Efficient Value of Information Computation

One of the most useful sensitivity analysis techniques of decision analysis is the computation of value of information (or clairvoyance), the difference in value obtained by changing the decisions by which some of the uncertainties are observed. In this paper, some simple but powerful extensions to previous algorithms are introduced which allow an efficient value of information calculation on the rooted cluster tree (or strong junction tree) used to solve the original decision problem.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999

Learning to select computations

The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning

Sequential Information Elicitation in Multi-Agent Systems

We introduce the study of sequential information elicitation in strategic multi-agent systems. In an information elicitation setup a center attempts to compute the value of a function based on private information (a-k-a secrets) accessible to a set of agents. We consider the classical multi-party computation setup where each agent is interested in knowing the result of the function. However, in our setting each agent is strategic,and since acquiring information is costly, an agent may be tempted not spending the efforts of obtaining the information, free-riding on other agents' computations. A mechanism which elicits agents' secrets and performs the desired computation defines a game. A mechanism is 'appropriate' if there exists an equilibrium in which it is able to elicit (sufficiently many) agents' secrets and perform the computation, for all possible secret vectors.We characterize a general efficient procedure for determining an appropriate mechanism, if such mechanism exists. Moreover, we also address the existence problem, providing a polynomial algorithm for verifying the existence of an appropriate mechanism.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004

On the Compositionality of Dynamic Leakage and Its Application to the Quantification Problem

Quantitative information flow (QIF) is traditionally defined as the expected value of information leakage over all feasible program runs and it fails to identify vulnerable programs where only limited number of runs leak large amount of information. As discussed in Bielova (2016), a good notion for dynamic leakage and an efficient way of computing the leakage are needed. To address this problem, the authors have already proposed two notions for dynamic leakage and a method of quantifying dynamic leakage based on model counting. Inspired by the work of Kawamoto et. al. (2017), this paper proposes two efficient methods for computing dynamic leakage, a compositional method along with the sequential structure of a program and a parallel computation based on the value domain decomposition. For the former, we also investigate both exact and approximated calculations. From the perspective of implementation, we utilize binary decision diagrams (BDDs) and deterministic decomposable negation normal forms (d-DNNFs) to represent Boolean formulas in model counting. Finally, we show experimental results on several examples.Comment: preprin

Max-value Entropy Search for Efficient Bayesian Optimization

Entropy Search (ES) and Predictive Entropy Search (PES) are popular and empirically successful Bayesian Optimization techniques. Both rely on a compelling information-theoretic motivation, and maximize the information gained about the $\arg\max$ of the unknown function; yet, both are plagued by the expensive computation for estimating entropies. We propose a new criterion, Max-value Entropy Search (MES), that instead uses the information about the maximum function value. We show relations of MES to other Bayesian optimization methods, and establish a regret bound. We observe that MES maintains or improves the good empirical performance of ES/PES, while tremendously lightening the computational burden. In particular, MES is much more robust to the number of samples used for computing the entropy, and hence more efficient for higher dimensional problems.Comment: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 201

Block-based quantum-logic synthesis

In this paper, the problem of constructing an efficient quantum circuit for the implementation of an arbitrary quantum computation is addressed. To this end, a basic block based on the cosine-sine decomposition method is suggested which contains $l$ qubits. In addition, a previously proposed quantum-logic synthesis method based on quantum Shannon decomposition is recursively applied to reach unitary gates over $l$ qubits. Then, the basic block is used and some optimizations are applied to remove redundant gates. It is shown that the exact value of $l$ affects the number of one-qubit and CNOT gates in the proposed method. In comparison to the previous synthesis methods, the value of $l$ is examined consequently to improve either the number of CNOT gates or the total number of gates. The proposed approach is further analyzed by considering the nearest neighbor limitation. According to our evaluation, the number of CNOT gates is increased by at most a factor of $\frac{5}{3}$ if the nearest neighbor interaction is applied.Comment: 15 pages, 8 figures, 5 tables, Quantum Information and Computation (QIC) Journa

Myopic Policy Bounds for Information Acquisition POMDPs

This paper addresses the problem of optimal control of robotic sensing systems aimed at autonomous information gathering in scenarios such as environmental monitoring, search and rescue, and surveillance and reconnaissance. The information gathering problem is formulated as a partially observable Markov decision process (POMDP) with a reward function that captures uncertainty reduction. Unlike the classical POMDP formulation, the resulting reward structure is nonlinear in the belief state and the traditional approaches do not apply directly. Instead of developing a new approximation algorithm, we show that if attention is restricted to a class of problems with certain structural properties, one can derive (often tight) upper and lower bounds on the optimal policy via an efficient myopic computation. These policy bounds can be applied in conjunction with an online branch-and-bound algorithm to accelerate the computation of the optimal policy. We obtain informative lower and upper policy bounds with low computational effort in a target tracking domain. The performance of branch-and-bounding is demonstrated and compared with exact value iteration.Comment: 8 pages, 3 figure

Efficient Estimation of the Value of Information in Monte Carlo Models

The expected value of information (EVI) is the most powerful measure of sensitivity to uncertainty in a decision model: it measures the potential of information to improve the decision, and hence measures the expected value of outcome. Standard methods for computing EVI use discrete variables and are computationally intractable for models that contain more than a few variables. Monte Carlo simulation provides the basis for more tractable evaluation of large predictive models with continuous and discrete variables, but so far computation of EVI in a Monte Carlo setting also has appeared impractical. We introduce an approximate approach based on pre-posterior analysis for estimating EVI in Monte Carlo models. Our method uses a linear approximation to the value function and multiple linear regression to estimate the linear model from the samples. The approach is efficient and practical for extremely large models. It allows easy estimation of EVI for perfect or partial information on individual variables or on combinations of variables. We illustrate its implementation within Demos (a decision modeling system), and its application to a large model for crisis transportation planning.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994

A graph-theoretical approach for the computation of connected iso-surfaces based on volumetric data

The existing combinatorial methods for iso-surface computation are efficient for pure visualization purposes, but it is known that the resulting iso-surfaces can have holes, and topological problems like missing or wrong connectivity can appear. To avoid such problems, we introduce a graph-theoretical method for the computation of iso-surfaces on cuboid meshes in $\mathbb{R}^3$. The method for the generation of iso-surfaces employs labeled cuboid graphs $G(V,E,\mathcal{F})$ such that $V$ is the set of vertices of a cuboid $C\subset\mathbb{R}^3$, $E$ is the set of edges of $C$ and $\mathcal{F}\,:\,V\rightarrow [0,1]$. The nodes of $G$ are weighted by the values of $\mathcal{F}$ which represents the volumetric information, e.g.\ from a Volume of Fluid method. Using a given iso-level $c\in (0,1)$, we first obtain all iso-points, i.e.\ points where the value $c$ is attained by the edge-interpolated $\mathcal{F}$-field. The iso-surface is then built from iso-elements which are composed of triangles and are such that their polygonal boundary has only iso-points as vertices. All vertices lie on the faces of a single mesh cell. We give a proof that the generated iso-surface is connected up to the boundary of the domain and it can be decomposed into different oriented components. Two different components may have discrete points or line segments in common. The graph-theoretical method for the computation of iso-surfaces developed in this paper enables to recover local information of the iso-surface that can be used e.g.\ to compute discrete mean curvature and to solve surface PDEs. Concerning the computational effort, the resulting algorithm is as efficient as existing combinatorial methods

On Computation of Error Locations and Values in Hermitian Codes

We obtain a technique to reduce the computational complexity associated with decoding of Hermitian codes. In particular, we propose a method to compute the error locations and values using an uni-variate error locator and an uni-variate error evaluator polynomial. To achieve this, we introduce the notion of Semi-Erasure Decoding of Hermitian codes and prove that decoding of Hermitian codes can always be performed using semi-erasure decoding. The central results are: * Searching for error locations require evaluating an univariate error locator polynomial over $q^2$ points as in Chien search for Reed-Solomon codes. * Forney's formula for error value computation in Reed-Solomon codes can directly be applied to compute the error values in Hermitian codes. The approach develops from the idea that transmitting a modified form of the information may be more efficient that the information itself.Comment: 10 pages, Submitted to ITW 2008 (with some minor modifications