In this paper, we investigate the constraint typology of mixed-integer linear programming MILP formulations. MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, timetabling optimization problems, providing optimized business solutions for industry sectors such as: manufacturing, agriculture, defence, healthcare, medicine, energy, finance, and transportation. Despite the numerous real-life Combinatorial Optimization Problems found and solved, and millions yet to be discovered and formulated, the number of types of constraints, the building blocks of a MILP, is relatively much smaller. In the search of a suitable machine readable knowledge representation for MILPs, we propose an optimization modelling tree built based upon an MILP ontology that can be used as a guidance for automated systems to elicit an MILP model from end-users on their combinatorial business optimization problems.
Agentic AI Software Engineers: Programming with Trust
National University of Singapore（新加坡国立大学）；Carnegie Mellon University, KBR Inc., NASA Ames（卡内基梅隆大学、KBR公司、NASA阿姆斯特朗实验室）；CISPA Helmholtz Center for Information Security, University of Stuttgart（CISPA海姆霍兹信息安全中心、斯图加特大学）；Columbia University（哥伦比亚大学）
Engineering Systems for Data Analysis Using Interactive Structured Inductive Programming
Dept. of Computer Science \& Information Systems, BITS Pilani, Goa, India School of Computer Science \& Engineering, University of New South Wales, Australia
Prompt Programming for Cultural Bias and Alignment of Large Language Models
Information Systems and Modeling, Los Alamos National Laboratory（信息系统与建模，洛斯阿拉莫斯国家实验室）；Advanced Research in Cyber Systems, Los Alamos National Laboratory（网络安全系统高级研究，洛斯阿拉莫斯国家实验室）；Center for National Security and International Studies（国家安全与国际研究中心）；Analytics Division, Los Alamos National Laboratory（分析部门，洛斯阿拉莫斯国家实验室）
Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language
University of Auckland（奥克兰大学）；Indian Institute of Science（印度科学学院）
GitHub Copilot是一个人工智能模型，用于从自然语言问题描述自动生成源代码。自2022年6月以来，Copilot已正式免费提供给所有学生作为Visual Studio Code等开发环境的插件。之前探索OpenAI Codex（Copilot的底层模型）的工作表明，它在典型的CS1问题上表现良好，因此人们担心它会对入门编程课程的教学产生影响。然而，关于Copilot不能很好地执行的问题类型，或者关于学生在解决错误时可能与Copilot进行的自然语言交互，我们知之甚少。我们通过评估Copilot在166个编程问题的公开数据集上的性能来探索这些问题。我们发现，它成功地解决了大约一半的这些问题的第一次尝试，它解决了60%的剩余问题，只使用自然语言的问题描述的变化。我们认为，这种类型的提示工程，我们相信将成为一个标准的人类和Copilot之间的互动时，它最初失败，是一个潜在的有用的学习活动，促进计算思维技能，并有可能改变代码编写技能的发展的性质。
