20-07 amend 5 second repub full text announcement.doc

AMENDMENT 5 TO BAA FA8750-20-S-7007

The purpose of this modification is to republish the original announcement, incorporating any previous amendments, pursuant to FAR 35.016(c).

This republishing also includes the following changes:

1. Part I, Overview Information:

a. No changes made;

2. Part II, Full Text Announcement:

a. Section IV.2, updates Content and Format language;

b. Section IV.3.a, updates the NISOPM reference to reflect it being codified in the CFR;

c. Section IV.4, adds paragraph g;

d. Section V., adds paragraph 4 regarding adequate price competition;

e. Section VI.1, updates the Proposal Formatting language;

f. Section VI.7, updates the applicable provisions;

g. Section VII: updated the OMBUDSMAN

No other changes have been 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: Stratagem: Applying State-of-the-Art Artificial Intelligence and Machine Learning Approaches to Air Battle Management

BAA NUMBER: FA8750-20-S-7007

PART I – OVERVIEW INFORMATION

This announcement is for an Open, 2 Step BAA which is open and effective until 29 Sep 2023. 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 4 PM Eastern Standard Time (EST) on 29 SEP 2023, the following submission dates are suggested to best align with projected funding:

For Focus Area 1 ONLY:

FY20 by 22 Nov 2019 FY21 by 31 Mar 2020 FY22 by 31 Mar 2021 FY23 by 31 Mar 2022

For Focus Areas 2 and 3 ONLY:

All potential Offerors are requested to wait to submit white papers for focus areas 2 & 3 until the BAA announcement is updated with specific dates. Due to funding limitations, it is anticipated that only one award will be made under Focus Area 2 and one award under Focus Area 3. Therefore, when the BAA announcement is updated to include white paper dates, if requested, proposals will also have a specific due date.

In lieu of an Industry Day, the Government will accept questions regarding the synopsis via email to the TPOCS identified in Section VII until 12:00 PM EST on 30 OCT 2019. The Government will then amend the synopsis with a Q&A document. After its initial posting, the Q&A document will not be updated. Any future changes to the BAA will be done via amendment to the synopsis.

See the Stratagem BAA Q&A document dated 8 NOV 2019 attached to this announcement.

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

CONCISE SUMMARY OF TECHNOLOGY REQUIREMENT: AFRL/RI is seeking innovative research to create a capability to develop new Artificial Intelligence-based capabilities that can reason in real-time about developments in the battlespace during wartime engagements and assist planners and decision makers responsible for reacting to those developments. As the Air Force begins to operate in contested environments against near-peer and/or peer adversaries, the demands on operational planners and warfighters will quickly increase, thereby requiring decision support assistance. In recent years, major developments in the field of Artificial Intelligence (AI) for video game playing agents has suggested that some of these approaches could be considered as candidates to provide that form of decision support. The objective of this effort is three-fold. First, AFRL is looking to investigate and develop machine intelligence approaches for supporting and performing operations in complex adversarial environments. It is imperative to explore the use of existing decision support AI algorithms and machine learning methods applicable to developing strategies and playing complex games. Second, AFRL desires the capability to capture human expertise to augment warfighter capability through the use of game play in Air Force (AF) relevant “video games” via interactive components. We will record and learn from subject matter experts and end-user warfighters using these AF video games that are motivated by operational use cases. Finally, AFRL will apply forms of domain adaptation techniques to transfer AI strategies and/or machine learned models from video game play to AF relevant challenge problems/simulations. AFRL envisions developing AI approaches in unclassified game playing domains and attempting to perform transfer to either unclassified or classified scenarios in tools like Advanced Framework for Simulation Integration and Modeling (AFSIM) or others. In order to develop these capabilities, focus will be placed on using existing game playing engines and newly developed interactive “simulation” environments that can be played by operators, either supported by or against AI agents.

BAA ESTIMATED FUNDING: Total funding for this BAA is approximately $9.9M. Individual awards will not normally exceed 24 months with dollar amounts normally ranging from $300,000 to $2,000,000. 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.

TYPE OF INSTRUMENTS THAT MAY BE AWARDED: Procurement contracts, grants, cooperative agreements or other transactions (OT) depending upon the nature of the work proposed. 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) (email requests are preferred):

Stratagem TPOC:

Brayden Hollis

AFRL/RISC

525 Brooks Rd

Rome, NY 13441-4505

Telephone: (315)330-2331

Email: brayden.hollis.1@us.af.mil Copies of white paper submissions should also be sent to:

Stratagem BAA MANAGER:

Mr. Warren Geiler

AFRL/RISC

525 Brooks Rd

Rome, NY 13441-4505

Telephone: (315)330-2122

Email: warren.geiler.1@us.af.mil Questions of a contractual/business nature shall be directed to the cognizant contracting officer, as specified below (email requests are preferred):

Ms. 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: Stratagem

BAA NUMBER: BAA FA8750-20-S-7007

CATALOG OF FEDERAL DOMESTIC ASSISTANCE (CFDA) Number: 12.800

I. TECHNOLOGY REQUIREMENTS:

The Air Force Research Laboratory is soliciting white papers under this Broad Agency Announcement (BAA) for research, development, integration, test, evaluation and delivery of technologies/techniques to create AI-based capabilities that could possibly assist planners and operators during the planning and execution management of warfighter forces. While these two roles and responsibilities are unique, we believe that the type of technology developed under this program has applicability to both. Specifically, AFRL/RI is looking to harness the power of new and existing breakthroughs in AI-based video game playing technologies developed over the past few years and apply those technologies to relevant Air Force-based “games”. The Air Force envisions game playing technologies to be valuable both as a planning tool, useful for performing “what-if” analysis of plans and courses of action, as well as a means of providing fast response capability for execution management.

Program Goal:

Games have always served as a major driver in the development of new Artificial Intelligence approaches for reaching expert levels of human playing capability. AI approaches spanning the “gaming spectrum” from Tic-Tac-Toe to StarCraft 2 have been developed and demonstrated game playing skill at least as good as expert human performance. AFRL is interested in developing human expert level AI technology that could be used as both a support capability for an operational planner as well as an adversarial opponent to wargame against. The vision for this program is to develop technologies that can be used in an AF-type game that an operational planner could use to “play” different plans out to find effective strategies to apply against potential adversaries. These plans could be human created, crafted, and modified, all based on the results of game play. In this form, planners/operators would sit and play the game much like real-time strategy games are played today, but with the ability to back up in time, change a plan to reflect a new understanding of the operating environment, or simply explore new strategies. Alternatively, the Air Force intends to develop plans at scale, exploring the vast space of possibility and then providing to a planner a set of plan options that have been discovered on their behalf. These plans could then be refined further through continued game play by the planner, changing and modifying as they see fit. AFRL views this as moving planning from a static process to a dynamic one.

To get to this vision of wargaming against an AI adversary (red agent) as well as being supported by AI capabilities (blue agent), AFRL/RI will be developing a prototype game in the Unity3D [1] game engine which will be provided to all awarded performers as a development and evaluation environment. This game is being crafted to address specific planner processes and requirements. Over the course of the program, operational planners will use this game and provide feedback regarding the types of AI produced plans being developed, the blue agent assistance, and provide valuable potential training data.

At the end of this program, success will be measured by how well AFRL can both develop plans that operational planners will accept as viable and effective, as well as to what extent AFRL can challenge planners to defeat an adversary that is attempting to disrupt their plans, operating in response to the planner’s strategy.

Technological Challenges:

A number of technical challenges have been identified that will be addressed under the Stratagem program:

1. Representation of the game

2. Dealing with imperfect information

3. Developing long-term strategy

4. Learning from limited data

5. Learning via interaction with human planners

6. Domain adaptation and transfer Representation of the game:

Proper representation of the game will be required in order to apply certain methods for reasoning and learning to take place. In terms of the types of information that will need to be encoded in the game representation will be things such as the rules of the game, the state and action space of the game, and payoff or utility functions that capture the reward or value of certain end states and behaviors. As different technical approaches may require different representations, AFRL will expect performers under the program to clearly define the representation used and why it was chosen as appropriate for their given approach. AFRL will also be taking into consideration the operating environment in which the game is being played. Factors such as terrain, weather, and positioning will need to be accommodated. The granularity of the operational elements in the game will be modeled at approximately the squadron level for air, brigade or battalion for ground, and ship level for maritime.

Dealing with imperfect information:

Imperfect information, as well as asymmetric information, is prevalent in many video games today and equally prevalent when attempting to develop plans that will play out against an adversary or respond to unintended adversarial actions. Whether that imperfect information is in the form of fog-of-war, popular in real-time strategy games like StarCraft 2 [2], or in the form of uncertainty about what an adversary is attempting to achieve, much like the different tracks of development in a game like Civilization [3], it is necessary to reason and make decisions in the face of that imperfect information. There have been many techniques developed to deal with imperfect information, both from the game theoretic side as well as the machine learning side (e.g. Reinforcement Learning). This program is open to suggested approaches from both perspectives.

Developing long-term strategy:

One of the strengths of human expert level AI game playing agents is the ability to plan long term strategy for achieving one’s goals. This was clearly demonstrated by the Google DeepMind’s AlphaStar [4] agents playing StarCraft 2 against human professional players. Many early approaches to playing StarCraft 2 relied on “out micro-managing” human players which proved unsuccessful for long-term success. AlphaStar’s major success in beating human professionals was accomplished by developing long-term strategy; understanding build paths, force compositions, tactics, all combined with flawless micro-management of forces, was required to defeat the human. While the pace of the operational planning process and wargaming will probably not require strong micro-management of force elements, other components similar to AlphaStar certainly will be. For example, the use of Long Short-Term Memory (LSTM) may be viable to train a recurrent neural network and may very well need to be combined with other components such as Reinforcement Learning to form an effective architecture for the development of a wargame strategy. While AlphaStar is a machine learning based approach, certain game theoretic approaches combined with proper abstractions and search mechanisms (whether supported by machine learning or not) may be applicable as well. Strategy developed to solve the game of heads-up no-limit poker by approaches such as Libratus [5] and DeepStack [6] used methods such as recursive reasoning, counterfactual value networks and regret minimization, as well as subgame solving methods. This program is open to suggested approaches from both perspectives.

Learning from limited data:

One of the limiting factors this effort will have to deal with is the lack of large volumes of data to train from. To reference AlphaStar again, much of the initial training and strategy development was learned from a large set of StarCraft 2 game replays. The Air Force does not have the luxury of game replays to train against. Therefore, if machine learning or other data intensive methods are to be used to develop strategy to play the wargame, methods of creating synthetic data, using forms of self-play, or methods of few-shot learning (if possible) may be necessary. Active learning approaches could certainly be applicable as well if an appropriate oracle/user-set can be established, potentially labeling good and bad plans as such and providing valuable feedback to the system.

Learning via interaction with human planners:

Access to operational planners to play against should be assumed to be limited. It is anticipated that AFRL will have access to planners/operators to wargame against a number of times throughout this program, but access should not be assumed to be frequent enough to create a substantial database. That said, it is desirable to capture human planner game play and planning strategies as it is most likely the case that valuable insights reside in those game play sessions. Although access to current day operational planners may be limited, other subject matter experts, former planners, and experienced human game players may offer viable interactive learning opportunities. Approaches such as imitation learning and learning from demonstration should be possible and could augment the data collected to build a training dataset.

Domain adaptation and transfer:

While the development of wargame capabilities will need to start very simple, focusing on one domain at first, AFRL envisions the ability to play/plan for a number of domains in the warfighting battlespace. AFRL intends to explore the possible use of domain adaptation approaches, such as transfer learning, to deal with a number of potential changes in the use of the wargame. Domain Adaptation and transfer considerations include (but are not limited to) to following:

· Shift to other domains: If strategies are developed for use of air forces, do those same strategies transfer well to ground forces, or maritime forces?

· Shift of operating environment and/or adversary: As it may be possible to develop an over-arching strategy that incorporates all domains that are under consideration, AFRL may need to adapt those strategies to new operating environments. For instance, an operation in a jungle environment against one adversary will likely be very different from a strategy used against an adversary in a desert or mountainous region of the world. Can AFRL use existing strategies to quickly create new strategies?

· Transfer between games/tools: Is it possible to use transfer learning to allow for training in one source environment and transfer to our game as the target? This could solve the data limitation problem as well if the source environment has sufficient data and maps well to the target. It may also be possible to develop strategies that are wargamed in the game environment and are desired to be used in other simulation tools such as the Advanced Framework for Simulation, Integration and Modeling (AFSIM) or the Air Warfare Simulation Model (AWSIM).

Stratagem Program Structure Given the above described technical challenges, the Stratagem program will consist of three focus areas. Focus area 1 will be the largest focus area, addressing technical challenges 1, 2, 3, and 4 with the goal of producing and end-to-end set of capabilities to play a wargame. Focus Areas 2 and 3 will be smaller efforts and are intended to address the technical challenges of learning from limited interactions with human planners (tech challenge 5) and exploring the possibility of transferability across domains and games/tools/simulations (tech challenge 6). The Stratagem program is only accepting whitepapers for Focus Area 1 at this time. A BAA modification will be made at a later date to announce requests for whitepapers for Focus Areas 2 and 3. They are introduced here only for a sense of completeness and direction of the program.

Focus Area 1 – Machine Intelligence for Strategy Development:

The focus of this area is to explore the use of new and existing algorithms and learning methods applicable to developing strategies. The Machine Intelligence for Strategy Development focus area seeks the development of a model architecture for an intelligent agent, determining which pieces that absolutely must be included in the design of an agent capable of reasoning in increasingly complex environments. This model architecture should be based around two main technologies: Computational Game Theory and Machine Learning, and will address technical challenges 1, 2, 3, and 4. However, compelling technical approaches that include elements outside of Computational Game Theory and Machine Learning would also be of interest.

The end goal is to develop a new state-of-the-art approach for matching and then exceeding expert human gameplay in advanced wargames. In particular, Focus Area 1 (FA1) should produce blue/red agents that can assist and/or play against operational planners in real-time as well as run large batches of games to produce plan options for operators to evaluate against their desired operational objectives. Plan quality will be a measure of number of objectives achieved operating within the constraints of currently available forces and forces arriving at different intervals during execution of a plan. Plans should also consider the timing (schedule) of forces arriving to battle, sustainment of existing forces in battle (logistics of resupply), and possible unknown capabilities of the adversary. Plans shall be saved at the conclusion of game play so they can be output in a to-be-determined format. Games will be recorded and stored such that we can continually build a game replay dataset for training agents.

In terms of size of operation and time, AFRL is considering wargames with an operational area of as little as 50 miles by 50 miles and as large as 500 miles by 500 miles. Timeline of execution of up to 100 days with a “turn” of the game being 1 day of execution. Referencing technical challenge 1, AFRL plans to model units at the squadron, brigade/battalion, and naval ship level of granularity. AFRL will be considering terrain, cities, and weather as modifiers to the offensive and defensive capabilities of a given unit, and conflict between adversary units will be reflected by simple probabilistic models, defined in the rules of the game. For example, the level of fidelity might be, given two equally modified units of different sizes, the larger unit will take (probabilistically) less damage than the smaller unit. At the end of a turn of conflict (1 day), each unit should have its capability updated – to include things such as number of troops/aircraft killed in action, wounded in action, how much munition is left, etc. Additional modifiers to offensive and defensive capability (at least for ground) should include terrain differences (e.g. higher elevation increases capability and vice versa) or city cover as well as time “dug in” to a location (being in a location for longer probably reflects higher level of defensive capability). Probability modifiers for air and naval conflict should be based on capabilities of the given craft and what they are in conflict with (i.e. air-to-air, air-to-ground, air-to-navel, etc). All of these concepts should be described in the game representation.

Focus Area 2 – Interactive Machine Learning:

The focus of this area is to develop a capability where a machine can learn to perform complex planning and game play actions via the use of human and AI planners/operators as teachers or opponents. This Focus Area will address technical challenge 5 and utilize existing, while possibly developing new approaches, for capturing human and heuristic expertise in game play via approaches such as Active Learning, Interactive Learning, Learning from Demonstration, and Imitation Learning. The plans produced via this project will be added to the game play dataset and used to augment training of blue and red agents.

Focus Area 3 – Domain Adaptation:

Focus Area 3 addresses technical challenge 6 and aims to apply existing forms of domain adaptation techniques and push further advances in the area of Transfer Learning. This focus area consists of two sub-goals as follows:

1. Successful transfer from existing games (source) to our wargame (target). Produce successful transfer from a game source such as StarCraft 2, and apply it to the wargame. Learning from rich data sources will then become possible and allow for AFRLs blue and red agents to produce more robust strategies.

2. Successful transfer from our wargame environment (source) to other tools/simulations (target) such as AFSIM and AWSIM. These tools are widely accepted in the operational community and will add validity to the Stratagem developed plans.

While this BAA is seeking white papers addressing each of the above focus areas, offerors may respond to one or more areas, or any portion thereof. Multiple white papers from a single proposer are allowed. There is NO requirement for a single, all-encompassing, comprehensive white paper. All potential Offerors are requested to wait to submit white papers for focus areas 2 & 3 until the BAA announcement is updated with specific dates. Due to funding limitations, it is anticipated that only one award will be made under Focus Area 2 and one award under Focus Area 3. Therefore, when the BAA announcement is updated to include white paper dates, if requested, proposals will also have a specific due date.

Stratagem Timeline

The Stratagem program is anticipated to run for 24 months and will begin with FA1, which will run the duration of the program. The output of FA1 will be delivered to the government and used to further develop the Unity3D wargame. Output from FA1 will also be used for Focus Area 2 (FA2) and Focus Area 3 (FA3), which are planned to start approximately 6-8 months later and last 12-18 months. AFRL anticipates at least four planner evaluation events, held once every 6 months, operational planners wargame against and with developed AI red and blue forces.

Program Metrics In order for the Government to evaluate the effectiveness of a proposed solution in achieving the program objectives, the program metrics listed below shall serve as the basis for determining whether satisfactory progress is being made to warrant continued funding of the program. Although the following program metrics are specified, proposers should note that the government has identified these goals with the intention of bounding the scope of effort, while affording the maximum flexibility, creativity, and innovation in proposing solutions to the stated problem. Metrics stated are only for Focus Area 1 at this time.

The first metric AFRL will be measuring against is Performance Level, with a Threshold of “Human Novice” and an Objective of “Human Expert”. The second metric is Number of Domains with a Threshold of “2” and an Objective of “3”. The third metric is Number of Agents with a Threshold of “2” and an Objective of “3”. The fourth metric is Scale with a Threshold of “Small-scale scenarios” and an Objective of “Full wargame scenario”.

Discussion on Program Metrics

The program metrics are to serve as high level measure of progress towards the end goal of developing an AI-based wargaming capability. Following is a brief discussion of these metrics meaning:

Performance Level

The threshold and objective measures for performance of the AI wargaming capability is based off of human levels of “play”. Simply, how well does an AI agent do playing the wargame against a human player? At the threshold, the AI player should be able to beat humans that are novices at the wargame; those that have experience (hours) playing the game, executing developed plans and adapting to adversary strategies, setting up achievable objectives, etc. The objective for the end of this program is to have an AI capability that can compete at, or above, the level of capability of seasoned veteran planners in realistic wargame scenarios (that have experience using the wargame tool).

Number of Domains The nature of operational planning and wargame exercises are inherently multi-domain. While the focus of this program is not directly concerned with solving multi-domain operations problems, AFRL will consider those scenarios as they pose a certain level of increased complexity than dealing with just a single domain. Considering games like Command and Conquer: Generals [7] and StarCraft 2 that are not necessarily considered “multi-domain games”, indirectly they do offer up flavors of multi-domain operations by considering ground, air, and cyber (scanning areas or hacking income) forces. AFRL requires a minimum of two domains be considered with the objective of three. These domains would include Air, Ground, and Maritime. Additional domains of Cyber and Space will be welcomed, but not necessary.

Number of Agents

The number of agents, or players, in the wargame is minimally 2. This can be agent-vs-agent or human-vs-agent. Agent-vs-agent will be necessary if utilizing machine learning methods or methods of self-play, in order to explore the space of possible game play scenarios. Human-vs-agent is required in order to test performance level (see above). As an objective however, AFRL would like to eventually see AI capability that can deal with a 3rd agent game player that represents either grey forces or another peer/near-peer adversary.

Scale

Similar to how initial mini-games of StarCraft 2 were used to train AI agents for game play in that game, AFRL envisions using smaller scale (subset of objectives-type) game scenarios to train an AI to handle different components of planning and playing out the wargame. These scaled back mini-games could be reduced domains (1 or 2), reduced size of operating environment, and/or reduced number of forces to plan for and adapt to. At the conclusion of the program, a full, multi-domain scenario at scale of realistic operations force levels should be playable by the AI.

The technical point of contact (TPOC) for each of these focus areas is:

Stratagem TPOC:

Brayden Hollis

AFRL/RISC

525 Brooks Rd

Rome, NY 13441-4505

Telephone: (315)330-2331

Email: brayden.hollis.1@us.af.mil References for Section I.

[1] https://unity.com [2] https://starcraft2.com [3] https://civilization.com [4] https://deepmind.com/blog/alphastar-mastering-real-time-strategy-game-starcraft-ii/ [5] https://science.sciencemag.org/content/359/6374/418 [6] https://science.sciencemag.org/content/356/6337/508 [7] https://en.wikipedia.org/wiki/Command_%26_Conquer:_Generals

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. Appropriate clauses will be included in resultant awards for non-fundamental research to prescribe publication requirements and other restrictions, as appropriate.

For certain research projects, it may be possible that although the research being performed by the awardee is restricted research, a sub-awardee may be conducting fundamental research. In those cases, it is the awardee’s responsibility to explain in their proposal why its sub-awardee’s effort is fundamental research.

CLOUD COMPUTING. In accordance with DFARS Clause 252.239-7010, if the development proposed requires storage of Government, or Government-related data on the cloud, offerors need to ensure that the cloud service provider proposed has been granted Provisional Authorization by the Defense Information Systems Agency (DISA) at the level appropriate to the requirement.

II. AWARD INFORMATION:

1. FUNDING: Total funding for this BAA is approximately $9,900,000. The anticipated funding to be obligated under this BAA is broken out by fiscal year as follows:

FY20 – $2,500,000

FY21 – $2,500,000

FY22 – $2,500,000

FY23 – $2,400,000

a. Individual awards will not normally exceed 24 months with dollar values normally ranging from $300,000 to $2,000,000. 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 of $9,900,000.

b. The Government reserves the right to select all, part, or none of the proposals received, subject to the availability of funds. All potential Offerors should be aware that due to unanticipated budget fluctuations, funding in any or all areas may change with little or no notice.

2. FORM. Awards of efforts as a result of this announcement will be in the form of contracts, grants, cooperative agreements or other transactions depending upon the nature of the work proposed.

3. BAA TYPE: This is a two-step open broad agency announcement. This announcement constitutes the only solicitation.

As STEP ONE – The Government is only soliciting white papers at this time. All potential Offerors are requested to wait to submit white papers for focus areas 2 & 3 until the BAA announcement is updated with specific dates. Due to funding limitations, it is anticipated that only one award will be made under Focus Area 2 and one award under Focus Area 3. Therefore, when the BAA announcement is updated to include white paper dates, if requested, proposals will also have a specific due date. DO NOT SUBMIT A FORMAL PROPOSAL. Those white papers found to be consistent with the intent of this BAA may be invited to submit a technical and cost proposal. See Section VI of this announcement for further details regarding the proposal.

III. ELIGIBILITY INFORMATION:

1. ELIGIBILITY: All qualified offerors who meet the requirements of this BAA may apply.

2. FOREIGN PARTICIPATION/ACCESS:

a. This BAA is closed to foreign participation. This includes both foreign ownership and foreign nationals as employees or subcontractors.

b. Exceptions.

1. Fundamental Research. If the work to be performed is unclassified, fundamental research, this must be clearly identified in the white paper and/or proposal. See Part II, Section I for more details regarding Fundamental Research. Offerors should still identify any performance by foreign nationals at any level (prime contractor or subcontractor) in their proposals. Please specify the nationals’ country of origin, the type of visa or work permit under which they are performing and an explanation of their anticipated level of involvement. You may be asked to provide additional information during negotiations in order to verify the foreign citizen’s eligibility to participate on any contract or assistance agreement issued as a result of this announcement

2. Foreign Ownership, Control or Influence (FOCI) companies who have mitigation plans/paperwork in place. Proof of approved mitigation documentation must be provided to the contracting office focal point, Amber Buckley, Contracting Officer, telephone (315) 330-3605, or e-mail Amber.Buckley@us.af.mil prior to submitting a white paper and/or a proposal. For information on FOCI mitigation, contact the contact the Defense Counterintelligence and Security Agency (DCSA). Additional details can be found at: https://www.dcsa.mil/mc/ctp/foci/

3. Foreign Nationals as Employees or Subcontractors. Applicable to any effort not considered Fundamental Research. Offerors are responsible for ensuring that all employees and/or subcontractors who will work on a resulting contract are eligible to do so. Any employee who is not a U.S. citizen or a permanent resident will be restricted from working on any resultant contract unless prior approval of the Department of State or the Department of Commerce is obtained via a technical assistance agreement or an export license. Violations of these regulations can result in criminal or civil penalties.

c. Information Regarding Non-US Citizens Assigned to this Project

1. Contractor employees requiring access to USAF bases, AFRL facilities, and/or access to U.S. Government Information Technology (IT) networks in connection with the work on contracts, assistance instruments or other transactions awarded under this BAA must be U.S. citizens. For the purpose of base and network access, possession of a permanent resident card ("Green Card") does not equate to U.S. citizenship. This requirement does not apply to foreign nationals approved by the U.S. Department of Defense or U.S. State Department under international personnel exchange agreements with foreign governments. It also does not apply to dual citizens who possess US citizenship, to include Naturalized citizens. Any waivers to this requirement must be granted in writing by the Contracting Officer prior to providing access. Specific format for waiver request will be provided upon request to the Contracting Officer. The above requirements are in addition to any other contract requirements related to obtaining a Common Access Card (CAC).

2. For the purposes of Paragraph 1, it an IT network/system does not require AFRL to endorse a contractor's application to said network/system in order to gain access, the organization operating the IT network/system is responsible for controlling access to its system. If an IT network/system requires a U.S. Government sponsor to endorse the application in order for access to the IT network/system, AFRL will only endorse the following types of applications, consistent with the requirements above:

a) Contractor employees who are U.S. citizens performing work under contracts, assistance instruments or other transactions awarded under this BAA.

b) Contractor employees who are non-U.S. citizens and who have been granted a waiver.

Any additional access restrictions established by the IT network/system owner apply.

3. FEDERALLY FUNDED RESEARCH AND DEVELOPMENT CENTERS AND GOVERNMENT ENTITIES: Federally Funded Research and Development Centers (FFRDCs) and Government entities (e.g., Government/National laboratories, military educational institutions, etc.) are subject to applicable direct competition limitations and cannot propose to this BAA in any capacity unless they meet the following conditions:

a. FFRDCs: FFRDCs must clearly demonstrate that the proposed work is not otherwise available from the private sector; and FFRDCs must provide a letter on official letterhead from their sponsoring organization citing the specific authority establishing their eligibility to propose to Government solicitations and compete with industry, and their compliance with the associated FFRDC sponsor agreement’s terms and conditions. This information is required for FFRDCs proposing to be prime contractors or sub-awardees.

b. Government Entities: Government entities must clearly demonstrate that the work is not otherwise available from the private sector and provide written documentation citing the specific statutory authority and contractual authority, if relevant, establishing their ability to propose to Government solicitations. While 10 U.S.C.§ 2539b may be the appropriate statutory starting point for some entities, specific supporting regulatory guidance, together with evidence of agency approval, will still be required to fully establish eligibility.

FFRDC and Government entity eligibility will be determined on a case-by-case basis; however, the burden to prove eligibility for all team members rests solely with the proposer.

Government agencies interested in performing work related to this announcement should contact the Technical Point of Contact (TPOC). If resulting discussions reveal a mutual interest, cooperation may be pursued via other vehicles.

IV. APPLICATION AND SUBMISSION INFORMATION:

All responses to this announcement must be addressed to the Technical Point of Contact (TPOC) listed in SECTION VII. DO NOT send white papers to the Contracting Officer.

1. SUBMISSION DATES AND TIMES:

It is recommended that white papers be received by 4 PM Eastern Standard Time (EST) on the following dates to maximize the possibility of award:

For Focus Area 1 ONLY:

FY20 by 22 Nov 2019

FY21 by 31 Mar 2020

FY22 by 31 Mar 2021

FY23 by 31 Mar 2022

For Focus Areas 2 and 3 ONLY:

All potential Offerors are requested to wait to submit white papers for focus areas 2 & 3 until the BAA announcement is updated with specific dates. Due to funding limitations, it is anticipated that only one award will be made under Focus Area 2 and one award under Focus Area 3. Therefore, when the BAA announcement is updated to include white paper dates, if requested, proposals will also have a specific due date.

White papers will be accepted until 4 PM EST on 29 Sep 2023, but it is less likely that funding will be available in each respective fiscal year after the dates cited. This BAA will close on 29 Sep 2023.

All offerors submitting white papers will receive notification of their evaluation results within 45 days of submission. Offerors should email the TPOC and the Contracting Officer listed in Section VII, for status of their white paper(s) after 45 days, if no such correspondence has been received.

2. CONTENT AND FORMAT: Offerors are required to submit a 3 to 5 page white paper summarizing their proposed approach/solution. The purpose of the white paper is to preclude unwarranted effort on the part of an offeror whose proposed work is not of interest to the Government.

The white paper will be formatted as follows:

a. Section A: Title, Period of Performance, Estimated Cost, Name/Address of Company, Technical and Contracting Points of Contact (phone and email)(this section is NOT included in the page count);

b. Section B: Task Objective; and

c. Section C: Technical Summary and Proposed Deliverables.

All white papers shall be double spaced with a font no smaller than 12 point. Please note that less than 12 point font is acceptable for graphics and illustrations (as in labels and short descriptions) as long as it is readable when printed out on standard 8x11 paper. In addition, respondents are requested to provide their Commercial and Government Entity (CAGE) Code, their unique entity identifier and electronic funds transfer (EFT) indicator (if applicable), an e-mail address and reference BAA FA8750-20-S-7007 with their submission.

Multiple white papers within the purview of this announcement may be submitted by each offeror. If the offeror wishes to restrict its white papers, they must be marked with the restrictive language stated in FAR 15.609(a) and (b).

3. HANDLING AND MAILING INSTRUCTIONS:

a. CLASSIFICATION GUIDANCE. All Proposers should review the NATIONAL INDUSTRIAL SECURITY PROGRAM OPERATING MANUAL (NISPOM), 32 CFR Part 117, as it provides baseline standards for the protection of classified information and prescribes the requirements concerning Contractor Developed Information under paragraph §117.13. Defense Counterintelligence and Security Agency (DCSA) Site for the NISPOM is: http://www.dcsa.mil/.

In the event of a possible or actual compromise of classified information in the submission of your white paper or proposal, immediately but no later than 24 hours, bring this to the attention of your cognizant security authority and AFRL Rome Research Site Information Protection Office (IPO):

Information Protection Office (contact only if a security compromise has occurred) Monday-Friday (0730-1630):

Call 315-330-4048 or Email: vincent.guza@us.af.mil Evenings and Weekends:

Call 315-330-2961

b. CLASSIFIED SUBMISSIONS. AFRL/RISC will accept classified responses to this BAA when the classification is mandated by classification guidance provided by an Original Classification Authority of the U.S. Government, or when the offeror believes the work, if successful, would merit classification.

Security classification guidance in the form of a DD Form 254 (DoD Contract Security Classification Specification) will not be provided at this time since AFRL is soliciting ideas only.

Offerors that intend to include classified information or data in their white paper submission or who are unsure about the appropriate classification of their white papers should contact the technical point of contact listed in Section VII for guidance and direction in advance of preparation.

c. MAILING INSTRUCTIONS.

Unclassified electronic submission to the TPOC identified in Section VII will only be accepted. Encrypt or password-protect all proprietary information prior to sending. Offerors are responsible to confirm receipt with the TPOC. AFRL is not responsible for undelivered documents. If electronic submission is used, only one copy of the documentation is required.

Questions can be directed to the TPOC listed in Section VII.

4. OTHER SUBMISSION REQUIREMENTS/CONSIDERATIONS:

a. COST SHARING OR MATCHING: Cost sharing is not a requirement. Cost sharing may be proposed and will be considered on a case-by-case basis. Cost share will not be a factor in selection for award.

b. SYSTEM FOR AWARD MANAGEMENT (SAM). Offerors must be registered in the SAM database to receive a contract award, and remain registered during performance and through final payment of any contract or agreement. Processing time for registration in SAM, which normally takes forty-eight hours, should be taken into consideration when registering. Offerors who are not already registered should consider applying for registration before submitting a proposal. The provision at FAR 52.204-7, System for Award Management (Oct 2018) applies.

c. EXECUTIVE COMPENSATION AND FIRST-TIER SUBCONTRACT/ SUBRECIPIENT AWARDS: Any contract award resulting from this announcement may contain the clause at FAR 52.204-10 - Reporting Executive Compensation and First-Tier Subcontract Awards (Jun 2020). Any grant or agreement award resulting from this announcement may contain the award term set forth in 2 CFR, Appendix A to Part 25 which can be viewed at: https://www.govinfo.gov/app/details/CFR-2012-title2-vol1/CFR-2012-title2-vol1-part25-appA.

d. ALLOWABLE CHARGES: The cost of preparing white papers/proposals in response to this announcement is not considered an allowable direct charge to any resulting contract or any other contract, but may be an allowable expense to the normal bid and proposal indirect cost specified in FAR 31.205-18. Incurring pre-award costs for ASSISTANCE INSTRUMENTS ONLY are regulated by 2 CFR part 200.458, Pre-Award Costs.

e. GOVERNMENT APPROVED ACCOUNTING SYSTEM: An offeror must have a government approved accounting system prior to award of a cost-reimbursement contract per limitations set forth in FAR 16.301-3(a) to ensure the system is adequate for determining costs applicable to the contract. The acceptability of an accounting system is determined based upon an audit performed by the Defense Contract Audit Agency (DCAA). IMPORTANT: If you do not have a DCAA approved accounting system access the following link for instructions: https://beta.sam.gov/opp/e628c811fafe041accdddf55fb8539bf/view?keywords=AFRL-BAA-Guide&sort=-relevance&index=&is_active=true&page=1

f. HUMAN USE: All research involving human subjects, to include the use of human biological specimens and human data, selected for funding must comply with Federal regulations for human subject protection. Further, research involving human subjects that is conducted or supported by the DoD must comply with 32 CFR 219, “Protection of Human Subjects” found at: http://www.access.gpo.gov/nara/cfr/waisidx_07/32cfr219_07.html, and DoD Instruction 3216.02, “Protection of Human Subjects and Adherence to Ethical Standards in DoD-Supported Research” found at: http://www.dtic.mil/whs/directives/corres/pdf/321602p.pdf.

1. Institutions awarded funding for research involving human subjects must provide documentation of a current Assurance of Compliance with Federal regulations for human subject protection, for example a Department of Health and Human Services, Office of Human Research Protection Federal Wide Assurance found at: http://www.hhs.gov/ohrp.

2. All institutions engaged in human subject research, to include subcontractors, must have a valid assurance. In addition, personnel involved in human subject research must document the completion of appropriate training for the protection of human subjects.

3. For all research that will involve human subjects in the first year or phase of the project, the institution must submit evidence of a plan for review by an institutional review board (IRB) as part of the proposal. The IRB conducting the review must be the IRB identified on the institution’s Assurance of Compliance. The protocol, separate from the proposal, must include a detailed description of the research plan, study population, risks and benefits of study participation, recruitment and consent process, data collection, and data analysis. The designated IRB should be consulted for guidance on writing the protocol. The informed consent document must comply with 32 CFR 219.116. A valid Assurance of Compliance and evidence of appropriate training by all investigators should accompany the protocol for review by the IRB.

4. In addition to a local IRB approval, an AFRL-level human subject regulatory review and approval is required for all research conducted or supported by the DoD. The Air Force office responsible for managing the award can provide guidance and information about the AFRL-level review process. Confirmation of a current Assurance of Compliance and appropriate human subjects protection training is required before AFRL-level approval can be issued.

5. The time required to complete the IRB review/approval process will vary depending on the complexity of the research and/or the level of risk to study participants; ample time should be allotted to complete the approval process.