Search

An online group testing method to search for a hidden object in a discrete search space is proposed. A relevant example is a search after a nonconforming unit in a batch, while many other applications can be related. A probability mass function is defined over the search space to represent the probability of an object (e.g., a nonconforming unit) to be located at some point or subspace. The suggested method follows a stochastic local search procedure and can be viewed as a generalization of the Learning Real-Time A* (LRTA*) search algorithm, while using informational distance measures over the searched space. It is proved that the proposed Informational LRTA* (ILRTA*) algorithm converges and always terminates. Moreover, it is shown that under relevant assumptions, the proposed algorithm generalizes known optimal information-theoretic search procedures, such as the offline Huffman search or the generalized optimum testing algorithm. However, the ILRTA* can be applied to new situations, such as a search with side information or an online search where the probability distribution changes. The obtained results can help to bridge the gap between different search procedures that are related to quality control, artificial intelligence, and information theory. [ABSTRACT FROM PUBLISHER]

This paper presents the performance of the Weight-Balanced Testing (WBT) algorithm with multiple testers. The WBT algorithm aims to minimize the expected number of (round of) tests and has been proposed for coding, memory storage, search and testing applications. It often provides reasonable results if used with a single tester. Yet, the performance of the WBT algorithm with multiple testers and particularly its upper bound have not been previously analyzed, despite the large body of literature that exists on the WBT algorithm, and the recent papers that suggest its use in various testing applications. Here we demonstrate that WBT algorithm with multiple testers is far from being the optimal search procedure. The main result of this paper is the generalization of the upper bound on the expected number of tests previously obtained for a single-tester WBT algorithm. For this purpose, we first draw an analogy between the WBT algorithm and alphabetic codes; both being represented by the same Q-ary search tree. The upper bound is then obtained on the expected path length of a Q-ary tree, which is constructed by the WBT algorithm. Applications to the field of testing and some numerical examples are presented for illustrative purposes. [ABSTRACT FROM AUTHOR]

Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of finite-state processes. The method utilizes a series of context-tree models to estimate the conditional distribution of the process output given the context of previous observations. The Kullback-Leibler divergence statistic is derived to indicate significant changes in the trees along the process. The method is implemented in a simulated flexible manufacturing system in order to detect significant changes in its production mix ratio output. [ABSTRACT FROM AUTHOR]

This paper presents a method for unsupervised classification of entities by a group of agents with unknown domains and levels of expertise. In contrast to the existing methods based on majority voting ("wisdom of the crowd") and their extensions by expectation-maximization procedures, the suggested method first determines the levels of the agents' expertise and then weights their opinions by their expertise level. In particular, we assume that agents will have relatively closer classifications in their field of expertise. Therefore, the expert agents are recognized by using a weighted Hamming distance between their classifications, and then the final classification of the group is determined from the agents' classifications by expectation-maximization techniques, with preference to the recognized experts. The algorithm was verified and tested on simulated and real-world datasets and benchmarked against known existing algorithms. We show that such a method reduces incorrect classifications and effectively solves the problem of unsupervised collaborative classification under uncertainty, while outperforming other known methods. [ABSTRACT FROM AUTHOR]

Projects are rarely executed exactly as planned. Often, the actual duration of a project's activities differ from the planned duration, resulting in costs stemming from the inaccurate estimation of the activity's completion date. While monitoring a project at various inspection points is pricy, it can lead to a better estimation of the project completion time, hence saving costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project's path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using information theory measures. We search for monitoring (inspection) points that can maximize the information about the project's estimated duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities' durations. An exhaustive search is performed of all possible monitoring points to find those with the highest expected information gain on the project duration. The proposed algorithm's complexity is little affected by the number of activities, and the algorithm can address large projects with hundreds or thousands of activities. Numerical experimentation and an analysis of various parameters are presented. [ABSTRACT FROM AUTHOR]

This paper addresses the problem classification of instances/questions based on the opinions (classes) provided by anonymous agents. The solution aggregates the agents' classifications, aiming to obtain as close as possible to an unknown correct classification. However, the agents' fields or domains of competence and their levels of expertise are unknown and can vary extensively. Many popular classification algorithms address such a problem by following a "wisdom-of-the-crowd" approach while using different voting methods and expectation–maximization techniques. These algorithms lead to correct classifications when the majority of the agents are experts, thus classifying the instances correctly. However, they often result in erroneous classification when only a small subset of the agents are indeed correct. Moreover, these algorithms often assume a fixed set of classes for all instances. This study presents a fast (one-pass) classification algorithm that can estimate the unknown agents' expertise level and aggregates their classifications accordingly, even when these are obtained from different questionnaires; thus, when the instances are not necessarily classified to a fixed set of classes. The proposed algorithm finds the experts and the nonexpert agents for each question by analyzing the distance between them. The algorithm identifies the expert agents for each instance and then classifies them accordingly. The suggested algorithm is validated and compared against known methods by using both simulated datasets and real-world datasets collected from various sources. The obtained results clearly demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]