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2021 International Conference Engineering and Telecommunication, En and T 2021

作者： Khachumov, Mikhail Khachumov, Vyacheslav Systems Institute of RAS Intelligent Control Laboratory Program Pereslavl-Zalessky Russia RUDN University Federal Research Center 'Computer Science and Control' of RAS Information Technologies Department Moscow Russia

ISBN: (纸本)9781728183909

We solve the planar case of the urgent pursuit-evasion problem for players using various strategies of motion. The following options are considered: players perform piecewise-linear motions;the pursuer moves in a straight line, and the evader moves in a circle;both players move along circular arcs. We propose schemes to solve the formulated problem based on the construction of Apollonius circles, which are geometric invariants. As participants of the game, we assume unmanned aerial vehicles (UAVs) operating in a non-deterministic environment. To solve the problem under uncertainty we use the theory of intelligent geometric control aimed at the joint application of complementary methods of precise geometric and flexible intelligent control as parts of one robotic system. Experimental studies of pursuit-evasion games are carried out in the MATLAB system using mathematical models of UAVs motion and wind loads. © 2021 IEEE.

关键词： intelligent control

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Solution to the Problem of Passing Over the Given Targets by an Unmanned Aerial Vehicle in an Unstable Environment

Solution to the Problem of Passing Over the Given Targets by...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Khachumov, Mikhail Intelligent Control Laboratory Ailamazyan Program Systems Institute of Ras Pereslavl-Zalessky Russia Federal Research Center "Computer Science and Control"of Ras Moscow Russia Rudn University Moscow Russia

ISBN: (纸本)9798350345551

The paper gives a statement and considers the solution to an urgent problem of flying over the given targets by an unmanned aerial vehicle (UAV) in unstable conditions. A criterion is formulated for constructing efficient routes and passing arbitrary located sites according to a given formal description of the terrain map with the presence of interferences. Heuristic rules are proposed aimed at minimizing the graph of the initial map and reducing the number of alternative flight routes, given the time constraints for completing the task. The search of the solution in this case can be carried out according to the base tree of routes. A heuristic algorithm for searching efficient routes in a limited time is considered, which has the complexity that ensures its implementation on the UAV onboard computer. It is substantiated that the proposed approach can significantly reduce the number of comparing alternatives for flying around given arbitrary located targets in comparison with the well-known methods for solving this problem. The paper describes a successful example of solving the UAV route planning problem with the proposed approach, which is complicated by the presence of wind flows affecting the speed and trajectory. © 2023 IEEE.

关键词： Motion planning

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Rapid modeling and analysis with QGENIE

Rapid modeling and analysis with QGENIE

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International Multiconference on computer science and Information Technology, IMCSIT '09

作者： Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Faculty of Computer Science Bialystok Technical University Wiejska 45A 15-351 Bialystok Poland

ISBN: (纸本)9781424453146

QGENIE is a specialized interface to GENIE, a decision modeling environment developed by the Decision systems Laboratory, University of Pittsburgh. QGENIE allows for rapid construction of graphical models in which all variables are propositional, almost no numerical probabilities are displayed at the user interface, and degrees of truth of propositions are displayed by means of node colors. All numerical parameters, such as prior probability distributions over variables and strengths of influences between variables, are entered by means of graphical sliders. While the underlying computations are all numerical and based on Bayesian updating, QGENIE makes the impression of a qualitative, "order of magnitude" type system that aids rapid model building and an approximate analysis of systems. © 2009 IEEE.

关键词： Probability distributions

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Does query-based diagnostics work?

Does query-based diagnostics work?

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8th Bayesian Modeling ApplicationsWorkshop, BMAW 2011

作者： Ratnapinda, Parot Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A 15-351 Bialystok Poland

Query-based diagnostics (Agosta, Gardos, & Druzdzel, 2008) offers passive, incremental construction of diagnostic models that rest on the interaction between a diagnostician and a computer-based diagnostic system. Effectively, this approach minimizes knowledge engineering, the main bottleneck in practical application of Bayesian networks. While this idea is appealing, it has undergone only limited testing in practice. We describe a series of experiments that subject a prototype implementing passive, incremental model construction to a rigorous practical test. We show that the prototype's diagnostic accuracy reaches reasonable levels after merely tens of cases and continues to increase with the number of cases, comparing favorably to state of the art approaches based on learning.

关键词： Bayesian networks

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An empirical evaluation of costs and benefits of simplifying Bayesian networks by removing weak arcs 27

An empirical evaluation of costs and benefits of simplifying...

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27th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2014

作者： Ratnapinda, Parot Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Faculty of Computer Science Bialystok University of Technology Wiejska 45A Bialystok15-351 Poland

We report the results of an empirical evaluation of structural simplification of Bayesian networks by removing weak arcs. We conduct a series of experiments on six networks built from real data sets selected from the UC Irvine Machine Learning Repository. We systematically remove arcs from the weakest to the strongest, relying on four measures of arc strength, and measure the classification accuracy of the resulting simplified models. Our results show that removing up to roughly 20 percent of the weakest arcs in a network has minimal effect on its classification accuracy. At the same time, structural simplification of networks leads to significant reduction of both the amount of memory taken by the clique tree and the amount of computation needed to perform inference. Copyright © 2014, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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PAC Bayesian performance guarantees for deep (Stochastic) networks in medical imaging

arXiv

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arXiv 2021年

作者： Sicilia, Anthony Zhao, Xingchen Sosnovskikh, Anastasia Hwang, Seong Jae Intelligent Systems Program University of Pittsburgh Department of Computer Science University of Pittsburgh

Application of deep neural networks to medical imaging tasks has in some sense become commonplace. Still, a "thorn in the side" of the deep learning movement is the argument that deep networks are prone to overfitting and are thus unable to generalize well when datasets are small (as is common in medical imaging tasks). One way to bolster confidence is to provide mathematical guarantees, or bounds, on network performance after training which explicitly quantify the possibility of overfitting. In this work, we explore recent advances using the PAC-Bayesian framework to provide bounds on generalization error for large (stochastic) networks. While previous efforts focus on classification in larger natural image datasets (e.g., MNIST and CIFAR-10), we apply these techniques to both classification and segmentation in a smaller medical imagining dataset: the ISIC 2018 challenge set. We observe the resultant bounds are competitive compared to a simpler baseline, while also being more explainable and alleviating the need for holdout sets. © 2021, CC BY.

关键词： Medical imaging

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Importance sampling for general hybrid Bayesian networks

Importance sampling for general hybrid Bayesian networks

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11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Yuan, Changhe Druzdzel, Marek J. Department of Computer Science and Engineering Mississippi State University Mississippi State MS 39762 United States Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States

Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler model. However, results thus obtained highly depend on the quality of the approximations. This paper describes an importance sampling-based algorithm that directly deals with hybrid Bayesian networks constructed in the most general settings and guarantees to converge to the correct answers given enough time.

关键词： Importance sampling

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Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition

Incorporating Geo-Diverse Knowledge into Prompting for Incre...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kyle Buettner Sina Malakouti Xiang Lorraine Li Adriana Kovashka Intelligent Systems Program Department of Computer Science University of Pittsburgh PA USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose, we explore the feasibility of probing a large language model for geography-based object knowledge, and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration, we propose geog-raphy knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an un-seen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas, and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training, and analysis is provided to direct future study of geographical robustness.

关键词： Geography Training computer vision Large language models Training data Europe Robustness

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Insensitivity of constraint-based causal discovery algorithms to violations of the assumption of multivariate normality

Insensitivity of constraint-based causal discovery algorithm...

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21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21

作者： Voortman, Mark Druzdzel, Marek J. Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA 15260 United States Faculty of Computer Science Bialystok Technical University Wiejska 45A 15-351 Bialystok Poland

ISBN: (纸本)9781577353652

Constraint-based causal discovery algorithms, such as the PC algorithm, rely on conditional independence tests and are otherwise independent of the actual distribution of the data. In case of continuous variables, the most popular conditional independence test used in practice is the partial correlation test, applicable to variables that are multivariate Normal. Many researchers assume multivariate Normal distributions when dealing with continuous variables, based on a common wisdom that minor departures from multivariate Normality do not have a too strong effect on the reliability of partial correlation tests. We subject this common wisdom to a test in the context of causal discovery and show that partial correlation tests are indeed quite insensitive to departures from multivariate Normality. They, therefore, provide conditional independence tests that are applicable to a wide range of multivariate continuous distributions. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Normal distribution

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Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth

Boosting Weakly Supervised Object Detection using Fusion and...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Cagri Gungor Adriana Kovashka Intelligent Systems Program University of Pittsburgh Department of Computer Science University of Pittsburgh

Despite recent attention to depth for various tasks, it is still an unexplored modality for weakly-supervised object detection (WSOD). We propose an amplifier method for enhancing the performance of WSOD by integrating depth information. Our approach can be applied to different WSOD methods based on multiple-instance learning, without necessitating additional annotations or inducing large computational cost. Our proposed method employs monocular depth estimation to obtain hallucinated depth information, which is then incorporated into a Siamese WSOD network using contrastive loss and fusion. By analyzing the relationship between language context and depth, we calculate depth priors to identify the bounding box proposals that may contain an object of interest. These depth priors are then utilized to update the list of pseudo ground-truth boxes, or adjust the confidence of per-box predictions. We evaluate our proposed method on three datasets (COCO, PASCAL VOC, and Conceptual Captions) by implementing it on top of two state-of-the-art WSOD methods, and we demonstrate a substantial enhancement in performance.

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