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25th International Conference on Intelligent Data Engineering and Automated Learning

作者： Prieto, Abraham Romero, Alejandro Bellas, Francisco Univ A Coruna Integrated Grp Engn Res La Coruna Spain

ISBN: (纸本)9783031777370;9783031777387

This paper presents an educational activity developed within the AIM@VET project, aimed at integrating Large language Models (LLMs) into Vocational Education and Training (VET) for programming robots using natural language. The curriculum covers key AI topics such as Human-Robot Interaction (HRI), natural language processing, and the use of advanced models like ChatGPT. Students engage in activities from basic command interpretation to advanced voice-controlled interactions, gaining practical experience with LLMs in robotics. Evaluations showed significant improvements in understanding and engagement, highlighting the effectiveness of LLMs in enhancing robotics education for VET students.

关键词： Large language Models Vocational Education Robotics Human-Robot Interaction natural language programming

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natural language programming of Complex Robotic BDI Agents

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2013年第2期71卷 211-230页

作者： Lincoln, Nicholas K. Veres, Sandor M. Univ Southampton Southampton Hants England

This paper presents a natural language design environment that enables the programming of complex robotic agent systems, comprising of a top level BDI architecture in conjunction with a low level operational system that relates to the hardware interface and supplemental computational processes. The design environment enforces synergy between the development of these traditionally disparate aspects through sharing of ontological information and implementing a form of natural language programming called sEnglish. The resultant system provides an inherent abstraction of defined operational concepts and procedures for agent reasoning and shared meaning between man and machine. Through this shared knowledge the robot's operational logic and skill execution details are clear to human operators and may thus facilitate the work of design teams to enable rapid prototyping of physical agent systems in simulation or hardware.

关键词： Autonomous systems natural language programming Rational agent systems

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Breaking Down High-Level Robot Path-Finding Abstractions in natural language programming 1

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19th International Conference of the Italian-Association-for-Artificial-Intelligence (AIxIA)

作者： Zhan, Yue Hsiao, Michael S. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24060 USA

ISBN: (数字)9783030770914

ISBN: (纸本)9783030770914;9783030770907

natural language programming (NLPr) allows people to program in natural language (NL) for specific domains. It poses great potential since it gives non-experts the ability to develop projects without exhaustive training. However, complex descriptions can sometimes have multiple interpretations, making program synthesis difficult. Thus, if the high-level abstractions can be broken down into a sequence of precise low-level steps, existing natural language processing (NLP) and NLPr techniques could be adaptable to handle the tasks. In this paper, we present an algorithm for converting high-level task descriptions into low-level specifications by parsing the sentences into sentence frames and using generated low-level NL instructions to generate executable programs for pathfinding tasks in a LEGO Mindstorms EV3 robot. Our analysis shows that breaking down the high-level pathfinding abstractions into a sequence of low-level NL instructions is effective for the majority of collected sentences, and the generated NL texts are detailed, readable, and can easily be processed by the existing NLPr system.

关键词： natural language processing natural language programming Program synthesis LEGO Mindstorms EV3

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IDE Augmented with Human-Learning Inspired natural language programming 22

IDE Augmented with Human-Learning Inspired Natural Language ...

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44th ACM/IEEE International Conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER)

作者： Young, Mitchell Nan, Zifan Shen, Xipeng North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781665495981

natural language (NL) programming, the concept of synthesizing code from natural language inputs, has garnered growing interest among the software community in recent years. Unfortunately, current solutions in the space all suffer from the same problem, they require many labeled training examples due to their data-driven nature. To address this issue, this paper proposes an NLU-driven approach that forgoes the need for large numbers of labeled training examples. Inspired by how humans learn programming, this solution centers around natural language Understanding and draws on a novel graph-based mapping algorithm. The resulting NL programming framework, HISyn, uses no training examples, but gives synthesis accuracies comparable to data-driven methods trained on hundreds of samples. HISyn meanwhile demonstrates advantages in terms of interpretability, error diagnosis support, and cross-domain extensibility. To encourage adoption of HISyn among developers, the tool is made available as an extension for the Visual Studio Code IDE, thereby allowing users to easily submit inputs to HISyn and insert the generated code expressions into their active programs. A demo of the HISyn Extension can be found at https://***/KKOqJS24FNo.

关键词： Program synthesis natural language programming code editor

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Enabling Near Real-Time NLU-Driven natural language programming through Dynamic Grammar Graph-Based Translation 22

Enabling Near Real-Time NLU-Driven Natural Language Programm...

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20th IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

作者： Nan, Zifan Shen, Xipeng Guan, Hui North CarolinaState Univ Dept Comp Sci Raleigh NC 27695 USA Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA

ISBN: (纸本)9781665405843

Recently, natural language (NL)-based program synthesis has drawn increasing interest. Conventional methods that depend on some predefined domain-specific rules suffer from the lack of robustness and generality. Recent efforts on adopting deep learning to map queries to code requires a large number of labeled examples, making them not applicable on domains with scarce labeled examples. Although a third alternative, natural language understanding (NLU)-driven approach addresses the problems, the long response time hinders its adoption in practice, especially in an interactive scenario. This paper presents a solution to enable near real-time NLU-driven NL programming. The solution features a new algorithm, dynamic grammar graph-based translation (DGGT), for identifying the best grammar tree for a query via dynamic programming. It also introduces two new optimizations, grammar-based pruning and orphan node relocation, to further reduce the search space and address the special complexities from queries. Evaluations on two domains, text editing and program source code analysis, show that the DGGT algorithm and the optimizations shortens the response time of a state-of-the-art NLU-driven synthesizer by up to 1887x (25-133 x on average) while improving the accuracy by 2-12%.

关键词： natural language programming program synthesis dynamic programming

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CoPrompt: Supporting Prompt Sharing and Referring in Collaborative natural language programming 24

CoPrompt: Supporting Prompt Sharing and Referring in Collabo...

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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Feng, Li Yen, Ryan You, Yuzhe Fan, Mingming Zhao, Jian Lu, Zhicong Hong Kong Univ Sci & Technol Computat Media & Arts Thrust Guangzhou Peoples R China Univ Waterloo Sch Comp Sci Waterloo ON Canada City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798400703300

natural language (NL) programming has become more approachable due to the powerful code-generation capability of large language models (LLMs). This shift to using NL to program enhances collaborative programming by reducing communication barriers and context-switching among programmers from varying backgrounds. However, programmers may face challenges during prompt engineering in a collaborative setting as they need to actively keep aware of their collaborators' progress and intents. In this paper, we aim to investigate ways to assist programmers' prompt engineering in a collaborative context. We first conducted a formative study to understand the workflows and challenges of programmers when using NL for collaborative programming. Based on our findings, we implemented a prototype, CoPrompt, to support collaborative prompt engineering by providing referring, requesting, sharing, and linking mechanisms. Our user study indicates that CoPrompt assists programmers in comprehending collaborators' prompts and building on their collaborators' work, reducing repetitive updates and communication costs.

关键词： large language model collaborative programming prompt engineering natural language programming natural language interface

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Formal Validation for natural language programming using Hierarchical Finite State Automata 13

Formal Validation for Natural Language Programming using Hie...

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13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Zhan, Yue Hsiao, Michael S. Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA 24060 USA

ISBN: (纸本)9789897584848

natural language programming (NLPr) is a sub-field of natural language processing (NLP) that provides a bridge between natural languages (NL) and programming languages (PL), allowing users to design programs in the form of structured NL documents. Due to the imprecise and ambiguous nature of NL, it is essential to ensure the correctness of translation for critical applications where errors are unacceptable. Machine learning-based approaches for error checking are insufficient as it can be difficult for even the most sophisticated models to capture all the relevant intricacies of a natural language. Automata offer a formalism that has been used in compiling programming languages, and this paper extends automata-based methods to validating programs written in natural languages. In particular, we propose a hierarchically structured finite-state automaton, modeled based on domain-specific knowledge, for NLPr input validation and semantic error reporting. Experimental results from validating a set of collected NL sentences show that the proposed validation and error reporting can catch the unexpected input components while validating the semantics.

关键词： natural language programming natural language Processing Formal Validation Hierarchical Finite State Automata

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HISyn: Human Learning-Inspired natural language programming 2020

HISyn: Human Learning-Inspired Natural Language Programming

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28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Nan, Zifan Guan, Hui Shen, Xipeng North Carolina State Univ Raleigh NC 27695 USA Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9781450370431

natural language (NL) programming automatically synthesizes code based on inputs expressed in natural language. It has recently received lots of growing interest. Recent solutions however all require many labeled training examples for their data-driven nature. This paper proposes an NLU-driven approach, a new approach inspired by how humans learn programming. It centers around natural language Understanding and draws on a novel graph-based mapping algorithm, foregoing the need of large numbers of labeled examples. The resulting NL programming framework, HISyn, using no training examples, gives synthesis accuracy comparable to those by data-driven methods trained on hundreds of training numbers. HISyn meanwhile demonstrates advantages in interpretability, error diagnosis support, and cross-domain extensibility.

关键词： Program synthesis natural language programming

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A natural language programming solution for executable papers

A natural language programming solution for executable paper...

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International Conference on Computational Science (ICCS) on the Ascent of Computational Excellence

作者： Veres, Sandor M. Adolfsson, J. Patrik Univ Southampton Fac Engn & Environm Southamton SO17 1BJ England

The paper describes a system for executable papers for publishers enabling them to reuse content and to generate further advances of science and engineering. The executable algorithmic descriptions within a paper are presented in natural language sentences and basic code, thereby making long term compatibility absolute. Authors are required to use publicly numerical libraries on the Internet or references to publications with executable papers. As used by authors the system automatically creates a web of algorithmic knowledge on the Internet. Novelty of new algorithms in publications can be evaluated by automated tools available to authors, reviewers and readers of scientific papers published.

关键词： interactive publications data driven computing natural language programming

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A natural language programming solution for executable papers

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Procedia Computer Science 2011年 4卷 678-687页

作者： Sandor M Veres J. Patrik Adolfsson Faculty of Engineering and the Environment University of Southampton Highfield Southamton SO17 1BJ UK

关键词： Interactive publications Data driven computing natural language programming

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