The Department of Defense, specifically the U.S. Army's Training and Doctrine Command (TRADOC), is seeking innovative solutions for the automated scenario generation for the Information Operations Network (ION) environment through the Small Business Innovation Research (SBIR) program. The primary objective is to develop a fully functional command and control (C2) simulation system that automates the scenario-building process, significantly reducing the time and effort required by at least 90%, while enhancing the realism and relevance of training scenarios for military personnel. This initiative is crucial for improving the scalability and adaptability of military exercises, as the current manual methods are labor-intensive and time-consuming. Interested parties must submit their proposals by June 25, 2025, with the opportunity being open for applications starting May 7, 2025. For more details, please refer to the solicitation at [SBIR.gov](https://www.sbir.gov/topics/12056).

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: 1. Automate US Army TRADOC's ION software to create a fully functional C2 simulation system to generate complex scenarios at a significantly faster rate than current manual methods. 2. Integrate the system with existing ION infrastructure, ensuring seamless deployment and minimal disruption to ongoing exercises. 3. Conduct user testing to validate the system's performance, usability, and effectiveness in supporting ION exercises. Desired Outcomes: 1. Reduce the time and effort required for scenario building by at least 90% through automation. 2. Increase the number of unique scenarios that can be generated for ION exercises. 3. Improve the realism and relevance of generated scenarios, resulting in more effective training for military personnel. 4. Advance research in the areas of social media, data analytics, and human factors. Requirements: 1. The proposed solution must be a mature, innovative technology that has been demonstrated in a relevant environment (e.g. Information Warfare Training Environment - IWTRE). 2. The solution must be capable of generating complex, realistic ION scenarios that meet the Army's training objectives. 3. The solution must be compatible with existing ION infrastructure and must not require significant modifications to the existing system. Key Performance Parameters (KPPs): 1. Scenario generation time: < 1 hour 2. Scenario quality: ≥ 95% similarity to manually created scenarios 3. User satisfaction: ≥ 90% rating on usability and effectiveness DESCRIPTION: The US Army's Training and Doctrine Command (TRADOC) has developed the Information Operations Network (ION) environment to simulate social media and digital domains for military exercises. ION provides an immersive environment for training, but scenario building remains a time-consuming and labor-intensive process. The current approach relies on manual content creation, which limits the scalability and adaptability of ION exercises. This D2P2 topic seeks a mature, innovative solution for the Joint force to automate the scenario building process for ION, enabling rapid generation of realistic and relevant scenarios that can be easily adapted to various Joint force training objectives and exercises. The proposed solution should leverage artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) techniques to analyze existing ION content, identify patterns, and generate new scenarios that mimic real-world social media, multi-modal sensors, and digital domains. All changes and additions to the production ION system must fit within the existing cARMY Azure Cloud architecture, ION’s security boundary, ION’s Risk Management Framework, must be built for mobile responsiveness, deployable on cARMY IL4, TRADOC G2 SIPRnet, and deployable on a Standalone Laptop (disconnected environment). PHASE I: Demand for this automation comes from operator input from previously conducted exercises. Operators complain that while ION is an excellent platform for simulating an information environment, significant time is needed to prepare the environment before the exercise. Also, ION currently lacks functionality to evaluate tactics, techniques, and procedures used during exercises. A successful D2P2 for ION automation will decrease the barriers for usage and greatly increase the adoption of ION for exercise purposes. ION is widely accepted by the Joint force as a useful capability, but the lack of automation hinders wider adoption. The qualifications for a small business to complete this D2P2 would be coding ability and an understanding of non-kinetic operations in the information environment enough to see the shortfalls of ION. Adding automation and evaluation functionality would not require heavy S&T but more end-user focus and data science skillsets to come up with appropriate metrics for evaluation of analytics used during ION events. Requirements Analysis and System Design: Identify the specific requirements and objectives of the automated scenario generation system for ION, based on the information provided in the proposal. Analyze existing ION content and identify patterns and characteristics that can be leveraged to generate new scenarios. Design a prototype system architecture that integrates AI, ML, and NLP techniques to automate scenario generation. Develop a detailed system design and specifications, including hardware, software, and network components. Define key performance parameters (KPPs) and evaluation criteria for the prototype system. *Deliverables: A comprehensive set of requirements and objectives for the automated scenario generation system for ION based on the information provided in the proposal. A report detailing the analysis of existing ION content, including identified patterns and characteristics that can be leveraged to generate new scenarios. A detailed system design and specifications document outlining the hardware, software, and network components of the prototype system. A set of key performance parameters (KPPs) and evaluation criteria for the prototype system. PHASE II: Prototype Development and Integration: Develop a proof-of-concept prototype of the automated scenario generation system for ION using the design and specifications developed in Phase 1. Test the prototype system in a simulated environment to validate its functionality, performance, and compatibility with existing ION infrastructure. Integrate the prototype system with existing ION software and infrastructure to ensure seamless deployment and minimal disruption to ongoing exercises. Perform user testing to validate the system's performance, usability, and effectiveness in supporting ION exercises, using the evaluation criteria defined in Phase 1. *Deliverables: A proof-of-concept prototype of the automated scenario generation system for ION, based on the design and specifications developed in Phase 1. A report detailing the results of testing the prototype system in a simulated environment, including performance, compatibility with existing ION infrastructure, and usability for military personnel. An integration plan outlining how the prototype system will be integrated with existing ION software and infrastructure without disrupting ongoing exercises. User testing results and a report evaluating the system's performance, usability, and effectiveness in supporting ION exercises, using the evaluation criteria defined in Phase 1. PHASE III DUAL USE APPLICATIONS: Validation and Refinement: Conduct additional validation testing to confirm that the prototype system meets the desired outcomes and KPPs outlined in the proposal. Analyze the results of the validation testing and make necessary refinements to the system design and specifications. Conduct a final review of the prototype system, including documentation of its current state and potential future developments. Prepare a final report, summarizing the results of the prototype development and validation, and providing recommendations for future research and development efforts in the area of automated scenario generation for ION. *Deliverables: A validation report confirming that the prototype system meets the desired outcomes and KPPs outlined in the proposal. A set of recommendations for refining the system design and specifications based on the results of the validation testing. A final review of the prototype system, including documentation of its current state and potential future developments A final report summarizing the results of the prototype development, validation, and refinement process, and providing recommendations for future research and development efforts in the area of automated scenario generation for ION. Dual Use Description: The proposed solution has potential dual-use applications, as it could be used not only for military training purposes, but also for civilian applications such as crisis management, emergency response, and cybersecurity training. For example, the solution could be used to simulate social media and digital domains during natural disasters, terrorist attacks, or other crises, allowing responders to practice their communication and coordination skills in a realistic environment. Cybersecurity training: The system could be adapted to generate realistic cyber-attack scenarios for training cybersecurity professionals. By automating the scenario generation process, it would be possible to quickly create a large number of diverse scenarios, improving the scalability and adaptability of cybersecurity training programs. Emergency management: The system could be used to generate realistic disaster scenarios for emergency management training exercises. By automating the scenario generation process, it would be possible to create a variety of disaster scenarios quickly and easily, improving the effectiveness and efficiency of disaster preparedness training programs. Social media analysis: The AI, ML, and NLP techniques used in the prototype system could be adapted for social media analysis, generating insights into online conversations and trends. This technology could be used by businesses to identify consumer preferences, track brand sentiment, and monitor for crisis situations. Virtual reality: The automated scenario generation system could be applied to the development of virtual reality simulations. By using AI and ML techniques to analyze existing content and develop new scenarios, it could be possible to create more realistic and engaging virtual environments for a range of industries, including gaming, education, and healthcare. Law enforcement: The system could be used to generate realistic scenarios for training law enforcement personnel in various situations, such as hostage negotiations, active shooter response, and crowd control. By automating the scenario generation process, it would be possible to create a variety of scenarios quickly and easily, improving the effectiveness and efficiency of law enforcement training programs. Intelligence analysis: The AI, ML, and NLP techniques used in the prototype system could be adapted for intelligence analysis, generating insights into complex data sets and identifying patterns and trends. This technology could be used by intelligence agencies to improve their understanding of global events and threats, and to develop more effective strategies for countering them. Gaming: The automated scenario generation system could be applied to the development of video games, creating more realistic and engaging gaming environments. By using AI and ML techniques to analyze existing content and develop new scenarios, it would be possible to create dynamic and unpredictable gaming experiences that adapt to player behavior. REFERENCES: 1. “We Wanted To Implement Data-Driven Operations During An Army Exercise—Here’s What We Learned” by Michael Schwille, Scott Fisher and Eli Albright, published in West Point Modern War Institute (2024). 2. “Tactical TikTok for Great Power Competition” by COL Theodore W. Kleisner (USA) and Trevor T. Garmey, published in Miltary Review (2022). KEYWORDS: "Automated Scenario Generation for Military Training" "Scenario Generation for Simulation-Based Training" "Intelligent Scenario Generation for Military Training"

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