Results 1  10
of
18
Nearoptimal nonmyopic value of information in graphical models
 In Annual Conference on Uncertainty in Artificial Intelligence
"... A fundamental issue in realworld systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset of variables in a graphical model. We present ..."
Abstract

Cited by 88 (17 self)
 Add to MetaCart
A fundamental issue in realworld systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset of variables in a graphical model. We present the first efficient randomized algorithm providing a constant factor (1 − 1/e − ε) approximation guarantee for any ε> 0 with high confidence. The algorithm leverages the theory of submodular functions, in combination with a polynomial bound on sample complexity. We furthermore prove that no polynomial time algorithm can provide a constant factor approximation better than (1 − 1/e) unless P = NP. Finally, we provide extensive evidence of the effectiveness of our method on two complex realworld datasets. 1
Optimal nonmyopic value of information in graphical models  efficient algorithms and theoretical limits
 In Proc. of IJCAI
, 2005
"... Many realworld decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. It has been general practice to use heuristicguided ..."
Abstract

Cited by 37 (5 self)
 Add to MetaCart
Many realworld decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. It has been general practice to use heuristicguided procedures for selecting observations. In this paper, we present the first efficient optimal algorithms for selecting observations for a class of graphical models containing Hidden Markov Models (HMMs). We provide results for both selecting the optimal subset of observations, and for obtaining an optimal conditional observation plan. For both problems, we present algorithms for the filtering case, where only observations made in the past are taken into account, and the smoothing case, where all observations are utilized. Furthermore we prove a surprising result: In most graphical models tasks, if one designs an efficient algorithm for chain graphs, such as HMMs, this procedure can be generalized to polytrees. We prove that the value of information problem is NP PPhard even for discrete polytrees. It also follows from our results that even computing conditional entropies, which are widely used to measure value of information, is a #Pcomplete problem on polytrees. Finally, we demonstrate the effectiveness of our approach on several realworld datasets. 1
Voila: Efficient featurevalue acquisition for classification
 In TwentySecond Conference on Artificial Intelligence (AAAI
, 2007
"... We address the problem of efficient featurevalue acquisition for classification in domains in which there are varying costs associated with both feature acquisition and misclassification. The objective is to minimize the sum of the information acquisition cost and misclassification cost. Any decisi ..."
Abstract

Cited by 16 (3 self)
 Add to MetaCart
We address the problem of efficient featurevalue acquisition for classification in domains in which there are varying costs associated with both feature acquisition and misclassification. The objective is to minimize the sum of the information acquisition cost and misclassification cost. Any decision theoretic strategy tackling this problem needs to compute value of information for sets of features. Having calculated this information, different acquisition strategies are possible (acquiring one feature at time, acquiring features in sets, etc.). However, because the value of information calculation for arbitrary subsets of features is computationally intractable, most traditional approaches have been greedy, computing values of features one at a time. We make the problem of value of information calculation tractable in practice by introducing a novel data structure called the Value of Information Lattice (VOILA). VOILA exploits dependencies between missing features and makes sharing of information value computations between different feature subsets possible. To the best of our knowledge, performance differences between greedy acquisition, acquiring features in sets, and a mixed strategy have not been investigated empirically in the past, due to inherit intractability of the problem. With the help of VOILA, we are able to evaluate these strategies on five real world datasets under various cost assumptions. We show that VOILA reduces computation time dramatically. We also show that the mixed strategy outperforms both greedy acquisition and acquisition in sets.
Selective Perception Policies for Guiding Sensing and Computation in Multimodal Systems: A Comparative Analysis
 Comput. Vis. Image Underst
, 2003
"... Intensive computations required for sensing and processing perceptual information can impose significant burdens on personal computer systems. We explore several policies for selective perception in SEER, a multimodal system for recognizing o#ce activity that relies on a layered Hidden Markov Model ..."
Abstract

Cited by 16 (4 self)
 Add to MetaCart
Intensive computations required for sensing and processing perceptual information can impose significant burdens on personal computer systems. We explore several policies for selective perception in SEER, a multimodal system for recognizing o#ce activity that relies on a layered Hidden Markov Model representation. We review our e#orts to employ expectedvalueofinformation (EVI) computations to limit sensing and analysis in a contextsensitive manner. We discuss an implementation of a onestep myopic EVI analysis and compare the results of using the myopic EVI with a heuristic sensing policy that makes observations at di#erent frequencies. Both policies are then compared to a random perception policy, where sensors are selected at random. Finally, we discuss the sensitivity of ideal perceptual actions to preferences encoded in utility models about information value and the cost of sensing.
Optimal Value of Information in Graphical Models
"... Many realworld decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. In medical decision making tasks, one needs to select ..."
Abstract

Cited by 16 (5 self)
 Add to MetaCart
Many realworld decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. In medical decision making tasks, one needs to select which tests to administer before deciding on the most effective treatment. It has been general practice to use heuristicguided procedures for selecting observations. In this paper, we present the first efficient optimal algorithms for selecting observations for a class of probabilistic graphical models. For example, our algorithms allow to optimally label hidden variables in Hidden Markov Models (HMMs). We provide results for both selecting the optimal subset of observations, and for obtaining an optimal conditional observation plan. Furthermore we prove a surprising result: In most graphical models tasks, if one designs an efficient algorithm for chain graphs, such as HMMs, this procedure can be generalized to polytree graphical models. We prove that the optimizing value of information is NP PPhard even for polytrees. It also follows from our results that just computing decision theoretic value of information objective functions, which are commonly used in practice, is a #Pcomplete problem even on Naive Bayes models (a simple special case of polytrees). In addition, we consider several extensions, such as using our algorithms for scheduling observation selection for multiple sensors. We demonstrate the effectiveness of our approach on several realworld datasets, including a prototype sensor network deployment for energy conservation in buildings. 1.
Value of information lattice: Exploiting probabilistic independence for effective feature subset acquisition
 Journal of Artificial Intelligence Research
"... We address the costsensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features. Because acquiring the features is costly as well, the objective is to acquire the right set of ..."
Abstract

Cited by 6 (2 self)
 Add to MetaCart
We address the costsensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features. Because acquiring the features is costly as well, the objective is to acquire the right set of features so that the sum of the feature acquisition cost and misclassification cost is minimized. We describe the Value of Information Lattice (VOILA), an optimal and efficient feature subset acquisition framework. Unlike the common practice, which is to acquire features greedily, VOILA can reason with subsets of features. VOILA efficiently searches the space of possible feature subsets by discovering and exploiting conditional independence properties between the features and it reuses probabilistic inference computations to further speed up the process. Through empirical evaluation on five medical datasets, we show that the greedy strategy is often reluctant to acquire features, as it cannot forecast the benefit of acquiring multiple features in combination. 1.
Bayesian Belief Networks: Odds and Ends
 The Computer Journal
, 1996
"... In artificial intelligence research, the belief network framework for automated reasoning with uncertainty is rapidly gaining in popularity. The framework provides a powerful formalism for representing a joint probability distribution on a set of statistical variables. ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
In artificial intelligence research, the belief network framework for automated reasoning with uncertainty is rapidly gaining in popularity. The framework provides a powerful formalism for representing a joint probability distribution on a set of statistical variables.
Integrating learning from examples into the search for diagnostic policies
 Artificial Intelligence
, 1998
"... This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decisionmaking actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decisionmaking actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which isthe sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeo between these two kinds of costs. This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO algorithm to solve this MDP.To makeAO e cient, the paper describes an admissible heuristic that enables AO to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previouslypublished methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of over tting. To reduce over tting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on ve benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today's desktop computers. 1.
Efficient MultipleDisorder Diagnosis by Strategic Focusing
, 1994
"... The belief network framework is becoming increasingly popular for building diagnostic knowledge based systems. The framework is especially suited for the task of diagnosis because it provides for modelling and dealing with multiple interacting disorders. However, this ability often is exploited ins ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
The belief network framework is becoming increasingly popular for building diagnostic knowledge based systems. The framework is especially suited for the task of diagnosis because it provides for modelling and dealing with multiple interacting disorders. However, this ability often is exploited insufficiently due to the computational complexity involved. In this paper, we present a method for multipledisorder diagnosis with a belief network that derives its efficiency from focusing on small sets of related disorders which are constructed by taking advantage of the independencies portrayed by the graphical part of the network. 1 Introduction Although diagnosing multiple disorders has been a longstanding concern of knowledgebased systems research, it is only recently that fundamental paradigms for dealing with multiple disorders have begun to arise. The paradigms of modelbased reasoning [ Reiter, 1987 ] and abductive reasoning [ Peng and Reggia, 1990 ] especially are tuned to mult...