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LiP-LLM: Integrating Linear programming and Dependency Graph With Large Language Models for Multi-Robot Task Planning

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 1122-1129页

作者： Obata, Kazuma Aoki, Tatsuya Horii, Takato Taniguchi, Tadahiro Nagai, Takayuki Osaka Univ Grad Sch Engn Sci Dept Syst Innovat Osaka 5650871 Japan Kyoto Univ Dept Informat Kyoto 6068501 Japan

This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. For multi-robots to efficiently perform tasks, it is necessary to manage the precedence dependencies between tasks. Although multi-robot decentralized and centralized task planners using LLMs have been proposed, none of these studies focus on precedence dependencies from the perspective of task efficiency or leverage traditional optimization methods. It addresses key challenges in managing dependencies between skills and optimizing task allocation. LiP-LLM consists of three steps: skill list generation and dependency graph generation by LLMs, as well as task allocation using linear programming. The LLMs are utilized to generate a comprehensive list of skills and to construct a dependency graph that maps the relationships and sequential constraints among these skills. To ensure the feasibility and efficiency of skill execution, the skill list is generated by calculated likelihood, and linear programming is used to optimally allocate tasks to each robot. Experimental evaluations in simulated environments demonstrate that this method outperforms existing task planners, achieving higher success rates and efficiency in executing complex, multi-robot tasks. The results indicate the potential of combining LLMs with optimization techniques to enhance the capabilities of multi-robot systems in executing coordinated tasks accurately and efficiently. In an environment with two robots, a maximum success rate difference of 0.82 is observed in the language instruction group with a change in the object name.

关键词： Robots Planning Resource management Linear programming Robot kinematics Natural languages Multi-robot systems Optimization programming Navigation task planning

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To Abstract or Not to Abstract? A Comparative Study Evaluating the User Experience of Spreadsheet programming with Sheet-Defined Functional Abstractions

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2025年第9期41卷 5445-5461页

作者： Kudebayeva, Anel Harte, Christopher Bryan-Kinns, Nick Stockman, Tony Queen Mary Univ London Dept Comp Sci London England Queen Mary Univ London London England Univ Arts London Creat Comp London England

Spreadsheets are a widespread functional programming paradigm that offer liveness and directness of interaction. However, spreadsheets are notoriously error-prone and difficult to debug. To overcome this limitation and improve the expressive power of spreadsheets, we propose an extension to the spreadsheet paradigm in the form of sheet-defined lambdas - user-defined functions that abstract computations on the sheet. This concept was developed and deployed in our web-based spreadsheet application named Lattice. We evaluate this approach through a user study which compared the user experience of programming in a spreadsheet with and without lambdas, as well as the difference in performance between learner (N = 12) and experienced (N = 12) programmers. The study measured participant task performance (task time, success rate and number of errors) and the quality of their user experience of using Lattice (video recordings of use, interviews and questionnaire responses). Our findings indicate that programming with lambdas is not only more efficient than writing formulas in a conventional way, but also provides a rewarding hedonic experience. However, we found that learners perceived the concept of functional abstractions with lambdas as difficult to comprehend;while experienced programmers noted potential utilitarian advantages that aid in managing the complexity of a spreadsheet program. The results obtained in this work contribute to a better understanding of human-spreadsheet interaction and can inform the future design of user-friendly computational systems.

关键词： User experience usability programming spreadsheet

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Competency-Based Hybrid Learning: A Modern Approach to Teaching programming and Digital Technologies Subjects

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IEEE ACCESS 2025年 13卷 54892-54919页

作者： Kucera, Erik Haffner, Oto Slovak Univ Technol Bratislava Fac Elect Engn & Informat Technol Bratislava 84104 Slovakia

The COVID-19 pandemic prompted the global education sector to experiment with various forms of online learning as institutions rapidly transitioned to remote formats. This article presents a comprehensive overview of a competency-based hybrid learning methodology, developed and implemented during the pandemic. In addition to detailing the methodology itself, the article also shares the experiences of educators at the authors' institution, who observed significant improvements in educational outcomes, surpassing even pre-pandemic standards. The methodology highlights the limitations of directly replicating traditional in-person instruction in an online format using existing materials and approaches. Instead, it advocates for carefully designed adaptations tailored to the digital environment, leveraging asynchronous components, interactive tools, and newly created e-learning resources to optimize effectiveness. This approach also requires increased interaction between educators and students beyond scheduled classes, ensuring timely support and guidance. Although this methodology may not suit all course types, it has proven particularly effective in advanced information and communication technologies (ICT) and digital technology subjects, such as programming, artificial intelligence, digital or electronic marketing, video editing, 3D engine work, etc. The positive student feedback further underscores the potential of this model to enhance educational quality and outcomes in these domains.

关键词： Hybrid learning Education programming profession programming Pandemics Engineering education Electronic learning Training Focusing COVID-19 online learning blended learning digital technologies ICT modern teaching training

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Analysis of ChatGPT-Generated Codes Across Multiple programming Languages

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IEEE ACCESS 2025年 13卷 23580-23596页

作者： Almanasra, Sally Suwais, Khaled Arab Open Univ Fac Comp Studies Riyadh 11681 Saudi Arabia

Our research focuses on the intersection of artificial intelligence (AI) and software development, particularly the role of AI models in automating code generation. With advancements in large language models like ChatGPT, developers can now generate code from natural language prompts, a task that traditionally required significant manual input and expertise. AI-generated code promises to boost productivity by enabling faster prototyping and automating repetitive coding tasks. However, as these models are increasingly adopted in real-world applications, questions surrounding their efficiency and code quality become critical. This research investigates ChatGPT-4o, a state-of-the-art language model, and its ability to generate functional, high-quality code in different programming languages. By comparing performance between Python and Java, the study seeks to shed light on AI's capabilities and limitations in code generation, addressing not only functional correctness but also broader software engineering concerns such as memory usage, runtime efficiency, and maintainability. The study addresses key questions related to the performance, code quality, and error management of AI-generated code by analyzing solutions for 300 data structure problems and 300 problems from the LeetCode platform. The findings reveal notable performance differences between the two languages: Java demonstrated superior runtime performance, particularly for medium and hard problems, while Python exhibited better memory efficiency across all complexity levels. The research also highlighted significant gaps in code quality, with both languages showing deficiencies in documentation and exception management. This study contributes to the literature by offering a comprehensive cross-language analysis of ChatGPT-4o's programming capabilities, addressing a gap in the evaluation of AI-generated code performance.

关键词： Codes programming Encoding Chatbots Runtime Python Software development management Java Complexity theory Standards Artificial intelligence software development ChatGPT large language model programming

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3-D Vertical Resistive Switching Random Access Memory (3D-VRRAM) With Multilevel programming for High-Density, Energy-Efficient In-Memory Computing

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IEEE TRANSACTIONS ON ELECTRON DEVICES 2025年第5期72卷 2677-2684页

作者： Bridarolli, D. Zucchelli, C. Mannocci, P. Ricci, S. Farronato, M. Pedretti, G. Sun, Z. Ielmini, D. Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB I-20133 Milan Italy IU NET I-20133 Milan Italy Hewlett Packard Labs Artificial Intelligence Res Lab AIRL Milpitas CA 95035 USA Peking Univ Inst Artificial Intelligence Sch Integrated Circuits Beijing 100871 Peoples R China

Resistive random access memory (RRAM) devices offer a broad range of attractive properties for in-memory computing (IMC) applications, such as nonvolatile storage, low read current, and high scalability. IMC allows to overcome the memory bottleneck of data-intensive workloads, such as deep learning on the edge. In this context, 3-D vertical RRAM (3D-VRRAM) is a promising option to achieve high memory cell capacity with low fabrication cost. In this work, we present an HfOx-based 3D-VRRAM crossbar array (CBA) capable of IMC with precise multilevel programming. We show an extensive experimental demonstration of both matrix-vector multiplication (MVM) and inverse/pseudoinverse matrix calculation via IMC on 3D-VRRAM. To further support the parallel IMC application in real-life scenarios, the work also reports a demonstration of relatively large-size problems adopting 2D-RRAM and SRAM-based memory arrays. These results support 3D-VRRAM for high-density, energy-efficient IMC for edge computing applications.

关键词： Semiconductor device measurement Three-dimensional displays Energy efficiency Electrodes Silicon compounds programming In-memory computing Hafnium compounds Current measurement Scanning electron microscopy Edge computing embedded memory in-memory computing (IMC) monolithic 3-D integration resistive random access memory (RRAM) scaling

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A Second-Order Cone programming (SOCP) Based Co-Optimization Approach for Integrated Transmission-Distribution Optimal Power Flow

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 3563-3572页

作者： Chowdhury, Md Mahmud-Ul-Tarik Hasan, Md Shamim Kamalasadan, Sukumar Univ North Carolina Charlotte Energy Prod Infrastructure Ctr EP Dept Elect Engn Charlotte NC 28223 USA

The advancement of grid technology with high integration of distributed energy resources (DERs) has led to dynamic changes in the distribution network with a substantial impact on the transmission network. With this impact of DER integration, separate optimal power flow (OPF) analysis in transmission and distribution (TD) networks is not always adequate for the optimal operation of the power system. Hence, this paper proposes a coordinated TD-OPF co-optimization method with second-order cone programming (SOCP) to address this challenge. This co-optimization method provides a feasible solution for an integrated TD system, bridging the gap between transmission and distribution network challenges with renewable-based DERs through data exchange between the transmission and distribution networks. Moreover, each distribution network employs a distributed OPF (D-OPF) algorithm, which supports decentralized operation, thereby enhancing the reliability and scalability of the proposed OPF model for large TD networks. The goal is to reduce the overall system planning expenses, optimizing the microgrids in distribution networks and the major generation units within the transmission network. The efficacy of the proposed TD-OPF co-optimization approach has been verified through numerical analysis.

关键词： Distribution networks Load flow Computational modeling Reactive power Power systems Analytical models Mathematical models Distributed power generation Renewable energy sources programming Transmission-distribution Co-OPF distributed OPF angle & conic relaxation SOCP-OPF

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A Mix-Integer programming Based Deep Reinforcement Learning Framework for Optimal Dispatch of Energy Storage System in Distribution Networks

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2025年第2期13卷 597-608页

作者： Hou, Shengren Salazar, Edgar Mauricio Palensky, Peter Chen, Qixin Vergara, Pedro P. Delft Univ Technol Intelligent Elect Power Grids IEPG Grp NL-2628 CD Delft Netherlands Eindhoven Univ Technol Elect Energy Syst EES Grp Eindhoven Netherlands Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Operat & Control Beijing 100084 Peoples R China

The optimal dispatch of energy storage systems (ESSs) in distribution networks poses significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand, and the variability inherent in renewable energy sources. By exploiting the generalization capabilities of deep neural networks (DNNs), the deep reinforcement learning (DRL) algorithms can learn good-quality control models that adapt to the stochastic nature of distribution networks. Nevertheless, the practical deployment of DRL algorithms is often hampered by their limited capacity for satisfying operational constraints in real time, which is a crucial requirement for ensuring the reliability and feasibility of control actions during online operations. This paper introduces an innovative framework, named mixed-integer programming based deep reinforcement learning (MIP-DRL), to overcome these limitations. The proposed MIP-DRL framework can rigorously enforce operational constraints for the optimal dispatch of ESSs during the online execution. This framework involves training a Q-function with DNNs, which is subsequently represented in a mixed-integer programming (MIP) formulation. This unique combination allows for the seamless integration of operational constraints into the decision-making process. The effectiveness of the proposed MIP-DRL framework is validated through numerical simulations, demonstrating its superior capability to enforce all operational constraints and achieve high-quality dispatch decisions and showing its advantage over existing DRL algorithms.

关键词： Distribution networks Training Real-time systems programming Microgrids Voltage control Artificial neural networks Voltage regulation optimal dispatch distribution network mixed-integer programming deep reinforcement learning (DRL) energy management

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Students' Test Anxiety and Performance in Introductory programming: Do Exam and Assessment Modalities Play a Role?

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2025年第3期33卷

作者： Tomic, Bojan Stojanovic, Tatjana Antovic, Ilija Milic, Milos Univ Belgrade Fac Org Sci Belgrade Serbia

Many students encounter programming for the first time during undergraduate studies. programming, being different from conventional elementary and middle school subjects, maybe a source of anxiety and frustration for these students. It is already known that heightened anxiety can negatively impact test performance, that females are more test anxious than males, and that many introductory computer science courses suffer from high dropout rates. This is the second part of our two-fold study, which aims to answer whether exam and assessment modalities influence students' test anxiety and the (possible) correlation between test anxiety and performance in an introductory programming course. The research questions and most important findings are as follows: (i) female versus male students' test anxiety regarding a computer-based automatically assessed exam (female students exhibited higher levels of test anxiety than male students), (ii) test anxiety regarding a paper-based manually assessed exam versus test anxiety regarding a computer-based automatically assessed exam (test anxiety during a paper-based manually assessed programming exam was lower than the latter), (iii) general test anxiety versus test anxiety regarding a computer-based automatically assessed exam (only female students were more test anxious on a computer-based automatically assessed exam than in general), (iv) test anxiety regarding a paper-based manually assessed exam and exam score (no correlation found), and (v) test anxiety regarding a computer-based automatically assessed exam and exam score (statistically significant, moderately negative correlation found only for female students). Our findings suggest that efforts should be made toward addressing female students' test anxiety in introductory programming as their well-being and exam performance are affected the most.

关键词： automated assessment evaluation and assessment higher education programming test anxiety

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Effect of programming Schemes on Short-Term Instability in 1T1R Configuration

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IEEE ACCESS 2025年 13卷 44555-44564页

作者： Liu, Xiaohua Chen, Yang Kopperberg, Nils Solfronk, Oliver Hoffmann-Eifert, Susanne Menzel, Stephan Waser, Rainer Wiefels, Stefan Forschungszentrum Julich Peter Grunberg Inst 7 D-52425 Julich Germany Rhein Westfal TH Aachen Inst Werkstoffe Elektrotech 2 D-52074 Aachen Germany Rhein Westfal TH Aachen JARA FIT D-52074 Aachen Germany Forschungszentrum Julich Peter Grunberg Inst 10 D-52425 Julich Germany

Short-term instability is one of the key challenges for redox-based resistive random-access memory (ReRAM) reaching practical applications in the field of neuromorphic computing. Developing programming schemes that balance resistance fine-tuning with short-term stability is crucial for improving the reliability of ReRAM devices. In this work, we explore the impact of programming schemes on the short-term variability of valence change mechanism (VCM)-based filamentary ReRAM devices. We design three programming schemes: a baseline multi-pulse scheme, a single short-pulse scheme, and a single long-pulse scheme. By comparing the programming statistics of the one-transistor-one-resistor (1T1R) structures under different schemes and analyzing their evolution over time, we propose the hypothesis that the number and width of pulses influence the filament size and internal distribution of oxygen vacancies, and thus the short-term stability of ReRAM devices. We employ three-dimensional Kinetic Monte Carlo simulation, constructing filaments of varying sizes and adjusting the oxygen vacancy distribution to simulate the outcomes of different programming schemes. The simulation results align with experimental findings. Based on existing experimental data and modeling studies, we propose programming strategies that balance programmability and reliability for VCM-based filamentary ReRAM devices.

关键词： programming Transistors Voltage Electrodes Solid modeling Three-dimensional displays Kinetic theory Stability criteria Resistance Monte Carlo methods Redox-based resistive random-access memory (ReRAM) programming schemes short-term stability simulation kinetic Monte Carlo (KMC)

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A Linear programming Bound for Sum-Rank Metric Codes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第1期71卷 317-329页

作者： Abiad, Aida Gavrilyuk, Alexander L. Khramova, Antonina P. Ponomarenko, Ilia Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands Vrije Univ Brussel Dept Math & Data Sci B-1050 Ixelles Belgium Shimane Univ Interdisciplinary Fac Sci & Engn Matsue 6908504 Japan Univ Memphis Dept Math Sci Memphis TN 38152 USA Eindhoven Univ Technol Dept Math & Comp Sci NL-5612 AZ Eindhoven Netherlands Hainan Univ Sch Math & Stat Haikou 570228 Peoples R China

We derive a linear programming bound on the maximum cardinality of error-correcting codes in the sum-rank metric. Based on computational experiments on relatively small instances, we observe that the obtained bounds o... 详细信息

关键词： Measurement Codes Linear programming Extraterrestrial measurements Eigenvalues and eigenfunctions Vectors Upper bound Symmetric matrices programming Polynomials Sum-rank metric code linear programming bound sum-rank metric graph association scheme eigenvalues

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