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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Andreychuk, Anton Yakovlev, Konstantin Panov, Aleksandr Skrynnik, Alexey AIRI Moscow Russian Federation Federal Research Center “Computer Science and Control” The Russian Academy of Sciences Moscow Russian Federation Moscow Institute of Physics and Technology Dolgoprudny Russian Federation

ISBN: (纸本)157735897X

Multi-agent pathfinding (MAPF) is a problem that generally requires finding collision-free paths for multiple agents in a shared environment. Solving MAPF optimally, even under restrictive assumptions, is NP-hard, yet efficient solutions for this problem are critical for numerous applications, such as automated warehouses and transportation systems. Recently, learning-based approaches to MAPF have gained attention, particularly those leveraging deep reinforcement learning. Typically, such learning-based MAPF solvers are augmented with additional components like single-agent planning or communication. Orthogonally, in this work we rely solely on imitation learning that leverages a large dataset of expert MAPF solutions and transformer-based neural network to create a foundation model for MAPF called MAPF-GPT. The latter is capable of generating actions without additional heuristics or communication. MAPF-GPT demonstrates zero-shot learning abilities when solving the MAPF problems that are not present in the training dataset. We show that MAPF-GPT is able to outperform the state-of-the-art learnable MAPF solvers on a diverse range of problem instances and is computationally efficient during inference. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep reinforcement learning

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A deep learning-based unsupervised learning method for spatially resolved transcriptomic data analysis

A deep learning-based unsupervised learning method for spati...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Peng, Lihong He, Xianzhi Zhang, Li Peng, Xinhuai Lu, Yuankang Li, Zejun Chen, Xing Hunan University of Technology School of Computer Science Zhuzhou China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Artificial Intelligence Research Institute Hunan Institute of Technology Hengyang China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China

ISBN: (纸本)9781665468190

Spatially resolved transcriptomic data provide a large quantity of high-throughput gene expression and spatial structure information of tissues. Spatial clusters obtained by spatial transcriptome helps us to identify co-expressed regions and gene modules corresponding to cell types. In this study, we developed a Deep learning-based spatial clustering algorithm (RkDeep) by combining Ratio-cut and k-means. We first preprocessed spatial transcriptome data using graph neural network, and conducted dimensional reduction on the preprocessed data with denoising autoencoder. Finally, we clustered spatial transcriptome data by combining ratio cut and k-means. We compared our proposed RkDeep method with the other two spatial clustering methods, Seurat and Panoview. The results show that RkDeep computed the smallest Davide-Bouldin index and the largest Caliniski Harabaz index, adjusted rand index and normalized mutual information. Moreover, RkDeep was applied to analyze spatial transcriptome data of adult mouse brain, adult mouse kidney, and breast cancer. The results show that RkDeep can more accurately identify cell types from spatial transcriptome data. © 2022 IEEE.

关键词： Gene expression

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Advancing XAI: new properties to broaden semantic-based explanations of black-box learning models

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Procedia computer science 2024年 246卷 2292-2301页

作者： Pavel Kosov Nahla El Kadhi Cecilia Zanni-Merk Latafat Gardashova Azerbaijan State Oil and Industry University Computer Engineering Department Baku AZ1010 Azerbaijan French-Azerbaijani University Computer Science Research Department Baku AZ1000 Azerbaijan ASOIU Scientific Research Institute ”Geotechnological Problems of Oil Gas and Chemistry” Baku AZ1010 Azerbaijan Normandie Université INSA Rouen LITIS Rouen 76000 France

For a long time, experts in different areas where Artificial Intelligence (AI) is widely applied have been requesting more clarity for the decisions made by AI. DARPA came up with a new framework for eXplainable AI (XAI) where the system exploits an explainable model to provide explanations through the explanation interface to users based on the level of their expertise. Later, ontologies were integrated in various ways, which paved the way for clearer explanations. Provided enough Expert Knowledge, ontologies can be a potent tool in XAI. Based on the ideas of Bellucci et al., their explainable system provides comprehensive explanations based on ”visible” properties found in images by Machine Learning (ML) models and described via ontologies. However, we believe that any property, not only visible ones, can be used to explore the data. New ”explanatory” properties are proposed to be used for explanations. Our system exploits ML models and more user-oriented Expert Knowledge using a wider range of properties for objects to build a more profound XAI.

关键词： XAI Ontology Expert Knowledge Explainability

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A GI|G|1| System with Service Device Vacations

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Moscow University Computational Mathematics and Cybernetics 2023年第4期46卷 215-221页

作者： Naumenko, M. A. Ushakov, V. G. Faculty of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Institute of Informatics Problems Federal Research Center of Computer Science and Control Russian Academy of Sciences Moscow Russia

The authors study a single-channel queuing system with an infinite number of waiting places, a recurrent incoming flow, and a vacation of the service device when the system is released. Vacation times and durations have arbitrary absolutely continuous distributions. A non-stationary distribution of the number of requirements in the system is found.

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Inverse Problems of the Analysis of Input-Output Balances

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Mathematical Models and computer Simulations 2021年第6期13卷 943-954页

作者： Rassokha, A.V. Shananin, A.A. Moscow Institute of Physics and Technology (National Research University) Moscow Russian Federation Federal Research Center “Computer Science and Control” Russian Academy of Sciences Moscow Russian Federation

Abstract: A modification of the approach to the analysis of the input-output balance is proposed. Instead of the assumption of V.V. Leontief on the constancy of the rates of costs of production factors, a more realistic assumption in modern conditions is used about the constancy of the proportions of intersectoral cash flows. An algorithm for solving the inverse problem is proposed, which allows us to identify a nonlinear input-output balance model with a utility function and Cobb-Douglas production functions according to the data from a symmetric input-output table. Based on the use of the Young transform and Fenchel duality, a technology is developed for analyzing intersectoral relationships using this model. The technology is tested on data from Russian economic statistics. © 2021, Pleiades Publishing, Ltd.

关键词： Fenchel duality inverse problem Lagrange multipliers symmetric table of input-output balance Young transform

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems 10

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Santosh, Kathari Kholmukhamedov, Timur Kumar, M. Sandeep Aarif, Mohd Muda, Iskandar Bala, B Kiran Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Turin Polytechnic University In Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350353068

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4's robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot's enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support. © 2024 IEEE.

关键词： Electronic commerce

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Forecasting-Aided Graphical Learning for Robust State Estimation of Distribution System 1

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8th Purple Mountain Forum on Smart Grid Protection and control, PMF 2023

作者： Di, Cao Zhao, Junbo Hu, Jiaxiang Huang, Yuehui Huang, Qi Chen, Zhe Hu, Weihao University of Electronic Science and Technology of China Chengdu China Department of Electrical and Computer Engineering University of Connecticut StorrsCT06269 United States State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems China Electric Power Research Institute Beijing China Department of Energy Technology Aalborg University Pontoppidanstraede 111 Aalborg Denmark

ISBN: (数字)9789819992515

ISBN: (纸本)9789819992508

The forecasting of of pseudo-measurements play an important role in distribution system state estimation (DSSE). This paper proposes robust DSSE method based on forecasting-aided graphical learning method. The nodal power consumption models are first built to produce pseudo-measurements based on deep neural network. Then, the pseudo-measurements and real-time measurements are represented as a graph according to the topology of the distribution network, which are further processed by a graph attention network to capture the mapping relationship between the graphical measurements and the state variables based on the error modeling of pseudo-measurements. The robustness against anomalous measurements is achieved through the embedding of structural information of DN. The modeling of pseudo-measurements further enhance its robustness by guiding the formulation of edge weights of the graph neural network. Comparative tests are carried out on a IEEE 119-node system to demonstrate the effectiveness and robustness of the proposed method. © 2024, State Grid Electric Power.

关键词： Electric power distribution

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Online Knowledge Integration for 3D Semantic Mapping: A Survey

arXiv

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arXiv 2024年

作者： Igelbrink, Felix Renz, Marian Günther, Martin Powell, Piper Niecksch, Lennart Lima, Oscar Atzmueller, Martin Hertzberg, Joachim German Research Center for Artificial Intelligence Research Department Plan-based Robot Control Osnabrück Germany Osnabrück University Institute of Computer Science Knowledge-Based Systems Group Osnabrück Germany Osnabrück University Institute of Computer Science Semantic Information Systems Group Osnabrück Germany

Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advances in deep learning now allow full integration of prior knowledge, represented as knowledge graphs or language concepts, into sensor data processing and semantic mapping pipelines. Semantic scene graphs and language models enable modern semantic mapping approaches to incorporate graph-based prior knowledge or to leverage the rich information in human language both during and after the mapping process. This has sparked substantial advances in semantic mapping, leading to previously impossible novel applications. This survey reviews these recent developments comprehensively, with a focus on online integration of knowledge into semantic mapping. We specifically focus on methods using semantic scene graphs for integrating symbolic prior knowledge and language models for respective capture of implicit common-sense knowledge and natural language concepts. © 2024, CC BY-NC-ND.

关键词： Knowledge graph

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The Model of Managing a Group of UAVs by a Single Operator for Tasks Requiring Increased Attention

The Model of Managing a Group of UAVs by a Single Operator f...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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

The problem of optimizing the load on an operator of unmanned aerial vehicles (UAVs), which performs real-time tasks of researching and monitoring territories in an unstable environment is considered. Working load depends on the intensity of the information exchange between onboard and ground control stations and is determined by the agreed limit that leads to a limitation in the number of managed vehicles. As a mathematical model of the multi-functional control system, we use a closed-loop queuing system, which allows to establish coordination among its major elements (onboard computing complex, ground control station and operator) and reduce the loss of service requests. The latter is one of the main requirements to functioning of the system during emergencies. Of considerable interest are optimization statements for the problems of distribution of functions between elements and choosing the load modes. We gave formulations of some optimization problems in a general form and made appropriate conclusions. It is assumed that as an applied task, the problem of monitoring fire sites in forest areas can be considered, which requires increased attention from the operator to process information coming to the ground control station.

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State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

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arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

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