Partitioning

Learning situation-dependent costs

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