23-06 Amend 3 GFS for TA1.docx

Amendment No. 3 to BAA FA8750-23-S-7006

The purpose of this amendment is to modify:

Part II:

a. Section I: adds the following statement to TA1:

The Government may provide Government Furnished Software (GFS) for this technical area and sub areas of the AI toolbox for related efforts.

No other changes are made.

NAICS CODE: 541715

FEDERAL AGENCY NAME: Department of the Air Force, Air Force Materiel Command, AFRL - Rome Research Site, AFRL/Information Directorate, 26 Electronic Parkway, Rome, NY, 13441-4514

BAA ANNOUNCEMENT TYPE: Modification

BROAD AGENCY ANNOUNCEMENT (BAA) TITLE: Artificial Intelligence and Next Generation Distributed Command and Control

BAA NUMBER: FA8750-23-S-7006

PART I – OVERVIEW INFORMATION

This announcement is for an Open, 2 Step BAA which is open and effective until 30 AUG 2028. Only white papers will be accepted as initial submissions; formal proposals will be accepted by invitation only. While white papers will be considered if received prior to 1600 hours Eastern Standard Time (EST) on 30 AUG 2028, the following submission dates are suggested to best align with projected funding:

FY24 by 13 SEP 2023 FY25 by 15 Mar 2024 FY26 by 15 Mar 2025 FY27 by 15 Mar 2026 FY28 by 15 Mar 2027

Offerors should monitor the Contract Opportunities on the SAM website at https://SAM.gov in the event this announcement is amended.

CONCISE SUMMARY OF TECHNOLOGY REQUIREMENT:

Artificial Intelligence (AI) holds great potential in transforming the Department of the Air Force (DAF) and Joint Force Command and Control (C2) capabilities across strategic, operational, and tactical levels by enabling decision makers to effectively assess the battlespace, rapidly explore, create, and select the best plan, and direct and monitor forces at pace and scale in a distributed setting. This BAA is interested in exploring new and advancing existing AI and distributed C2 concepts.

By accelerating the research & development of novel AI-based and distributed capabilities to support Joint All Domain C2, the DAF can achieve a strategic decision advantage, where AI becomes a key and prevalent component to future C2 systems. With the rise of AI-based applications also comes new challenges to include the ability to effectively manage, monitor, and adapt deployed AI capabilities and effective C2 in a distributed and contested environment.

Distributed operations is a critical component of Next-Generation C2. Key to the DAF transitioning from a single monolithic C2 node to multiple distributed C2 nodes is the ability orchestrate operational processes, while optimizing for limited resources. Through the development of distributed C2 capabilities the DAF can achieve an agile, fully mobile, distributed, and virtualized C2 operational capability. This BAA is interested in key areas for advancing C2 capability for the DAF to include but not limited to development and application of AI to C2, new concepts and techniques for the battle management and orchestration of AI at pace and scale, how the use of AI by adversaries can be considered in the C2 planning and execution process and distributed and collaborative C2 to enable C2 anywhere and anyplace.

BAA ESTIMATED FUNDING: Total funding for this BAA is approximately $99M. Individual awards will not normally exceed 48 months with dollar amounts normally ranging from $200K to $20M. There is also the potential to make awards up to any dollar value as long as the value does not exceed the available BAA ceiling amount.

ANTICIPATED INDIVIDUAL AWARDS: Multiple Awards are anticipated. However, the Air Force reserves the right to award zero, one, or more Procurement Contracts, Other Transactions, or Assistance Instruments, for all, some, or none of the solicited effort based on the offeror’s ability to perform desired work and funding fluctuations. There is no limit on the number of OTs that may be awarded to an individual offeror.

TYPE OF INSTRUMENTS THAT MAY BE AWARDED: FAR based Procurement contracts, CFR based grants and cooperative agreements or other transactions (OT) under 10 USC 4021, 10 USC 4022 ( previously 10 USC 4002, 2371, 10 USC 4003, 2371b) depending upon the nature of the work proposed. 10 USC 4023 also allows for FAR based contracts, OTs for research, OTs for Prototype, and assistance instruments.

In the event that an Other Transaction for Prototype agreement is awarded as a result of this competitive BAA, and the prototype project is successfully completed, there is the potential for a prototype project to transition to award of a follow-on production contract or transaction. The Other Transaction for Prototype Agreement itself will also contain a similar notice of a potential follow-on production contract or agreement.

AGENCY CONTACT INFORMATION: All white paper submissions and any questions of a technical nature shall be directed to the cognizant Technical Point of Contact (TPOC) as specified below (unless otherwise specified in the technical area):

BAA PROGRAM MANAGER:

Gennady Staskevich

AFRL/RISC

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-4889 Email: gennady.staskevich@us.af.mil

Questions of a contractual/business nature shall be directed to the cognizant contracting officer, as specified below (email requests are preferred):

Amber Buckley Telephone (315) 330-3605 Email: Amber.Buckley@us.af.mil

Emails must reference the solicitation (BAA) number and title of the acquisition.

Pre-Proposal Communication between Prospective Offerors and Government Representatives: Dialogue between prospective offerors and Government representatives is encouraged. Technical and contracting questions can be resolved in writing or through open discussions. Discussions with any of the points of contact shall not constitute a commitment by the Government to subsequently fund or award any proposed effort. Only Contracting Officers are legally authorized to commit the Government.

Offerors are cautioned that evaluation ratings may be lowered and/or proposal rejected if proposal preparation (Proposal format, content, etc.) and/or submittal instructions are not followed.

PART II – FULL TEXT ANNOUNCEMENT

BROAD AGENCY ANNOUNCEMENT (BAA) TITLE: AI for Next Generation C2

BAA NUMBER: BAA FA8750-23-S-7006

CATALOG OF FEDERAL DOMESTIC ASSISTANCE (CFDA) Number: 12.800, 12.910

I. TECHNOLOGY REQUIREMENTS:

The Air Force Research Laboratory is soliciting white papers under this Broad Agency Announcement for research, development, integration, test and evaluation of technologies/techniques to support research in the following focus areas:

Technical Area 1. Command & Control of AI Systems to Achieve Mission Tailored AI

OPERATIONAL CONTEXT FOR PROJECT “BATTLE MANAGEMENT OF AI”

The Air Force Research Laboratory is soliciting white papers under Technical Area 1 for research, development, integration, test, and evaluation of technologies/techniques that augment battle management systems with novel control processes for adapting behavior of artificial intelligence (AI) capabilities during mission execution. The resultant “Battle Management of AI” will enable operators to reason through a complex space of competing design factors and mission dependencies to select and deploy AI components that are compatible with mission tactics and evolving battlefield conditions. This capability will provide AF tactical operations with an improved level of agility and responsiveness that is paramount for safe and effective utilization of AI-based warfighting systems.

Modern warfare has become increasingly dependent on AI-based warfighting systems that use trained models to perform tasks at speed and scales beyond human capacity. These AI-based systems can support a variety of functions such as classification of targets for ISR and control of autonomous vehicles for combat. Because models are trained a priori on data (and simulations) in an anticipatory fashion, AI-based systems encounter situations in the real world that are incompatible with training feature distributions and parameterization of employed algorithms. The result is degradation to model performance that can negatively impact mission effectiveness and safety. Therefore, the Air Force requires new battle management processes to monitor performance of AI-based systems and update incumbent models in response to changing battlespace conditions. In the trivial case, operators will simply repurpose a pretrained model that fortuitously fulfills unanticipated mission requirements. In the extreme case, operators will coordinate a distributed workflow, known as an AI COA, to retrain, test, and deploy new models in line with mission execution, so that dependent systems can continue to function as intended with minimal loss of service. This process to detect shifts in performance of AI-based systems and adapt models for new environments is analogous to traditional battle management during conflict, where assets are provisioned and dynamically revectored to prosecute new targets in short order.

Figure 1 demonstrates how our AI control functions for “Battle Management of AI” could be applied to an ISR use case. A new kind of battle manager within the forward tent, deemed the AI Interface Officer, monitors the performance of computer vision models hosted on UAVs and looks for cases of “AI drift” – unexpected behavior caused when the domain of the learned function is no longer compatible with input data. In this case, an object detection model is no longer performant with live sensor data due to significant changes from a weather event. The AI Safety officer must evaluate the risk to mission success posed by continued employment of the model. If the risk is deemed too high, the AI Safety Officer will coordinate with remote operators via cloud-based services to determine the root cause of the drift and propose new AI adaptation strategies (e.g., replace model, fine tune, transfer learn, etc.) and deployment options (e.g., “use uplink to replace model on UAV at 1500 hours”) that accommodate the environment while also adhering to imposed mission timelines. In order to select the most appropriate response option, we expect that operators will rely on a host of information including model drift severity, available algorithms and data, computing power, and available time windows for when UAVs can be serviced with new models, etc. Each response option has a cost in terms of time, resource utilization, and expected benefit. Our AI control processes must help guide operators toward the best solution given the circumstances at hand. Once a new AI deployment solution is selected, the task force can instantiate and execute a workflow to deploy the new AI capability in an expedient manner: in this case, a new object detector that has been refined to handle low luminosity and noisy images resulting from inclement weather.

Figure 1: Battle Management of AI concept. A forward-facing AI Safety officer observes a drift event during an ISR mission and issues a request for a new object detection model that accommodates the new environment – a rain event obfuscating red targets in EO range.

Although Figure 1 highlights a remote sensing use case, the monitor-adapt-deploy pattern generalizes to other classes of AI-enabled missions beyond perceptual learning for ISR. For example, operators could update control policies onboard autonomous collaborative platforms (ACPs) with improved skills to evade adversary forces or provide cover fire. Some alert mechanism, perhaps triggered by unacceptable platform attrition rates or poor mission performance, should help operators decide when and how to update the autonomy. For the ACPs, operators face the challenge to select the right combination of behavioral policies and perceptive capabilities (e.g., “eyes” and “ears”) that are compatible for the motor characteristics of the platform and mission tactics. Once operators converge on the most appropriate configuration, some test and evaluation, perhaps using simulation, will provide evidence that the new platform behavior is safe, effective, and appropriate given the field conditions.

In contrast to the workflow presented in Figure 1, much of the DoD’s AI is currently designed and built by data scientists in pristine “lab-like” environments with low-stress settings, where compute resources are plentiful, environments are static, and response timelines are akin to those in academia and industry. In these settings, there are few competing factors that engineers must reconcile – the mantra is always “the more, the merrier” with regards to data, GPUs, epochs, and performance. In contrast, battle managers of AI must operate in austere environments with limited resources. The team must quickly analyze a complex trade space of engineering options to balance model performance with available power, compute cycles, policy restrictions, and response deadlines. For example, operators might choose to train smaller models with less parameters in order accommodate short timelines at the expense of robustness and generality.

Additionally, AI is usually sandwiched within software stacks or embedded within complex hardware systems. Although end users may have direct access to AI inferences (output), such as bounding boxes for object detection and blobs of natural language in large language models, the underlying models are not typically available for inspection or replacement. Therefore, battle management of AI requires a new kind of software architecture that embraces portability and composability of AI models. Operators need white-box visibility into AI-based systems and new software interfaces to query, publish, and deploy ad-hoc models onto platforms during mission execution. With the right user training, interfaces, and control processes that provide white-box insight for both operators and engineers, we propose that AI can be managed much like other physical assets.

Note that we are not soliciting proposals related to communications hardware or communications networks to address the orchestration of Battle Management of AI processes. There is an overall assumption that current communications networks and hardware are sufficient to support the concept. Additionally, we are not soliciting proposals for the design and implementation of AI development frameworks – this market is already saturated with contributions from both open source and industry. We do, however, seek innovation for how to connect existing AI development frameworks within battle management control stations and workflows to configure the behavior of AI as described in Figure 1. Although heavy compute is generally needed for training modern AI, we are soliciting neither hardware nor server farms – the government has portable HPCs with enough GPU processing power to support a variety of different AI training regimes and access to these resources will be provided to offerors.

TECHNICAL CHALLENGES:

Fundamentally, TA1 will enable operators to command and control the application of AI to suit specific mission objectives and environmental conditions. AI adaptation and deployment must look and feel like warfighting – operators should have command of AI behaviors (TA 1-1), control of AI manufacturing and deployment processes (TA 1-2), and situational awareness of AI-enabled assets in the battlespace (TA 1-3). Figure 2 presents these requirements in the form of three technical subareas, which are organized according to different phases of our battle management process. Note that grey components of the diagram, although critical to the end-to-end process, are not solicited by this TA1 and serve only to inform the design of software interfaces for future integration. Additionally, offerors may propose solutions for individual components or the complete end-to-end process.

Overall, the three technical areas form an AI manufacturing feedback loop, where military doctrine and battlefield conditions work in tandem to inform the design and deployment of mission-tailored AI. The manufacturing process begins with TA 1-1, which consumes mission state information in order to derive model specifications, known as AI COAs. The primary challenge for TA-1 is to translate “mission speak” devoid of explicit AI requirements into technical specifications that are realizable and sufficiently detailed to guide the AI adaptation and deployment process. TA 1-2 consumes AI COAs and instantiates workflows to manufacture and deploy the requested AI capability onto host platforms. The primary challenge for TA 1-2 is to coordinate tasks among distributed agents (human and automated) that perform traditional AI development activities, such as model training, synthetic data generation, and simulation runs for testing. Finally, TA 1-3 monitors the behavior of AI-driven platforms and outputs alerts to TA 1-1 when performance falls below certain effectiveness thresholds. The primary challenge for TA 1-3 is to understand which conditions cause AI to underperform (drift) and to capture relevant system state and data samples for consideration in TA 1-1 and TA 1-2 during the next cycle of design, manufacture, and deployment. The following sections provide further details about each technical area.

Figure 2: TA1 project structure organized according to phase within the battle management of AI process. White graphics depict technical areas and interfaces that are solicited by this BAA. Grey portions depict technologies and capabilities that will either be provided to offerors or modeled (stubbed in) by the government in order to exercise the full end-to-end process. The gradient on the “Deploy” arrow indicates that offerors may rely on mock platforms in cases when access to real platforms is cost prohibitive.

TA 1-1: AI COA Design: will enable battle managers to efficiently explore a complex space of (possibly competing) AI deployment options, known as AI COAs. The technology should derive a trade space of functional and non-functional requirements that specify the behavior and deployment of mission-tailored AI. The technology must consider mission artifacts, such as ATOs, ACOs, ISR sync matrices, and field reports, to ensure that output AI requirements are both valid and feasible. For example, a sync matrix will contain available time windows for when an AI model can be uploaded to a robot, which bounds training and evaluation completion times, whereas the Size, Weight, and Power (SWaP) of host platforms will dictate attributes like model size and inference rates.

Figure 3 illustrates a small hypothetical AI COA space. When an automatic target capability (ATR) exhibits aberrant behavior, an operator must choose the best adaptation and deployment options to resolve the drift. Because each step has a local cost, the technology should help operators understand the overall compounded cost as it pertains to fulfillment of mission requirements and resource availability. The system must provide the right kind of information for operators to reason about the merits of each option. For example, the system could communicate tradeoffs in terms of model accuracy versus deployment readiness, assuming better models take longer to train and evaluate. Ultimately, the key challenge is to help operators assess the risks associated with each AI COA as it pertains to mission success. This is especially difficult for novel COAs that lack a precedent or other historical data that could provide an empirical basis for comparison.

Although Figure 3 illustrates COA dependencies as a graph, Visual Analytics has proven that large node-link diagrams are ineffective communication mediums. Thus, we seek novel presentation approaches that make the AI COA selection process look and feel like a declarative specification (what we want) rather than a prescriptive process definition (how to build it). The actual instantiation and execution AI COAs is reserved for TA 1-2 below.

Figure 3: AI Deployment Search Space. Each option to build and deploy mission-compatible AI has associated benefits and limitations with regard to the specific mission at hand. Operators must choose the best options that satisfy objective AI behaviors and which meet delivery timelines.

In summary, TA 1-1 must address the following technical challenges:

· COA Ranking - trade-off analyses for different AI adaptation and deployment options in terms of mission compatibility, risk, model robustness, and projected delivery timelines. The system should team with battle managers to help determine which composition of AI resources are most compatible with mission rules, regulations, platform allocations, and scheduling.

· COA Selection – an interface that enables battle managers to explore the information generated by COA Ranking. Battle managers will review and refine COA options until a satisfactory solution is achieved. When battle managers make a final selection, the COA information must be shipped off in a form that can guide workflow instantiation and execution in TA 1-2.

· Resource Inventory (baseline provided by the Government) - a metadata management system that provides operators with an “AI inventory”, such as available datasets (and synthetic data generators), algorithms, models, and host platforms. The metadata should be current and rich enough for operators (or algorithms) to compose AI COAs that are both realizable and compatible with mission objectives. The COA Ranking system will use the AI resource metadata as input for trade-off analyses. The Government will make available the GOTS “AI Passport” federation system as a technology baseline that manages access control for different classes of distributed AI resources.

TA 1-2: AI Model Production: will execute AI COAs that control the manufacture and deployment of mission-tailored AI in accordance with specifications from TA 1-1. The technology will instantiate mixed-initiative workflows to compose AI resources, such as training sets, algorithms, and platforms, that are distributed across local HPCs and remote cloud services. Operators must be able to coordinate workflow execution from within next-generation battle management stations, such as those supported by the Tactical Operations Center (TOC) family of systems. Therefore, TA 1-2 is largely an integration effort that will repurpose existing battle management concepts to control the “traditional” DevSecOps cycle. Operators will use command and control (C2) message sets (e.g., UCI and UC2) and routing infrastructure to coordinate activities for data gathering, training, evaluation, and model upload.

For automated tasks, such as model training and evaluation, the system can rely on both local and cloud-based resources, if available. The computing environment will depend largely upon specific mission use cases, which are open for discussion. For manual tasks, such as labeling data or approving the final deployment onto a platform, the technology should inform operators about specific work requirements and delivery deadlines through some tasking system. For example, prior to model deployment onto a platform, the system could provide risk assessment reports (input) to the operator, who in turn must make the final decision (output) for whether to “hit the deploy button”.

Once a newly manufactured AI model is approved and ready for deployment, the system should post model updates onto the host platforms, perhaps while in motion assuming sufficient comms. Platforms should expose software interfaces that enable operators to dynamically read, update, and delete models, much like standard Create-Read-Update-Delete (CRUD) operations for databases. For platforms (e.g., collaborative combat aircraft) that may be cost prohibitive to instrument with our required AI management interface, offerors can use surrogate platforms and stub interfaces that simulate upload or provide other evidence that a model meets the host platform SWaP requirements. Although TA1 is not soliciting procurement of new platforms, we expect offerors for TA 1-2 to develop adaptors and processes around open platform interfaces when provided by the Government.

The agents (human and automated) working various pieces of TA 1-2 will be distributed across both tactical and enterprise locations, thus making the execution process vulnerable to contested communications. However, for the sake of this TA, the offeror may assume adequate network and comms to support distributed workflow orchestration and execution. Although TA 1-2 is decentralized, the supporting technology should provide a centralized view of workflow execution status that provides forward operators in TA 1-3 with projected completion timelines.

In summary, TA 1-2 must address the following technical challenges:

· AI COA Execution – mixed-initiative workflow middleware that will manufacture, test, and deploy AI capabilities onto host platforms in accordance with AI COAs from TA 1-1. The system should support distributed environments and scale to service multiple requests in parallel while maintaining awareness about resource allocation and utilization. The system should also seize opportunities to reuse intermediate products, when possible, to preempt lengthy development pipelines. For scenarios that require access to data across different security classifications, the government will provide GOTs tools, such as Phoenix Prime and AI Passport, to mitigate the need for offerors to build guards and access controls.

· C2 of AI Messaging – an extension to existing command and control message formats (e.g., UCI and UC2) and routing infrastructure that enables battle managers to coordinate the execution of distributed AI manufacturing workflows. The messages should describe inputs and outputs for specific tasks as well as expected response deadlines. The messaging infrastructure should also allow operators using TA 1-3 to subscribe to key events for overall status tracking of process execution.

· CRUD for AI – APIs to control reads, updates, and deletion of AI models onboard weapons platforms from ground stations. The APIs can be tailored for specific platforms, data formats, and model implementations, such as ONNX for deep learning – the Government is not soliciting solutions that attempt to standardize AI datasets or models. If an offeror has access to weapons platforms, the Government will work with the offeror to establish the appropriate set of requirements to interface with onboard hardware.

TA 1-3 AI Common Operating Picture (COP) – provides situational awareness about the health, status, and location of deployed AI assets. Operators need to know when models begin to drift and understand what environmental conditions had the most impact on mission performance. Therefore, we anticipate the need for an AI Common Operating Picture (or AI COP) that provides information about a model’s hosting platform, physical location, historical performance, and provenance about model owner and training strategy.

The system should rely on both notifications from model drift detectors and field reports from operators who have had first-hand observations of AI’s performance. Because drift detection research is still in its infancy, TA 1-3 solicits frameworks to integrate existing (possibly rudimentary) drift detection methods as opposed to development of new state-of-the-art algorithms. The framework should have access to model provenance from TA 1-2 in order to support out-of-distribution detection algorithms that rely on a model’s training dataset. Also, the framework should define software interfaces to establish I/O requirements for different kinds of detection algorithms (e.g., MITRE’s Menelaus and SEI’s Auger) and enable plug-and-play functionality. Ultimately, the framework should aggregate reports from multiple detectors and operator feedback in order to trigger drift events when the system reaches consensus.

TA 1-3 also seeks to understand which kinds of diagnostic data from AI-enabled platforms is most important for drift detection and establishing behavioral requirements for subsequent AI adaptation cycles. For example, flight telemetry data from collaborative combat aircraft (CCA) in conjunction with field observations about adversary responses could help operators understand the environmental factors that caused the autonomy to behave poorly, which will influence the selection of AI COAs.

In summary, TA 1-3 must address the following technical challenges:

· AI Capability Tracker – combines traditional position-location information (PLI) for mobile weapons platforms with AI metadata for resident models, such as class of algorithm, model owner, drift reports, etc. The system should present this information using existing visualization frameworks and metaphors that are familiar to battle managers, who will determine whether incumbent AI should be replaced based on the performance of models and mission success. Once operators determine the need to update a model and trigger the next cycle of manufacture, the tracker should provide status information from TA 1-2 regarding productivity and expected delivery times.

· AI Monitoring – enables plug-and-play integration of drift detection algorithms and live performance feedback (i.e., ground truth) from operators. We anticipate that model drift may be passively detected through data analyses that run silently in the background or through active reporting from warfighters who are dissatisfied with the performance of deployed AI. The framework should output drift assessments to the AI Capability Tracker and, upon detection, gather drift evidence (e.g., data samples from ground station feeds) to help operators in TA 1-1 establish new AI requirements that accommodate new battlefield states. Additionally, operators and engineers working TA 1-2 can use offending data samples gathered from TA 1-3 as training or evaluation data to assess the performance of new models.

TA1 PROGRAM EXECUTION AND EVALUATION:

Due to lack of relevant simulation environments and/or virtual labs that can exercise the kinds of battle management processes required for TA1, the Government will conduct live experiments to assess completion of technical milestones on-site at different military exercises. Thus, offerors must be prepared to accompany the Government at exercises and demonstrations throughout the period of performance. The different exercises will each provide a unique environment that is relevant for testing some component of the battle management of AI process. For example, Project Convergence will afford offerors with a chance to test how well the system can respond to ad-hoc ISR requests in desert settings using small UAS, whereas Valiant Shield will provide opportunities to test adaptation of autonomy for larger platforms in maritime settings.

Figure 4: Progression of battle management of AI from a centralized team of S&Es supporting a single mission to a federation of S&Es and warfighters across enterprise and forward tactical positions supporting multiple missions. For each experiment we will subject our Task Force to varying degrees of stressors, such as workload, operator expertise, and available resources, and use observable measures, such as productivity, adaptability, and team elasticity to assess the utility of our underlying AI development framework.

Figure 4 shows how TA1 will use demonstrations to mature our battle management of AI concept from a baseline of S&Es conducting AI adaptation in a centralized location to our objective end state, where monitoring, adaptation, and deployment processes are controlled primarily by uniformed personnel across different stations. Each exercise will allow the Government to assess whether milestones have improved our initial set of KPPs, which are open for refinement and additions from offerors. The table below provides some high-level candidate metrics as examples of the sort of information that the Government is interested in measuring throughout the life of the program.

The Government may provide Government Furnished Software (GFS) for this technical area and subareas of the AI toolbox for related efforts.

Technical Point of Contact:

Dr. Nicholas Del Rio

AFRL/RISC

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-3117 Email: nicholas.del_rio@us.af.mil

Technical Area 2. Federated, Composable Autonomy & AI Toolbox

The federated deployment and management of Artificial Intelligence (AI) holds vast importance for the United States and partners. The collaboration across AI tools and platforms is challenging due to the lack of common DOD and partner standards, classification boundaries, and the use of a diverse set of AI tools. The goal of this TA is to combine the technical expertise, resources, to develop tools, data, and suitable procedures to demonstrate the advanced federated deployment, and lifecycle management of AI capabilities to include but not limited to, development of common standards for data, algorithm, model, evaluation, and deployment of AI/ML capabilities, development and testing toolkits to enable third party development of AI/ML components and interoperability across a diverse set of AI/ML components, and workflow engine to facilitate orchestration, coordination, and composition of AI/ML components to form novel pipelines based on mission need. The underlying framework should be allow for federation of information and capability between new and existing AI/ML pipelines to include tracking of all metadata, data, and AI/ML sharing.

The emphases here, is on the development of collaborative federated AI tools (toolbox) and procedures that enable sharing of information, and raw data while in pursuit of common goal, while also ensuring the full compliance of policies and procedures of each federated partner. The AI toolbox should define the requisite interfaces and standards to allow third party to develop and contribute AI/ML components that encompasses all the stages, from data to AI capability, and incorporates communication and federation tools to support data and AI solutions. In essence, the toolbox must enable the composition of a wide variety of AI capability that can be stitched together to support diverse mission threads while not redeveloping or prescribing a single underlying AI/ML platform or tool.

Specific areas of interest for this TA include: (1) Shareable ML Models, and data representation, (2) Adaptable ML models, such as Low-shot & Interactive Learning (IL). The ability to adapt and train robust ML models in the presence of limited, incomplete, and dynamic data and operating environments is critical to all countries. (3) The federated, tailored, AI Common Operating Picture (AI-COP) to provide a shared view of all data, algorithms, and models resident with the joint toolbox. The COP provides warfighters with a comprehensive understanding of the tactical AI environment and helps the team to track capabilities, gaps, and productivity. (4) AI Management/Analysis that enables teams to optimize the time and resources used during ML data preparation, tagging, and training. (5) Federation and sharing of AI models, data, and tools that allows the best of AI resources across the federated environments.

The Government may provide Government Furnished Software (GFS) for this technical area of the AI toolbox for related efforts.

Technical Point of Contact:

Mr. Patrick Fisher

AFRL/RISC

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-7424 Email: patrick.fisher.6@us.af.mil

Technical Area 3. Advanced Wargaming Agents

Wargaming, and simulation plays an increasingly important role of modern warfare. These capabilities, provide the means to examine and explore warfighting concepts, explore scenarios from multiple perspectives and help to assess planning choices and their outcomes. As such, there is a need for a creation of a common architecture of software and hardware for campaign level planning which empowers the efficient development of artificial intelligence (AI) and enables the reuse of core AI capabilities. Over the last decade, there have been many breakthroughs in game-playing AI, however, there does not exist a method or practice to facilitate the incorporation and reuse of AI agents leading to the duplication of efforts to redeploy agents on new simulations or wargames. Given the immense importance of these AI integration in C2 decision making, there is a critical need to enable the efficient development agents at a faster pace with lower cost without the need to continuously duplicate previous efforts.

An example of possible solutions could be a wrapper for both agents and environments that could facilitate plug-n-play capabilities, or novel agent algorithms that allow for easy transferability and minimal retraining to play in new environments. This technology is focused more on learning agents such as reinforcement learning or genetic algorithms but can expand to other types of algorithms such as monte carlo tree search. AFRL/RI is not looking for hardcoded solutions to specific wargames but should apply to a general set of games (e.g. turn based hex wargames). AFRL/RI is looking to apply this technology to a collection of strategic level wargames and does not plan to utilize single unit or tactical simulation environments such as AFSIM unless to facilitate the strategic level plan.

Specific areas of interest include but are not limited to generalized approaches for unifying agents environments, new AI agents for playing diverse set of wargames, evaluation benchmarks, and gaming environments.

Technical Point of Contact:

Capt Shaun Ryer

AFRL/RISB

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-2261 Email: shaun.ryer.1@us.af.mil

Dr. Brayden Hollis 525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-2331 Email: brayden.hollis.1@us.af.mil

Technical Area 4. Interactive Learning for C4I

DoD faces significant data and human oversight challenges when attempting to employ AI/ML approaches to solving command, control, communications, computers and intelligence (C4I) problems. Interactive Learning (IL) is a data efficient ML approach that uses human in or on the loop to quickly train a model to make inferences. This approach is of interest to AFRL/RI in any situation where a user’s preference or intuition is required to solve a problem, and specifically encoding all necessary context to analytically solve the problem is infeasible.

As an example, AFRL/RI has implemented an IL approach that allows the users to interact with existing planning tools in a natural way via pairwise comparison of plan options. An AI agent observes the user’s interaction with the planning system and learns the user’s preferences for generating a “reasonable plan” that a user would accept. Essentially, the interactive learning agent asks the planning tool to generate a set of plans, using various planner input configurations or nudges, and presents the results to the use for ranking. This is akin to digital map giving the user two or three routes from start to destination and allowing the user to select which they prefer. This selection triggers the interactive learning agent to prompt the auto-planner to generate more plans in accordance with the users’ indicated preference, and then allow the user to evaluate those plans. This iterative process fits naturally into the iterative planning process, allows the user to focus on planning and not the mechanics of generating plans, allows the user to use their own experience and preference to obtain a good plan in accordance with their expectation, and leverages the speed of the machine. In addition to tactical planning, operational and strategic planning may be considered as well.

Planning is not the only area of interest for IL applications. Other applications may include but are not limited to intelligence tipping and queuing, course of action generation, image or spectrum analysis, hyper-parameter tuning, acceleration of high-fidelity modeling and simulation environments, and any DAF application of interest where human perception can be augmented via learning.

Submissions to this TA should include explanation of the query strategy (how you will solicit labels from the “oracle”) and the data efficiency of the approach (how your application will know which data to label).

Technical Point of Contact:

Daniel Carpenter

AFRL/RISA

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-7121 Email: daniel.carpenter.5@us.af.mil

Technical Area 5. C2 Complexity Dominance

Challenges to fully realizing the DoD’s JADC2 vision include the inherent complexity in joint all-domain operations. While characteristics of modern warfare such as tempo, scale of operations, interdependency, and command structures all contribute to decision complexity encountered by the DoD, adversaries also experience complexity when engaging with DoD forces in a Joint All-Domain environment.

The C2 Complexity Dominance technical area is focused on research to exploit operational complexity. By judiciously enhancing the complexity encountered by adversary forces through non-cyber means, DoD forces can shape the adversary’s understanding of the battlespace and their ultimate decisions and actions to the DoD’s advantage. In particular, this technical area is interested in approaches to develop, model, deploy, and assess techniques to impose complexity on both human and AI agents on the adversary’s side, in order to ultimately shape the adversary’s decisions and actions.

Dr. Ashley Prater-Bennette

AFRL/RISB

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-2033 Email: ashley.prater-bennette@us.af.mil

Technical Area 6. Generative AI C4I

With advancements of large language models and generative AI, it is now possible able to generate answers as if curated by human, write poems & documents that are contextual, to writing executable code, and generating useful web pages. These advancements are already having disruptions across fields like education (i.e., students use ChatGPT to write essays), code development, natural language web search and other areas. Some of the drawbacks of commercial solutions are the generated results could be constrained due to policies, or inability to fine-tune models.

The interest of this TA is to explore potential advantages of utilizing generative artificial Intelligence (GAI) in the domain of C4I and DAF application through prototyping relevant use-cases hosted in IL 6> environments, guided by successful applications of GAI in academia and industry. More specifically, this TA is interested in the following areas: (1) Aggregate: Utilize unstructured data by exploiting latent embedding space learned by latest Generative Transformer architectures (ChatGPT). (2) Reason: Develop a mapping capability between learned concepts in the NLP domain to actionable plans expressed in temporal causal logic (scenarios generated in predefined markup [*] represented as event flow models). (3) Assess: Train GPT style models to aggregate IL>6 information to allow objective conditional probabilities (in contrast to probabilities of token occurrence) to be extracted from unstructured data. (4) React: Fill gaps in operational data through plausible generative sampling constrained by learned distributions (Stable Diffusion). In addition, this TA is interested in identifying novel DAF C4I generative use cases and rapidly exploring and assessing their efficacy on DAF data and environment.

Dr. Edward Verenich

AFRL/RISC

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-2766 Email: edward.verenich.2@us.af.mil

Technical Area 7. Software Defined Distributed C2

Future contested fights with a near peer adversary will demand the need to command and control (C2) an increasingly dynamic set of time sensitive missions that are highly dependent on adversary, environment, and mission conditions. The evolving peer threat has created an operational reality that the Air Force (AF) must contend with; connected austere C2 nodes can be threatened and that communications avenues can be contested and degraded. This reality is driving the creation and adoption of Agile Combat Employment (ACE) Concepts of Operations (CONOPS) that are both agile and survivable by employing various adaptive basing strategies. These CONOPS have emphasized this inherent tradeoff between efficiency and resiliency in order to meet process execution timelines, balance resource utilization, and account for the capacity of distributed C2 functions.

The Software Defined Distributed C2 technical area is focused on research to optimize and orchestrate distributed C2 nodes with the associates C2 processes, functions, and resources that are distributed across the theater. By distributing C2 functions and resources through ACE CONOPS, the AF will increase the available resources and survivability of these resources. The challenge is fully realizing the AF’s vision of increasing capacity and resiliency through ACE. This technical area is interested in approaches to develop, model, and assess techniques of an orchestration logic to replace the manual lift of coordinating the execution of AF planning and operational processes across the distributed C2 nodes.

Another challenge is the expected contested operational environment that ACE is preparing for. A dynamically changing set of external factors like environment and intent of mission threads will require an approach that can orchestrate processes in execution, manage the complexity of shared resources and maximize the capacity and resiliency of the distributed C2 functions. This tech area is interested in technical concepts or proposed approaches that account for dynamic changes across the C2 nodes and apply a priority of process execution to enable the AF to efficiently utilize the distributed resources available in a contested environment.

Mr. Ryan Hilliard

AFRL/RISB

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-2571 Email: ryan.hilliard.3@us.af.mil

Technical Area 8. Tactical AI

Secondary sensors distributed on the battlefield have untapped potential for DoD operations. The Air Force is heavily reliant on radiofrequency (RF) signal connectivity critical mission functions across all domains, creating a need for technologies to detect, reason over, and ultimately mitigate sources of interference. The Global Positioning System (GPS), for instance, is particularly susceptible to environmental occlusion, signal loss from multipath propagation effects, and intentional jamming and spoofing by an adversary. Mobile end-user devices already deployed and in operational use have sophisticated sensing capabilities that can be harnessed via the TAK ecosystem (Android Tactical Assault Kit (ATAK) and TAK Server) towards the detection and localization of signals of interest such as Global Navigation Satellite System (GNSS) jammers/spoofers, cellular jammers, gunshots, Wi-Fi, tactical radios, and civilian UAS.

The goal of this technical area is to develop, demonstrate, and assess the scalability of efficient command and control protocols and intelligent central control processes to enable distributed signal detection and geolocation. Approaches should consider factors such as client sensing duty cycle, proximity to other sensors, status of nearby devices, battery, performance optimizations, threat levels/posture, and reporting schedules. Scalability is a primary consideration, with application deployment environments ranging from the tactical level (tens to hundreds of devices joined by a TAK server) to the civil level (leveraging government-issued and managed devices numbering in the millions). Proposals considering AI/ML may employ TAK-ML, a machine learning framework designed to facilitate data collection, training, execution, inference, and deployment of ML capabilities in the TAK ecosystem, David Castello

AFRL/RISC

525 Brooks Rd Rome, NY 13441-4505 Telephone: (315) 330-4043 Email: david.castello@us.af.mil

IMPORTANT NOTES REGARDING:

FUNDAMENTAL RESEARCH. It is DoD policy that the publication of products of fundamental research will remain unrestricted to the maximum extent possible. National Security Decision Directive (NSDD) 189 defines fundamental research as follows:

‘Fundamental research’ means basic and applied research in science and engineering, the results of which ordinarily are published and shared broadly within the scientific community, as distinguished from proprietary research and from industrial development, design, production, and product utilization, the results of which ordinarily are restricted for proprietary or national security reasons.

As of the date of publication of this BAA, the Government cannot identify whether work proposed under this BAA may be considered fundamental research and may award both fundamental and non-fundamental research. Proposers should indicate in their proposal whether they believe the scope of the research included in their proposal is fundamental or not. While proposers should clearly explain the intended results of their research, the Government shall have sole discretion to select award instrument type and to negotiate all instrument terms and conditions with selectees.