SPECIAL NOTICE
A -- Mastering Complexity in Multi Domain Command & Control (MDC2)
- Notice Date
- 12/20/2017
- Notice Type
- Special Notice
- NAICS
- 541715
— Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)
- Contracting Office
- Department of the Air Force, Air Force Materiel Command, AFRL/RIK - Rome, 26 Electronic Parkway, Rome, New York, 13441-4514, United States
- ZIP Code
- 13441-4514
- Solicitation Number
- RFI-AFRL-RIK-18-02
- Point of Contact
- Dr. Lee Seversky, Phone: 315-330-2846, Gail E. Marsh, Phone: 315-330-7518
- E-Mail Address
-
lee.seversky@us.af.mil, Gail.Marsh@us.af.mil
(lee.seversky@us.af.mil, Gail.Marsh@us.af.mil)
- Small Business Set-Aside
- N/A
- Description
- Solicitation Number: RFI-AFRL-RIK-18-02 Notice Type: Special Notice Requests for Information (RFI) Title: Mastering Complexity in Multi Domain Command & Control (MDC2) NAICS Code: 54715 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) 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 and the landscape of research and development (R&D) that could drive possible technical studies as they pertain to mastering the complexity of Multi Domain Command and Control (MDC2). The Air Force (AF) is investigating the breadth of technologies required to enable a future Multi Domain Command and Control (MDC2) capability. AFRL/RI is interested in those technologies that can help it master complexity while executing Multi Domain Operations across Air, Space, and Cyber within three main technical areas; Complex Adaptive Systems, Complex Effects Analysis, and Machine Intelligence. This RFI is requesting information to better understand existing vendor offerings and the landscape of research and development (R&D) towards Multi-Domain Command and Control (MDC2). Technical Challenge: In any Command and Control (C2) system/environment there are three universally held parameters that must be dealt with in order to achieve any success in the environment: 1. Mitigate uncertainty in spite of the environment set by the adversary 2. Increase operational tempo to get inside the adversary's control loop 3. No increase in manpower to mitigate the uncertainty and increase the ops tempo We can view a C2 system as a function of the uncertainty, tempo, and manpower: C2 = f(uncertainty, tempo, manpower) w.r.t an operating domain. The ability to effectively command & control within a domain is dictated by the complexity of the operating domain. This complexity drives all three parameters, where complexity determines operational uncertainty, tempo, and dictates overall manpower requirements. Traditionally, C2 has been considered only for a singular domain, where the complexity has to date been manageable through existing C2 systems & processes. As the AF moves towards Multi-domain operations, this complexity becomes multiplicative, where each of the singular domains: Air, Space, Cyber, brings with it new operating dimensions in the form of rule-sets, resources, tempo, capabilities, and uncertainties that all must be accounted for, reasoned over, and integrated together in order to achieve a true multi-domain C2 capability. It is this complexity that the AF seeks to master. The ability to manage the complexity of C2 systems so that it does not slow AF operations, but rather speeds up operational tempo, and the ability of the AF to use this complexity as a weapon against adversaries to slow their operational tempo, degrade their effectiveness, and overwhelm their C2 functions becomes key. The mastering of complexity to allow AF freedom of movement, while at the same time using complexity to restrict the movement of an adversary, assumes that the human in the loop is capable of keeping pace or getting ahead of this complexity. Going forward this complexity will exceed human capacity and will require the detailed reasoning found in machine-level processing. It is for this reason that an investment in machine intelligence is required to enable the human user to master this complexity at speeds greater than the adversary. There exists a natural tension between these two end goals, on the one hand C2 systems must be sufficiently complex as to enable the development, control, and execution of complex effects, including the integration of multiple systems-of-systems, functions, and people. On the other hand, it should not be so complex that it impedes or nullifies the very impact of such effects on the adversary. Therefore, successful solutions are those where the complexity of the MDC2 system is less than that of the MDC2 effects that are deployed. Achieving this balance will ensure that the AF is able to outpace and overwhelm the adversary at a tempo and effectiveness that is less then what is required to plan, execute, and assess those effects. While this provides a general construct for measuring success, it will be critical to establish such measures of C2 complexity in order to guide and benchmark MDC2 technology solutions and capabilities. There are several technical areas of interest for this RFI. Each is described in more detail below. It is expected that a submission should address at least one of the following technical areas. Complex Adaptive Systems - Mastering complexity through composition, orchestration, and distribution of C2 services. C2 systems are inherently complex, comprised of many interconnected system-of-systems spanning people, policy, doctrine, geographic and functionally distributed resources & information, computing, and communication functions. This complexity will only further increase as we seek to move towards true MDC2. AFRL/RI seeks information on technologies that can assist in the management and mitigation of the complexity spanning next generation composable C2 architectures, active management of C2 functions & systems, analytics, orchestration (including distributed operations), and information representation & modeling techniques. Key challenges include but are not limited to 1) Composable C2 systems-of-systems to enable rapid evaluation, integration, and orchestration of new C2 capabilities into existing and new architectures and/or systems; 2) Distributed planning, execution, and assessment capabilities to provide a resilient C2 platform for contested operations and environments; 3) Machine-to-machine and machine-to-human workflows and analytics to achieve mission-optimized and adaptive C2-of-C2 capabilities driven by real-time data and intelligent systems for the identification, creation, and composition of C2 capabilities on demand; 4) Scalable information representation & modeling techniques for information & data exchange; and 5) Verifiable adaptive system-of-system composition, provenance, and explainability. Complex Effects Analysis - Controlling and executing complex effects at speed and scale. Multi-domain operations presents new opportunities for developing and executing complex effects chains that are highly synchronized across domains, continuously adapting to the battlespace, and layered to maximize overall effect. However, to achieve such complexity requires new tools and capabilities for realizing seamless effects generation, planning, and execution across domains. AFRL/RI seeks information on technologies that can assist in the quantification, planning, and execution of MD effects, automated planning tools, course-of-action generation, and effects analysis capabilities as well as visualization and exploratory techniques to aid the decision maker in the understanding and provenance of developed effects chains. Key challenges of imposing complexity on the adversary through deployment of multi-domain effects chains is the ability to 1) Characterize, predict, and assess their effect (how to measure the complexity that is imposed); 2) Synthesize, de-conflict, and layer complex effects packages (how to package effects); 3) Present & analyze integrated effects (how to understand the plans that were generated). Additional areas of interest include but are not limited to force readiness and presentation of forces and computation models for representing intent, mission objectives, plans, resource, and outcomes. Machine Intelligence - Harnessing the speed and scale of machines to exponentially increase human capacity to command & control in an increasingly complex battlespace. The complexity of MD operations will quickly exceed human capacity and will require machine reasoning to augment the human decision maker across all stages of the C2 Monitor, Assess, Plan, and Execute (MAPE) cycle. Foundational to realizing future C2 intelligent systems is the development of machine reasoning and learning systems for decision making and multi-agent systems for the coordination and planning of distributed agents across Air, Space, and Cyber in the presence of incomplete information and operation in uncertain and contested environments. AFRL/RI seeks information on capabilities and technologies that demonstrate how Machine Intelligence can be used to reduce the complexity of C2 systems as well as increase the speed and scale of operations to impose complexity. Key challenges include but are not limited to: 1) Plan analytics for the recommendation, adaptation, and synthesis of plans and courses of actions; 2) Generalized planning techniques, including but not limited to game-theoretic approaches and machine learning and multi-agent systems for large-scale battle management and operational strategy and tactics development; 3) C2 analytics for predicting and recommendation of new C2 workflows, system composition, and information exchange; 4) Distributed multi-agent, multi-objective planning capabilities to include planning with attrition, degraded communication, & planning in the presence of an active adversary; 5) Operationalizing Machine Learning to support algorithm deployment, human feedback, online model updates, and learning capabilities shared across multi-security boundaries for developing mission-tailored learning capabilities across C2 functions. For each proposed technology, the respondent should indicate its current Technology Readiness Level (TRL). For the to-be-designed technologies, the respondent should provide the timeframe and rough cost estimate (non-binding) needed to reach TRL 6 within each technology area. 3.0 REQUEST FOR INFORMATION (RFI) ABSTRACTS 3.1 CONTENT NO CLASSIFIED INFORMATION SHOULD BE INCLUDED IN THE RFI RESPONSE. 3.2 FORMAT All abstracts shall state that they are submitted in response to this announcement. The abstracts will be formatted as follows: Section A : A cover page identifying the company or organization, street address, and the names, emails and telephone numbers of the point of contact. In the case of partnerships, please provide the appropriate information for the lead POC. Also provide a short summary statement of the company's or party's experience/capabilities and a short summary of the organization's experience in the areas described above. This section is not included in the page count. Section B : Technical Summary. The Government is assessing the current state-of-the-art and future MDC2 complexity solutions. The RFI responses should describe the product solution proposed, address coverage of the requirements stated in this RFI by the proposed solution, explain the potential advantage to the Air Force, and provide a rough order of magnitude for the cost of the proposed solution. The abstracts shall be limited to 8 pages. All abstracts shall be double spaced with a font no smaller than 12pt font. All responses to this announcement must be addressed to the Government technical point of contacts (TPOC), listed in Section 7.0 of this announcement. Respondents are required to submit at least one electronic copy to the TPOCs in Microsoft Office Word. 3.3 ADDITIONAL INFORMATION. The submitted documentation and content thereof 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 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 SUBMISSION. RFI abstract due date is February 15, 2018. 7.0 AGENCY CONTACTS. Verification of government receipt or questions of a technical nature can be directed to the cognizant technical points of contact (TPOCs): Primary TPOC Secondary TPOC Dr. Lee Seversky Dr. Nathaniel Gemelli Telephone: 315-330-2846 Telephone: 315-330-3252 Email: lee.seversky@us.af.mil Email: nathaniel.gemelli@us.af.mil Questions of a contractual/business nature shall be directed to the cognizant Contracting Officer, as specified below: Gail Marsh Telephone: (315) 330-7518 Email: gail.marsh@us.af.mil
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