Requests for Information (RFI) For Operationalizing Machine Learning for Command & Control (OMLC2) Program

Solicitation Number: RFI-AFRL-RIK-18-07 Notice Type: Special Notice Synopsis: Requests for Information (RFI) For Operationalizing Machine Learning for Command & Control (OMLC2) Program FEDERAL AGENCY NAME: Department of Air Force, Air Force Materiel Command, AFRL - Rome Research Site, AFRL/Information Directorate, 26 Electronic Parkway, Rome, NY, 13441-4514 1.0 GENERAL INFORMATION 1.1 THIS IS A REQUEST FOR INFORMATION (RFI) ONLY. This RFI is issued solely for information and planning purposes and does not constitute a Request for Proposal (RFP) or a promise to issue a RFP in the future. This request for information does not commit the Government to contract for any supply or service whatsoever. Further, the Air Force is not at this time seeking proposals and will not accept unsolicited proposals. Responders are advised that the U.S. Government will not pay for any information or administrative costs incurred in response to this RFI. All costs associated with responding to this RFI will be solely at the interested party's expense. Not responding to this RFI does not preclude participation in any future RFP, if any is issued. If a solicitation is released, it will be synopsized on the Federal Business Opportunities (FedBizOpps; www.fbo.gov) website. It is the responsibility of the potential offerors to monitor this site for additional information pertaining to this requirement. 1.2 FEEDBACK: Submission of an abstract is voluntary. Respondents are advised that AFRL is under no obligation to provide feedback with respect to any information submitted under this RFI. 1.3 REGULATORY GUIDANCE: This publication constitutes a Request for Information (RFI) as defined in Federal Acquisition Regulation (FAR) 15.201(e), "RFIs may be used when the Government does not presently intend to award a contract, but wants to obtain price, delivery, other market information, or capabilities for planning purposes. Responses to these notices are not offers and cannot be accepted by the Government to form a binding contract." 2.0 REQUEST FOR INFORMATION (RFI) The Air Force Research Laboratory, Information Directorate (AFRL/RI) is seeking information to better understand existing vendor offerings within the landscape of research and development (R&D) that could later drive the development of prototypes of Machine Learning (ML) enabled Operational Command & Control (C2) functions and assess their notional value1, 2 to Operational C2. The Air Force is investigating the incorporation of Machine Learning capabilities into Air Force C2 applications. As such, it is interested in the identification of C2 applications that can benefit from the incorporation of these capabilities, an understanding of how these applications and operations can notionally benefit, and the algorithms, and necessary data that will be a part of these implementations. This RFI is requesting information to better understand those AF C2 applications that have incorporated ML, those that could incorporate ML in the future and the algorithms which support these advanced capabilities. The C2 applications should fall into one of the following categories: Operational C2 supporting the air tasking process, battle management supporting operations execution, tactical-level C2 supporting the end-user, and Multi Domain C2. Motivation and Technical Challenge: In any C2 system/environment there are three universally held conditions that must be achieved in order to realize any success in the environment: 1. Mitigate the uncertainty in spite of the environment set by the adversary, 2. Increase the operational tempo to get inside the adversary's control loop, 3. Incur no increase in manpower to mitigate the uncertainty and increase the ops tempo. Successfully executing a C2 process requires that all three conditions be met. Rapid technological progress, and the rise in adversarial assertions across multiple domains add to the uncertainty and the ops tempo of already laborious and time consuming C2 operations, thus putting our C2 success conditions in jeopardy3. What is required to achieve the three conditions described above, is a means by which the system can support the user by conducting detailed reasoning at high speeds that it excels at, while allowing the human user to exercise higher level organizational reasoning that they excel at. Recent advances in ML such as Deep Learning, Generative Adversarial Networks, Reinforcement Learning, Active Learning, etc. are evident in current applications in both depth (capability), and breadth (applications across multiple differing domains). Applications of ML have already been demonstrated in various levels of autonomy to include applications across computer vision, text processing, recommendation systems, and navigation systems. Commercial and private sectors have also seen many successful applications of ML such as digital assistants (Siri, Google, Alexa), vehicle control (semi-autonomous driving, summoning the vehicle, self-parking), and robotics, etc. In each of these ML-enabled applications, the machine conducts detailed reasoning across potentially large volumes of various types of data. This leaves the human user available to make higher level decisions; it is this ML-enabled technology approach, within the context of AF C2 applications and functions, which are of interest to this RFI. Given the complexity of the C2 process (integration of people, procedures and systems) used to carry out the commander's intent, it is unclear what are the specific ML roles and applications within the operational C2 environment. The complexity is further compounded by stove piped legacy systems that are composed of many structured and unstructured data sources, and intertwined by hidden and manual processes. Rapid technological growth, multiple services, complex and latent processes (often not capturing important/relevant decision supporting data), heavy reliance on legacy software, long acquisition cycle, and limited resources (budgets, personnel) are the major reasons for lack of rapid application of ML. Different from the many successful commercial applications of ML - C2 poses unique operating challenges for ML to include data-scarce environments, dynamic problem and mission sets, explainability and trust, as well as the ability to leverage and follow hierarchy, roles, and authorities within a military domain and/or mission. The intent of the OMLC2 program is to identify near and mid-term ML implementation opportunities within AF C2 processes. These processes include the Operational level C2 functions supporting the air tasking process, battle management functions supporting operations execution, tactical-level C2 supporting end users, and Multi Domain C2 supporting multi domain operations4, 5, 6. The objective of this RFI is to solicit informational responses that contain all of the following: 1. Identify and describe potential ML-enabled C2 functions which fall into one of the categories listed above (i.e., C2 functions supporting the air tasking process, battle management supporting operation execution, tactical-level C2 supporting end users9, and Multi-Domain C2 supporting multi domain operations). 2. Identify and describe the data and ML algorithm requirements associated with the identified ML-enabled C2 functions. For example, what type and quantity of data is required by the algorithm, is it readily available, if not, at what cost & effort to acquire it; what specific ML algorithm is being targeted, how much effort is needed to mature the algorithm and adapt it to the C2 function/application. 3. Provide and describe a notional quantitative assessment of the value added by virtue of incorporating ML algorithm(s) and its applicable data into the potential C2 functions. Technology Readiness and Scope: The desired Technology Readiness Level (TRL) capability for this RFI is level 4 or higher. For each proposed approach, the respondent should indicate its current TRL. For the - to-be-designed' technologies, the respondent should provide the timeframe and rough cost estimate (non-binding) needed to reach TRL-6 with an implementation on hardware. The ML contribution should be highly evident and described using common ML technical descriptions. While advanced ML techniques is valued, this RFI is focused on new innovative ways to bring ML to C2 to achieve new C2 capabilities. Leveraging of open and existing ML technology and applying it to C2 is within scope and expected. The following list of items are considered out of scope: a) Application of ML algorithms into ISR8 functions and applications b) Domains other than Air, Cyber, and Space7 c) End-to-end system stacks and frameworks. ML capability or application must be severable from larger C2 system or ML system products or frameworks REFERENCES: 1. www.af.mil/Portals/1/documents/SECAF/AF_30_Year_Strategy.pdf 2. www.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf 3. www.doctrine.af.mil/Portals/61/documents/Annex_3-30/3-30-D25-C2-C2-Mechanisms.pdf 4. othjournal.com/2017/04/03/multi-domain-command-and-control-the-air-force-perspective-with-brigadier-general-b-chance-saltzman-part-1-of-2/ 5. www.af.mil/Portals/1/documents/csaf/letter3/Enhancing_Multi-domain_CommandControl.pdf 6. www.darpa.mil/attachments/AF-MDC2.pdf 7. https://www.af.mil/About-Us/Speeches-Archive/Display/Article/143968/cyberspace-as-a-domain-in-which-the-air-force-flies-and-fights/ 8. https://www.af.mil/ISR/ 9. Platform of Interest: https://en.wikipedia.org/wiki/Android_Tactical_Assault_Kit 3.0 SUBMISSION INSTRUCTIONS AND FORMAT 3.1 SUBMISSION DUE DATE: RFI abstract due date is October 26, 2018. 3.2 CONTENT: Should address the technical requirements identified in Section 2 in addition to the notes below. NO CLASSIFIED INFORMATION SHOULD BE INCLUDED IN THE RFI RESPONSE. The proposed period of performance for a proposed prototype should not exceed 24 months. 3.3 FORMAT: All RFI responses shall state that they are submitted in response to this announcement. The RFI response shall be formatted as follows: Section A: A cover page identifying the company or organization, street address, and the name(s), email(s) and telephone number(s) of the point(s) of contact. In the case of partnerships, please provide the appropriate information for the lead POC. Also provide a short summary statement of both the company and party's experience/capabilities and a summary of the organization's experience in the areas described above. This section is not included in the page count. Section B: A Technical Summary describing the following four parts in sufficient detail: 1. Identify and describe potential ML-enabled C2 functions which fall into one of the categories listed above (i.e., C2 functions supporting the air tasking process, battle management supporting operation execution, tactical-level C2 supporting the end-user, and Multi-Domain C2). 2. Identify and describe data and algorithm requirements associated with the ML-enabled application. 3. Provide and describe a - notional' quantitative assessment of the value added by virtue of incorporating ML algorithms into the potential C2 function. 4. Provide a rough order of magnitude for the cost and description of any proposed prototype. The RFI response shall be limited to 8 pages. The RFI response shall be double spaced with a font no smaller than 12pt. Electronic Submissions are preferred. All responses to this announcement must be addressed to the Technical Point of Contact TPOC(s) identified in Section 6.0. Responses must be provide in Microsoft Word format. Encrypt or password-protect all proprietary information prior to sending. Offerors are responsible to confirm receipt with the TPOC(s). AFRL is not responsible for undelivered documents. 3.3 ADDITIONAL INFORMATION: The submitted documentation becomes the property of the U.S. Government and will not be returned. No solicitation documents exist at this time. This is NOT an Invitation for Bid (IFB) or a Request for Proposal (RFP). The Government does not intend to award a contract on the basis of this request. This is a request for information (RFI) announcement for planning purposes only. The Government will not reimburse costs associated with the documentation submitted under this request. Responders are solely responsible for all expenses associated with responding to this inquiry. Although proposal terminology may be used in this inquiry, your response will be treated as information only and will not be used as a proposal. This announcement is not to be construed as a formal solicitation. It does not commit the Government to reply to information received, or to later publish a solicitation, or to award a contract based on this information. 3.4 PROPRIETARY INFORMATION: This notice is part of Government market research. Information received as a result of this request will be considered as sensitive and will be protected as such. Any company or industry proprietary information contained in responses should be clearly marked as such, by paragraph, such that publicly releasable and proprietary information are clearly distinguished. Any proprietary information received in response to this request will be properly protected from unauthorized disclosure. The Government will not use proprietary information submitted from any one source to establish the capability and requirements for any future acquisition, so as to inadvertently restrict competition. 4.0 INDUSTRY DISCUSSIONS. AFRL/RI representatives may or may not choose to meet with potential offerors. Such discussions would only be intended to get further clarification of potential capability to meet the requirements. 5.0 SPECIAL CONSIDERATIONS. Multiple abstracts within the purview of this RFI announcement may be submitted by each responder. 6.0 AGENCY CONTACTS. Verification of government receipt or questions of a technical nature can be directed to the cognizant technical point of contact (TPOCs): Primary TPOC Mr. Gennady Staskevich Telephone: 315-330-4889 Email: Gennady.Staskevich@us.af.mil Alternate TPOC(s) Dr. Nathaniel Gemelli Telephone: 315-330-3252 Email: nathaniel.gemelli@us.af.mil Dr. Lee Seversky Telephone: 315-330-2846 Email: Lee.Seversky@us.af.mil Questions of a contractual/business nature shall be directed to the cognizant Contracting Officer, as specified below: Amber Buckley Telephone: (315) 330-3605 Email: amber.buckley@us.af.mil