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Planning of proactive behaviors for human-robot cooperative tasks under uncertainty

KNOWLEDGE-BASED SYSTEMS 2014年 72卷 81-95页

作者： Kwon, Woo Young Suh, Il Hong Hanyang Univ Dept Elect & Comp Engn Seoul 133791 South Korea

For seamless human-robot cooperation, a robot may need to take several steps proactively to minimize unnecessary delays between the human's intention and the robot's corresponding reactions. By predicting exogenous events from human intention and generating proactive plans based on the predicted events, a robot can reduce delays and significantly improve interaction. In this paper, we propose a decision-theoretic proactive planning framework that selects best proactive actions and the best times for those actions as a means to improving human-robot interactions. To this end, we developed a composite node temporal Bayesian network as an extension to handle both the nature of an event and its time of occurrence within a single framework. We also developed a composite node temporal influence diagram that combines a composite node temporal Bayesian network with a limited memory influence diagram to solve proactive planning problems. Experimental results obtained using a robot assistant in a manual assembly task demonstrate the effectiveness of our proposed framework. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Temporal planning Proactive interaction Decision theoretic model probabilistic graphical model Proactive planning

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Mathematical modeling of coastal marine environments using observational data for coastal management

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OCEAN & COASTAL MANAGEMENT 2015年 116卷 396-403页

作者： Kim, Jinah Park, Jinah KIOST Coastal Disaster Res Ctr Ansan South Korea Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

Coastal development affects human life and economic activity. Given a necessity to develop coastal areas, there is a need for a method by which to understand and quantitatively assess and predict changes to coastal marine environments. In this paper, we propose a mathematical modeling for coastal marine environments using observational data. In particular, we establish probabilistic graphical model based on the data-driven statistical model for Saemangeum coast of Korea, where land reclamation work has been taking place. The derived model consists of latent and observation variables and their causal relationships. Ocean currents occurred by water exchange appear to be the key factor influencing the coastal marine environments in the artificial lake of Saemangeum coast. Hence, coastal water quality in the coastal management is the major concern by stakeholders. Using the proposed model we were able to compute the followings to demonstrate the usage of our proposed model: First, if the lake were to be entirely cut off from sea water exchange (which takes place through sluice gates in the sea dyke to the open sea), coastal water quality may deteriorated to approximately 37.5% of its current quality. Secondly, in order to maintain a minimum acceptable coastal water quality in the artificial lake, permitting its use in industrial supply and agriculture, currents of about 0.6 m/s are required for sea water exchange. This approach will assist in coastal management by supporting decision-making, policy planning, and the establishment of strategies for sustainable coastal development and conservation of coastal marine environments. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Coastal marine environments probabilistic graphical model probabilistic inference Coastal water quality Coastal management Sustainable coastal development

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Multiscalar genetic pathway modeling with hybrid Bayesian networks

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WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS 2020年第1期12卷 e1479-e1479页

作者： Ziebarth, Jesse D. Cui, Yan Univ Tennessee Hlth Sci Ctr Ctr Integrat & Translat Genom Dept Genet Genom & Informat Memphis TN 38163 USA

Bayesian network modeling of real world datasets is often complicated by the fact that they are hybrid datasets that contain both discrete and continuous variables. For example, recent advances in high throughput biotechnologies have made it possible to generate large-scale data across multiple biological scales-from discrete variables such as DNA variations to continuous variables such as omics traits and disease phenotypes. Such large heterogeneous and multiscalar datasets present a great challenge for biological knowledge discovery. Here we discuss the Bayesian Network Webserver (http://***/BNW), a web-based platform for creating hybrid Bayesian network models, and its use in discovering causal relationships from heterogeneous and multiscalar system genetics datasets. This article is categorized under: Statistical and graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical models > Bayesian models Statistical models > graphical models Applications of Computational Statistics > Computational and Molecular Biology

关键词： Bayesian network genetic pathway probabilistic graphical model

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Video Copy Detection Based on Spatiotemporal Fusion model

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Tsinghua Science and Technology 2012年第1期17卷 51-59页

作者： Jianmin Li Yingyu Liang Bo Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology(TNList)Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China

Content-based video copy detection is an active research field due to the need for copyright pro- tection and business intellectual property protection. This paper gives a probabilistic spatiotemporal fusion approach for video copy detection. This approach directly estimates the location of the copy segment with a probabilistic graphical model. The spatial and temporal consistency of the video copy is embedded in the local probability function. An effective local descriptor and a two-level descriptor pairing method are used to build a video copy detection system to evaluate the approach. Tests show that it outperforms the popular voting algorithm and the probabilistic fusion framework based on the Hidden Markov model, improving F-score （F1） by 8%.

关键词： video copy detection probabilistic graphical model spatiotemporal fusion model

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Inference and Evolutionary Analysis of Genome-Scale Regulatory Networks in Large Phylogenies

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CELL SYSTEMS 2017年第5期4卷 543-+页

作者： Koch, Christopher Konieczka, Jay Delorey, Toni Lyons, Ana Socha, Amanda Davis, Kathleen Knaack, Sara A. Thompson, Dawn O'Shea, Erin K. Regev, Aviv Roy, Sushmita Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA MIT Dept Biol 77 Massachusetts Ave Cambridge MA 02142 USA Dartmouth Coll Biol Dept Hanover NH 03755 USA Univ Calif Berkeley Dept Mol & Cell Biol 229 Stanley Hall Berkeley CA 94720 USA Wisconsin Inst Discovery 330 North Orchard St Madison WI 53715 USA Harvard Univ Dept Chem & Chem Biol Cambridge MA 02138 USA Harvard Univ Northwest Lab Howard Hughes Med Inst Cambridge MA 02138 USA Harvard Univ Northwest Lab Ctr Syst Biol Fac Arts & Sci Cambridge MA 02138 USA Harvard Univ Northwest Lab Dept Mol & Cellular Biol Cambridge MA 02138 USA Howard Hughes Med Inst Chevy Chase MD 20815 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53792 USA

Changes in transcriptional regulatory networks can significantlycontribute to species evolution and adaptation. However, identification of genome-scale regulatory networks is an open challenge, especially in non-model organisms. Here, we introduce multi-species regulatory network learning (MRTLE), a computational approach that uses phylogenetic structure, sequence-specific motifs, and transcriptomic data, to infer the regulatory networks in different species. Using simulated data from known networks and transcriptomic data from six divergent yeasts, we demonstrate that MRTLE predicts networks with greater accuracy than existing methods because it incorporates phylogenetic information. We used MRTLE to infer the structure of the transcriptional networks that control the osmotic stress responses of divergent, non-model yeast species and then validated our predictions experimentally. Interrogating these networks reveals that gene duplication promotes network divergence across evolution. Taken together, our approach facilitates study of regulatory network evolutionary dynamics across multiple poorly studied species.

关键词： comparative functional genomics evolution of gene regulatory networks evolution of stress response network inference phylogeny probabilistic graphical model regulatory networks yeast

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Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2016年第7期11卷 1319-1328页

作者： Baur, Christoph Milletari, Fausto Belagiannis, Vasileios Navab, Nassir Fallavollita, Pascal Tech Univ Munich Comp Aided Med Procedures Munich Germany Johns Hopkins Univ Comp Aided Med Procedures Baltimore MD USA

Catheter guidance is a vital task for the success of electrophysiology interventions. It is usually provided through fluoroscopic images that are taken intra-operatively. The cardiologists, who are typically equipped with C-arm systems, scan the patient from multiple views rotating the fluoroscope around one of its axes. The resulting sequences allow the cardiologists to build a mental model of the 3D position of the catheters and interest points from the multiple views. We describe and compare different 3D catheter reconstruction strategies and ultimately propose a novel and robust method for the automatic reconstruction of 3D catheters in non-synchronized fluoroscopic sequences. This approach does not purely rely on triangulation but incorporates prior knowledge about the catheters. In conjunction with an automatic detection method, we demonstrate the performance of our method compared to ground truth annotations. In our experiments that include 20 biplane datasets, we achieve an average reprojection error of 0.43 mm and an average reconstruction error of 0.67 mm compared to gold standard annotation. In clinical practice, catheters suffer from complex motion due to the combined effect of heartbeat and respiratory motion. As a result, any 3D reconstruction algorithm via triangulation is imprecise. We have proposed a new method that is fully automatic and highly accurate to reconstruct catheters in three dimensions.

关键词： C-arm fluoroscopy Catheter 3D reconstruction Electrophysiology probabilistic graphical model Arrhythmias

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Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

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ADVANCES IN WATER RESOURCES 2017年第Nov.期109卷 29-40页

作者： Hu, Yao Quinn, Christopher J. Cai, Ximing Garfinkle, Noah W. Univ Illinois Ven Te Chow Hydrosyst Lab Dept Civil & Environm Engn Urbana IL USA Univ Michigan Dept Civil & Environm Engn 2350 Hayward St Ann Arbor MI 48109 USA Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm-corn price, underlying groundwater level, monthly mean temperature and precipitation-have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Agent-based modeling Bounded rationality Behavioral uncertainty probabilistic graphical model Directed information graph Boosted regression trees

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Topic modeling and improvement of image representation for large-scale image retrieval

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INFORMATION SCIENCES 2016年 366卷 99-120页

作者： Nguyen Anh Tu Dong-Luong Dinh Rasel, Mostofa Kamal Lee, Young-Koo Kyung Hee Univ Dept Comp Sci & Engn Yongin 446701 Gyeonggi Do South Korea

In this paper, we present a new visual search system for finding similar images in a large database. However, there are a number of challenges regarding the robustness of the image representations and the efficiency of the retrieval framework. To tackle these challenges, we first propose an encoding technique based on soft-assignment of local features to convert an entire image into a single vector, which is a compact and discriminative representation. This encoded vector is suitable for most types of efficient indexing methods to produce an initial result. To compensate for the lack of incorporating geometric and object-related information during the encoding scheme, we then propose a probabilistic topic model to formalize the spatial structure among the local features. Moreover, the topic model allows us to effectively extract the object and background regions from the image. This is performed by a Markov Chain Monte Carlo algorithm for approximate inference. Finally, benefiting from the extracted objects in each image, we present a re-ranking scheme to automatically refine the initial search results. Our proposed retrieval framework has two major advantages: i) an aggregation strategy through soft-assignment improves the discriminative power of the representation, which has a determinative effect on the retrieval precision;and ii) the probabilistic latent topic model enables us to not only gain insight into the spatial structure of the image, but also handle a large variation in the object appearance. The experimental results from four benchmark datasets show that our approach provides competitive accuracy, and runs about ten times faster. Our studies also verify that proposed approach works effectively on large-scale databases of millions of images. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Topic modeling probabilistic graphical model Image retrieval Image representation Image coding Bag-of-visual words

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Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第6期23卷 5068-5078页

作者： Chen, Jianyu Li, Shengbo Eben Tomizuka, Masayoshi Univ Calif Berkeley Berkeley CA 94720 USA Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100084 Peoples R China Shanghai Qi Zhi Inst Shanghai 200030 Peoples R China Tsinghua Univ Sch Vehicle & Mobil State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping. In this article, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios. A sequential latent environment model is introduced and learned jointly with the reinforcement learning process. With this latent model, a semantic birdeye mask can be generated, which is enforced to connect with certain intermediate properties in today's modularized framework for the purpose of explaining the behaviors of learned policy. The latent space also significantly reduces the sample complexity of reinforcement learning. Comparison tests in a realistic driving simulator show that the performance of our method in urban scenarios with crowded surrounding vehicles dominates many baselines including DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned model is able to provide a better explanation of how the car reasons about the driving environment.

关键词： Autonomous vehicles Reinforcement learning probabilistic logic Roads Task analysis Autonomous automobiles graphical models Autonomous driving deep reinforcement learning probabilistic graphical model interpretability

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Aggregating Crowdsourced Quantitative Claims: Additive and Multiplicative models

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2016年第7期28卷 1621-1634页

作者： Ouyang, Robin Wentao Kaplan, Lance M. Toniolo, Alice Srivastava, Mani Norman, Timothy J. Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China US Army Res Lab Networked Sensing & Fus Branch Adelphi MD 20783 USA Univ Aberdeen Dept Comp Sci Aberdeen Scotland Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

Truth discovery is an important technique for enabling reliable crowdsourcing applications. It aims to automatically discover the truths from possibly conflicting crowdsourced claims. Most existing truth discovery approaches focus on categorical applications, such as image classification. They use the accuracy, i.e., rate of exactly correct claims, to capture the reliability of participants. As a consequence, they are not effective for truth discovery in quantitative applications, such as percentage annotation and object counting, where similarity rather than exact matching between crowdsourced claims and latent truths should be considered. In this paper, we propose two unsupervised Quantitative Truth Finders (QTFs) for truth discovery in quantitative crowdsourcing applications. One QTF explores an additive model and the other explores a multiplicative model to capture different relationships between crowdsourced claims and latent truths in different classes of quantitative tasks. These QTFs naturally incorporate the similarity between variables. Moreover, they use the bias and the confidence instead of the accuracy to capture participants' abilities in quantity estimation. These QTFs are thus capable of accurately discovering quantitative truths in particular domains. Through extensive experiments, we demonstrate that these QTFs outperform other state-of-the-art approaches for truth discovery in quantitative crowdsourcing applications and they are also quite efficient.

关键词： Crowdsourcing truth discovery quantitative task probabilistic graphical model

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