27,130 research outputs found
Active Classification: Theory and Application to Underwater Inspection
We discuss the problem in which an autonomous vehicle must classify an object
based on multiple views. We focus on the active classification setting, where
the vehicle controls which views to select to best perform the classification.
The problem is formulated as an extension to Bayesian active learning, and we
show connections to recent theoretical guarantees in this area. We formally
analyze the benefit of acting adaptively as new information becomes available.
The analysis leads to a probabilistic algorithm for determining the best views
to observe based on information theoretic costs. We validate our approach in
two ways, both related to underwater inspection: 3D polyhedra recognition in
synthetic depth maps and ship hull inspection with imaging sonar. These tasks
encompass both the planning and recognition aspects of the active
classification problem. The results demonstrate that actively planning for
informative views can reduce the number of necessary views by up to 80% when
compared to passive methods.Comment: 16 page
Controlled Sensing for Multihypothesis Testing
The problem of multiple hypothesis testing with observation control is
considered in both fixed sample size and sequential settings. In the fixed
sample size setting, for binary hypothesis testing, the optimal exponent for
the maximal error probability corresponds to the maximum Chernoff information
over the choice of controls, and a pure stationary open-loop control policy is
asymptotically optimal within the larger class of all causal control policies.
For multihypothesis testing in the fixed sample size setting, lower and upper
bounds on the optimal error exponent are derived. It is also shown through an
example with three hypotheses that the optimal causal control policy can be
strictly better than the optimal open-loop control policy. In the sequential
setting, a test based on earlier work by Chernoff for binary hypothesis
testing, is shown to be first-order asymptotically optimal for multihypothesis
testing in a strong sense, using the notion of decision making risk in place of
the overall probability of error. Another test is also designed to meet hard
risk constrains while retaining asymptotic optimality. The role of past
information and randomization in designing optimal control policies is
discussed.Comment: To appear in the Transactions on Automatic Contro
Sequentiality and Adaptivity Gains in Active Hypothesis Testing
Consider a decision maker who is responsible to collect observations so as to
enhance his information in a speedy manner about an underlying phenomena of
interest. The policies under which the decision maker selects sensing actions
can be categorized based on the following two factors: i) sequential vs.
non-sequential; ii) adaptive vs. non-adaptive. Non-sequential policies collect
a fixed number of observation samples and make the final decision afterwards;
while under sequential policies, the sample size is not known initially and is
determined by the observation outcomes. Under adaptive policies, the decision
maker relies on the previous collected samples to select the next sensing
action; while under non-adaptive policies, the actions are selected independent
of the past observation outcomes.
In this paper, performance bounds are provided for the policies in each
category. Using these bounds, sequentiality gain and adaptivity gain, i.e., the
gains of sequential and adaptive selection of actions are characterized.Comment: 12 double-column pages, 1 figur
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