Artificial Intelligence and Robotics Commons^™

A Review Of Intelligent Generation Of Combat Simulation Scenarios, Zhiming Dong, Zhongqi Hu, Zhaoyang Liu, Heyang Zhou

Journal of System Simulation

Abstract: In order to improve the efficiency of combat simulation, this paper provided a theoretical reference for the research on the intelligent generation of combat simulation scenarios. It systematically reviewed the intelligent generation methods of combat simulation scenarios based on large language models (LLMs). It began by introducing the basic content of combat simulation scenarios, analyzed the shortcomings of current mainstream scenario generation methods, and discussed how to leverage LLMs to address these issues. Next, it outlined the application paradigms and key supporting technologies for the intelligent generation of combat simulation scenarios based on LLMs. Finally, it pointed out the …

Digital Twinned Industrial Robot: Conceptual Framework, Key Technologies, And Case Study, Yongkui Liu, Kang Yang, Benben Tuo, Yaduo Pan, Xinyu Wang, Yihan Wang, Yongqian Gong, Lin Zhang, Lihui Wang, Tingyu Lin, Bin Zi, Yuan Li, Wei You, Xun Xu

Journal of System Simulation

Abstract: To effectively enhance the value and full life cycle management level of industrial robots, this paper integrated deeply digital twin with industrial robots and discussed a new concept, namely digital twinned industrial robot (DTIR). It defined the concept, composition, and typical characteristics of DTIRs and proposed their system architecture. From the perspective of the full life cycle of "design, manufacturing, operation and maintenance, and decommissioning", the key technologies of DTIRs were systematically sorted out. Furthermore, the validity of the proposed conceptual framework was verified through a case study. Finally, the paper summarized the findings and discussed the future development …

Large-Scale Social Simulator: Frontiers And Perspectives, Jinghua Piao, Chen Gao, Fang Zhang, Jun Su, Yong Li

Journal of System Simulation

Abstract: Social experiments, as a typical research method in social sciences, aim to study specific social phenomena or the impacts of policies by observing the behaviors of individuals, organizations, or social groups in real or simulated environments. However, traditional social experiment methods often face challenges such as random bias, high costs, and ethical risks, making them inadequate to address increasingly complex research demands. Against this backdrop, computational social experiments have emerged, enabling researchers to conduct social experiments within computational simulation environments that are free from random bias, cost-efficient, and ethically manageable. Meanwhile, China is currently undergoing a critical period of …

Combat-Oriented Comprehensive Simulation And Verification Technology For Equipment System Rms, Yue Zhang, Wenliang Zhang, Qiang Feng, Xing Guo, Yi Ren, Zili Wang

Journal of System Simulation

Abstract:Existing reliability maintainability supportability (RMS) simulation and verification methods for equipment systems are typically conducted under standard conditions and suffer from weak combat environments and task modeling capabilities. To address this limitation, a multi-agent RMS simulation and verification framework was proposed. Key breakthroughs included agent modeling techniques for complex environments and variable tasks, interaction mechanisms among environmental agents, task agents, equipment, and support systems, and a simulation-based comprehensive RMS evaluation method. Case studies demonstrate that the proposed method effectively models complex environments and variable tasks, supports combat-oriented simulation and verification and design scheme evaluation, and meets combat-ready development requirements.

Multi-Scenario Multi-Satellite Mission Planning Method Based On Adaptive Large Neighborhood Search, Xiutian Li, Ling Wang, Yingwu Chen, Lining Xing, Yingguo Chen

Journal of System Simulation

Abstract: To further improve the execution efficiency of remote sensing satellites, an integrated optimization framework combining adaptive large neighborhood search (ALNS) and a constraint programming-boolean satisfiability problem (CP-SAT) solver monitor was proposed, addressing the challenges of complex constraints, dynamic scale, and resource heterogeneity in multi-scenario multi-satellite mission planning. A unified multi-objective mixed-integer programming model was established, coupling heterogeneous constraints of point targets and area tasks. A time-domain rolling mechanism dynamically decomposed the problem scale, and a priority screening strategy enhanced the search efficiency of ALNS. Solution feasibility was verified in real time through the CP-SAT monitor. Results show that compared …

Ai Project Facilitation Guidance For Research Computing And Data (Rcd) Professionals, Anna Alber, Laura Briggs, Paul Brunk, Manasvita Joshi, Atish P. Kamble, Amira Kefi, Timothy Middelkoop, Semir Sarajlic, Ana Maria Sokovic, Jeffrey N. Valdez, Ying Zhang

Administration and Staff Articles and Research

The role of Artificial Intelligence (AI) in research and education continues to rapidly grow, resulting in increased collaboration between researchers in AI and Research Computing and Data (RCD) professionals to meet the research and teaching demands. RCD professionals bridge the gap between research and technology by guiding and collaborating with researchers and educators through the process of selecting the hardware, software, and services best suited for executing their AI projects. This includes ensuring compliance with funding and regulatory requirements across the entire lifecycle of the project. In this paper, we present an overview of the AI project lifecycle and how …

Stranger Disputes: When Artificial Intelligence Turns Arbitration Upside Down, Imre Stephen Szalai

Pepperdine Dispute Resolution Law Journal

Arbitration agreements are everywhere in the United States. These agreements already block access to courts in a troubling manner, and pursuant to these agreements, parties must resolve their disputes before a private, human arbitrator with broad, virtually unreviewable powers. However, with the growth of AI, companies could easily redraft their contracts to require arbitration before non-human bots or AI arbitrators instead of a human arbitrator. Based on the history, values, policy, and text of the Federal Arbitration Act (FAA), this Article concludes that the FAA would govern and support the use of an AI arbitrator. As a result, a pre-dispute …

Limitations Of Using Large Language Models For Automated Essay Scoring, Thomas A. Fink

Theses

Background: Automated essay scoring (AES) is a challenging deep learning problem. The two most widely used methods for predicting essay quality scores, supervised learning-based and LLM-based, have their own limitations. Although supervised learning-based methods are more accurate, they only predict a score and do not offer descriptive feedback to students. On the other hand, LLM-based methods can offer rubric-guided feedback but are known to be less accurate.

Methods: This work focuses on improving the accuracy of state-of-the-art LLM-based AES methods. We began by thoroughly investigating why these methods were performing poorly for certain datasets and certain examples. This led us …

Integrating Artificial Intelligence And Machine Learning Technologies Into Common Operating Picture And Course Of Action Development, C. Anthony Pfaff, Christopher John Hickey

Books, Monographs & Collaborative Studies

C. Anthony Pfaff and Christopher John Hickey, Principal Investigators

©2025 C. Anthony Pfaff. All rights reserved.

Integrating Artificial Intelligence and Machine Learning Technologies into Common Operating Picture and Course of Action Development explores the potential of artificial intelligence (AI) and machine learning to revolutionize military planning processes by enhancing situational awareness and expediting course of action development within the Joint planning process. The study delves into technical, organizational, and resource considerations that are critical for AI integration. In addition, the study highlights the importance of clean, structured data in training AI systems, addresses challenges in data collection across varying formats …

"Chatgpt Told Me To Say It": Ai Chatbots And Class Participation Apprehension In University Students, Daisuke Akiba

Publications and Research

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

Understanding The Roots Of Swarm Intelligence In Defence To Find The Path Forward: A Scientometric Study Of Autonomous Systems, Anton Klarin, Pi-Shen Seet, Janice Jones, Michael N. Johnstone, Helen Cripps, Jalleh Sharafizad, Tony Marceddo

Research outputs 2022 to 2026

Swarm intelligence, inspired by the decentralised, adaptive and self-synchronising behaviours of natural swarms, is a pivotal component of autonomous systems, enhancing efficiency, robustness and scalability. The research in this area is nascent and interdisciplinary. To drive this important research forward, it is necessary to adopt a systems perspective on what is available in the current literature. This chapter offers a comprehensive systems perspective of the integration of swarm intelligence within the broader domain of automation, emphasising its application in the defence sector. A systems perspective of an interdisciplinary field is afforded through scientometrics. Using VOSviewer algorithms, we analysed 1706 publications …

Cilia In The Brain Display Region-Dependent Oscillations Of Length And Orientation, Roudabeh Vakil Monfared, Sherif Abdelkarim, Pieter Derdeyn, Kiki Chen, Hanting Wu, Kenneth Leong, Tiffany Chang, Justine Lee, Sara Versales, Surya M. Nauli, Kevin Beier, Pierre Baldi, Amal Alachkar

Pharmacy Faculty Articles and Research

In this study, we conducted high-throughput spatiotemporal analysis of primary cilia length and orientation across 22 mouse brain regions. We developed automated image analysis algorithms, which enabled us to examine over 10 million individual cilia, generating the largest spatiotemporal atlas of cilia. We found that cilia length and orientation display substantial variations across different brain regions and exhibit fluctuations over a 24-h period, with region-specific peaks during light-dark phases. Our analysis revealed unique orientation patterns of cilia, suggesting that cilia orientation within the brain is not random but follows specific patterns. Using BioCycle, we identified rhythmic fluctuations in cilia length …

Modern Procedural Terrain Generation Techniques And Their Background, Hunter A. Barton

2025 Symposium

Procedural terrain generation has become a staple in many digital environments, enabling the automated creation of large-scale and realistic landscapes for applications such as video games and movies. This paper provides an in-depth look at smooth noise functions and their use for terrain generation, as well as an overview of some more modern methods of generation. A method utilizing machine learning stlye transfer was reproduced for this paper with some alterations to improve visualization and realism.

Can We Discover Physical Models Using Machine Learning? A Case Study Of Galaxy Sizes, Festa Buçinca-Çupallari, Ariyeh Maller, Viviana Acquaviva, Austen Gabrielpillai, Rachel S. Somerville

Publications and Research

We explore the ability of machine learning methods to discover underlying equations of physics by searching for the equations governing galaxy size in a semianalytic model. This case study allows us to evaluate the process as we know the ground truth. We find that we fail to find an equation to predict galaxy size on the entire data set, but are successful when we separate out disk galaxies where we expect the physics driving galaxy size to be different than in bulge-dominated systems. We are also able to find an equation for bulge size, but not without adding an additional …

In The Shadow Of Prompts: Adversarial Attacks And Model Cloning In Large Language Models, Kanchon Gharami

Doctoral Dissertations and Master's Theses

Large-language models (LLMs) already power mission critical tasks such as command-and-control chat, satellite ground-station automation, military analytics, and cyber-defense. Since most of these services are offered through application programming interfaces (APIs) that still expose full or top-k logits and lack mature safeguards, they present a serious, often overlooked attack surface. Earlier work has shown how to rebuild the output projection layer or distill surface behavior, but no attack has produced a deployable clone within a tight query budget. In this thesis, we address this problem by presenting a practical pipeline for cloning LLMs under constrained settings. The approach first estimates …

Benford's Law In Basic Rnn And Long Short-Term Memory And Their Associations, Farshad Ghassemi Toosi

Department of Computer Science Publications

Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term …

The Impact Of Artificial Intelligence As An Intervening Variable Between The Digital Government Strategy And Competency Development "An Applied Study At Sharjah Police Sciences Academy ", Elsayed Kamal Risha, Abd Al-Rahman Al-Naqbi

Journal of Police and Legal Sciences

The study aimed to determine the impact of the digital government strategy on competencies development, through artificial intelligence as an intervening variable, and to achieve the objectives, the study relied on the quantitative approach and the questionnaire was used as the main tool for collecting data. The study community represented officers, non-commissioned officers and individuals at the Sharjah Academy for Police Sciences, and the study sample amounted to 30 affiliates, i.e. the total number of employees in the Competency Development Department at the Academy. The study reached a set of results, the most prominent of which are:

- The existence …

Legislative And Security Confrontation Of Crimes Artificial Intelligence In The State Of Kuwait (An Analytical Study), Rashid Mohammed Al Marri

Journal of Police and Legal Sciences

The study aimed to demonstrate the mechanisms of legislative and security confrontation of artificial intelligence crimes. The use of technologies associated with artificial intelligence may go beyond the imposed limits, whether by exploiting it through its program developers & specialists to commit crimes in the cyber field, or it may be with the growing capabilities of artificial intelligence to make decisions in the field. Many behaviors occur automatically. Our research also aims to study the criminal responsibility for these crimes, & determine it in order to hold the real perpetrator accountable in accordance with the legal rules in force to …

Bridging Classical Rhetoric And Ai: A Systematic Framework For Developing Authorial Voice Through Large Language Models, Daniel Plate

Theses

This project addresses critical gaps in AI-assisted writing by developing the first systematic framework that integrates classical rhetorical principles with modern large language model capabilities for authorial voice development. The primary focus is on creating reliable methods for stylistic control through strategic AI collaboration rather than ad hoc prompting approaches. The project develops a comprehensive coding system for analyzing prose style, creates ten distinct authorial personas, and establishes a dual curation methodology that structures both stylistic analysis and content preparation. Implementation through the AI Writing Guide website provides practical tools including prompt templates, annotated examples, and instructional materials that demonstrate …