Artificial Intelligence (AI) Research Institutes

Artificial Intelligence (AI) Research Institutes is sponsored by National Science Foundation (NSF) and USDA National Institute of Food and Agriculture (NIFA). This program, jointly sponsored by NIFA and NSF, aims to advance foundational AI research in agriculture and food systems, build new multidisciplinary communities, and create the workforce needed for an AI-powered revolution in agriculture.

NSF 20-604: National Artificial Intelligence Research Institutes | NSF - U.S. National Science Foundation Archived funding opportunity This solicitation is archived. Important information for proposers and award recipients All proposals must be submitted in accordance with the requirements specified in the funding opportunity and in the Proposal & Award Policies & Procedures Guide (PAPPG) and its supplements .

All NSF grants and cooperative agreements are subject to the applicable set of NSF award terms and conditions . NSF has updated its research security policies for NSF funded projects. NSF 20-604: National Artificial Intelligence (AI) Research Institutes Accelerating Research, Transforming Society, and Growing the American Workforce Please refer to Frequently Asked Questions (FAQs) NSF 20-123 for more information.

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5mb) National Science Foundation Directorate for Computer and Information Science and Engineering Directorate for Biological Sciences Directorate for Education and Human Resources Directorate for Engineering Directorate for Geosciences Directorate for Mathematical and Physical Sciences Directorate for Social, Behavioral and Economic Sciences Office of Integrative Activities National Institute of Food and Agriculture Department of Homeland Security, Science & Technology Directorate U.S. Department of Transportation, Federal Highway Administration Full Proposal Deadline(s) (due by 5 p.

m. submitter's local time): Important Information And Revision Notes This is a revision of NSF 20-503, the solicitation for the National AI Research Institutes. This solicitation continues and expands a multi-agency effort to establish institute-scale AI research with the potential for long-term payoffs in AI.

The list of desiderata (and corresponding solicitation-specific evaluation criteria) for Institutes has been expanded from five to six. The intent is to emphasize separately the goals that Institutes be multidisciplinary and that they be comprised of multiple organizations working together, led by organizations distributed throughout the country to grow new centers of AI leadership.

Revised themes for Institute proposals (see program description). Please note the following changes in these themes: The Theme “AI-Augmented Learning” is continued, soliciting an institute in response to that broad theme. In addition, an institute is solicited with a primary focus on advancing research in AI techniques to focus on Adult Learning.

The Theme “AI-Driven Innovation in Agriculture and the Food System” is continued and includes a new reference to the USDA Science Blueprint for 2020-2025 and the overarching themes that provide a framework for USDA’s science initiatives. The remaining themes are new for this solicitation. This solicitation does not invite planning proposals.

Planning activities may be a feature of future solicitations in this program. Limit on Number of Proposals per Organization: This solicitation imposes a limit on the number of proposals that may be submitted by an organization. Limit on Number of Proposals for Senior Personnel: An individual may be designated as senior personnel on at most one project team submitting to this solicitation.

Note that this is equivalent to the restriction in the prior solicitation for that competition’s “Institute Track”. Agency partners on this solicitation have changed. With this solicitation, NSF expands the sponsors to include partners from industry, who share NSF's commitment to increasing national competitiveness in AI and have indicated an interest in the themes called out in this solicitation.

Details of these partnerships are spelled out in the program description. Any proposal submitted in response to this solicitation should be submitted in accordance with the revised NSF Proposal & Award Policies & Procedures Guide (PAPPG) ( NSF 20-1 ), which is effective for proposals submitted, or due, on or after June 1, 2020.

Summary Of Program Requirements National Artificial Intelligence (AI) Research Institutes Artificial Intelligence (AI) has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits.

Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in machine learning (ML) have made it possible for AI research and development to create new sectors of the economy and revitalize industries.

Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

This program is a joint government effort between the National Science Foundation (NSF), U.S. Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA), U.S. Department of Homeland Security (DHS) Science & Technology Directorate (S&T), and the U.S. Department of Transportation (DOT) Federal Highway Administration (FHWA).

New to the program this year are contributions from partners in U.S. industry who share in the government’s goal to advance national competitiveness through National AI Research Institutes. This year’s industry partners are Accenture, Amazon, Google, and Intel Corporation.

This program solicitation invites proposals for full institutes that have a principal focus in one or more of the following themes, detailed in the Program Description: Theme 1: Human-AI Interaction and Collaboration Theme 2: AI Institute for Advances in Optimization Theme 3: AI and Advanced Cyberinfrastructure Theme 4: Advances in AI and Computer and Network Systems Theme 5: AI Institute in Dynamic Systems Theme 6: AI-Augmented Learning Theme 7: AI to Advance Biology Theme 8: AI-Driven Innovation in Agriculture and the Food System Cognizant Program Officer(s): Please note that the following information is current at the time of publishing.

See program website for any updates to the points of contact. AI Institutes Program Team, email: AIInstitutesProgram@nsf.

gov Applicable Catalog of Federal Domestic Assistance (CFDA) Number(s): --- USDA-NIFA Agriculture and Food Research Initiative --- Highway Research and Development Program --- Mathematical and Physical Sciences --- Computer and Information Science and Engineering --- Social Behavioral and Economic Sciences --- Education and Human Resources --- Office of International Science and Engineering --- Office of Integrative Activities (OIA) --- Department of Homeland Security, Science & Technology Directorate Anticipated Type of Award: Cooperative Agreement Estimated Number of Awards: 8 NSF plans to make approximately 8 Institute awards.

Anticipated Funding Amount: $128,000,000 to $160,000,000 Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

Who May Submit Proposals: Proposals may only be submitted by the following: Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members.

Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus.

Non-profit, non-academic organizations: Independent museums, observatories, research labs, professional societies and similar organizations in the U.S. associated with educational or research activities. There are no restrictions or limits. Limit on Number of Proposals per Organization: An organization may submit no more than two proposals to this solicitation as lead institution.

Organizations wishing to contribute to more Institute proposals are encouraged to participate as non-lead organizations in Institute proposals in a manner that helps to create significant new research capabilities in new centers of AI leadership throughout the country.

In the event that an organization exceeds these limits, proposals will be accepted based on earliest date and time of proposal submission, i.e., the first two proposals will be accepted, and the remainder will be returned without review.

Limit on Number of Proposals for Senior Personnel: 1 An individual may be designated as senior personnel (which includes but is not limited to PI or co-PI) on at most one project team submitting to this solicitation.

In the event that an individual exceeds this limit, proposals will be accepted based on earliest date and time of proposal submission, i.e., the first proposal will be accepted, and the remainder will be returned without review. Proposal Preparation and Submission Instructions A.

Proposal Preparation Instructions Letters of Intent: Not required Preliminary Proposal Submission: Not required Full Proposals submitted via FastLane: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www. nsf.

gov/publications/pub_summ. jsp? ods_key=pappg .

Full Proposals submitted via Grants. gov: NSF Grants. gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.

gov guidelines apply (Note: The NSF Grants. gov Application Guide is available on the Grants. gov website and on the NSF website at: https://www.

nsf. gov/publications/pub_summ. jsp?

ods_key=grantsgovguide ). Cost Sharing Requirements: Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations: The following instructions apply to awards made by USDA-NIFA: For awards made by USDA-NIFA, Section 1462(a) and (c) of the National Agricultural Research, Extension, and Teaching Policy Act of 1977 (NARETPA) limits indirect costs for the overall award to 30 percent of Total Federal Funds Awarded (TFFA) under a research, education, or extension grant.

The maximum indirect cost rate allowed under the award is determined by calculating the amount of indirect costs using: the sum of an institution’s negotiated indirect cost rate and the indirect cost rate charged by subawardees, if any; or 30 percent of TFFA (TFFA = Field K. , Total Costs and Fee, on SF-424 R&R Budget).

The maximum allowable indirect cost rate under the award, including the indirect costs charged by the subawardee(s), if any, is the lesser of the two rates. If the results of 1), is the lesser of the two, the grant recipient is allowed to charge the negotiated indirect cost rate on the prime award and the subaward(s), if any. Any subawards would be subject to the subawardee’s negotiated indirect cost rate.

The subawardee may charge its negotiated indirect cost rate on its portion of the award, provided the sum of the indirect cost rate charged under the award by the prime awardee and the subawardee(s) does not exceed 30 percent of the TFFA. If the result of 2), is the lesser of the two, then the maximum indirect cost rate allowed for the overall award, including any subaward(s), is limited to 30 percent of the TFFA.

That is, the indirect costs of the prime awardee plus the sum of the indirect costs charged by the subawardee(s), if any, may not exceed 30 percent of the TFFA. In the event of an award, the prime awardee is responsible for ensuring the maximum indirect cost allowed for the award is not exceeded when combining indirect costs for the Federal portion (i.e., prime and subawardee(s)) and any applicable cost-sharing (see 7 CFR 3430. 52(b)).

Amounts exceeding the maximum allowable indirect cost is considered unallowable and will be handled accordingly. See sections 408 and 410 of 2 CFR 200. Other Budgetary Limitations: Other budgetary limitations apply.

Please see the full text of this solicitation for further information. Full Proposal Deadline(s) (due by 5 p. m.

submitter's local time): Proposal Review Information Criteria National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information Additional award conditions apply. Please see the full text of this solicitation for further information. Additional reporting requirements apply.

Please see the full text of this solicitation for further information. AI is advancing rapidly, enabled and significantly fueled by Federally-funded basic research. Increasingly sophisticated and integrated approaches for AI systems appear in applications across all sectors of the economy, and new challenges emerge for advancing, applying, and governing these promising technologies.

AI holds the potential to transform lives across our Nation through increased economic prosperity, improved educational opportunities and quality of life, and enhanced security.

At the same time, the potential capabilities and complexities of AI, combined with the wealth of interactions with human users and the environment, make it critically important to further advance our understanding of AI, including aspects of transparency, security, and control.

Among Federal research investments, institute-scale activities enable multidisciplinary, multi-stakeholder teams to focus on larger-scale, longer-time horizon challenges in both foundational and use-inspired AI research, and development of the future AI workforce, as well as addressing some of society's grand challenges.

National AI Research Institutes will serve as national nexus points for collaborative efforts spanning institutions of higher education, federal agencies, industry, and nonprofits/foundations in such areas. They should also accelerate the transition of AI innovations into many economic sectors, and nurture and grow the next generation of talent.

A long-term, substantive, and highly visible investment in AI research, infrastructure, and workforce development will realize the potential of, and enable the U.S. to maintain global leadership in, AI. AI enables computers and other automated systems to perform tasks that have historically required human cognition and human decision-making abilities.

Research in AI is therefore concerned with the understanding of the mechanisms underlying thought and intelligent behavior and their implementation in machines.

The full AI endeavor is inherently multidisciplinary, encompassing the research necessary to understand and develop systems that perceive, learn, reason, communicate, and act in the world; exhibit flexibility, resourcefulness, creativity, real-time responsiveness, and long-term reflection; use a variety of representation or reasoning approaches; and demonstrate competence in complex environments and social contexts.

What is sometimes referred to as “core AI” research addresses, in general, the theory and methods that give rise to these target abilities and their implementation in machines. It includes research in all matters of learning, abstraction, and inference required for intelligent behavior as well as general architectures for intelligence, integrated intelligent agents, and multiagent systems.

Machine learning, that is, methods for solving tasks by generalizing from data, has made great advances in recent years through the combination of new algorithms, increases in computing power, and the growing availability of data.

Machine learning does not, however, encompass all of core AI; that also includes research on knowledge representation, logical and probabilistic reasoning, planning, search, constraint satisfaction, and optimization. In some lines of AI research, computational models and mechanisms of intelligence draw direct inspiration from living systems.

Biologically-inspired computing draws from connectionism, behavior, and emergence in living systems to inform algorithm and system design. Computational neuroscience contributes models based on theory and analysis of computational processes in the nervous system.

Behavioral and cognitive science informs much of the motivation and design of systems seeking to implement behavior typical of human perceptual, motor, and cognitive processes and their interactions. Perception and communication are critical capabilities associated with intelligent behavior. Where AI is concerned, the field of computer vision studies methods that enable systems to sense and reason about the visual world.

Human language technologies (also known as “natural language processing” and “natural language understanding”) research enables intelligent systems to analyze, produce, translate, and respond to human text and speech. Intelligent systems may be able to act upon the world through embodiment. Robotics is closely aligned with but not identical to embodied AI.

While an embodied AI may be a robot, this solicitation does not include in its scope work teleoperated robots or industrial robots that simply repeat programmed patterns of motion.

As intelligent systems amplify humans’ capabilities to accomplish individual and collective goals, research is needed to assess the benefits, effects, and risks of AI-enabled computing systems; and to understand how human, technical, and contextual aspects of systems interact to shape those effects.

Relevant research areas therefore include consideration of explainable and trustworthy AI; validation of AI-enabled systems; AI safety, security, and privacy; and the role of emotion and affect in the design and perception of increasingly sophisticated machine intelligence.

Research in AI also encompasses novel software and hardware architectures, as well as methods for carrying out AI algorithms on a variety of computing systems and platforms, including those that operate under additional constraints such as time (e.g., real-time) or energy, or those targeting specific application classes or use cases.

Developing hardware further optimized for AI and ML algorithms or hardware offers the potential for even higher levels of performance. The above definition of AI and its principal disciplines establishes the scope of this National AI Research Institutes program. I.

B. Foundational and Use-Inspired AI Research Research in foundational AI seeks to develop theory and methods that are independent of any particular domain of application. Use-inspired AI research refers to basic research that has use for society in mind.

Use-inspired research seeks new methods and understanding in AI by situating the research in a domain of application to simultaneously inform progress in AI and solve particular use cases. As an example, foundational research in machine learning gave rise to breakthroughs in deep neural networks motivated by performance in controlled contexts like character recognition.

Later, use-inspired research in the intersection of machine learning and linguistics led to the development of recurrent neural networks in AI while also revolutionizing language modeling for speech and text processing.

We use the phrase “use-inspired” rather than “applied” to emphasize that this solicitation seeks to support work that goes beyond merely applying known techniques and adds new knowledge and understanding in both foundational AI and use-inspired domains.

Ideally there is a virtuous cycle between foundational and use-inspired research, where foundational results provide a starting point for use-inspired research, and the results from use-inspired research are generalized and made foundational. AI has advanced tremendously and today promises personalized healthcare; enhanced national security; improved transportation; and more effective education, to name just a few benefits.

Increased computing power, the availability of large datasets and streaming data, and algorithmic advances in ML have made it possible for AI development to create new sectors of the economy and revitalize industries. Continued advancement, enabled by sustained federal investment and channeled toward issues of national importance, holds the potential for further economic impact and quality-of-life improvements.

The National AI Research Institutes program – a joint effort of NSF, USDA-NIFA, DHS S&T, DOT FHWA, and several industry partners – will fund Institutes comprising scientists, engineers, and educators united by a common focus on advancing the research frontiers in AI.

AI Research Institutes will have as their primary focus the advancement of multidisciplinary, multi-stakeholder research on larger-scale, longer-time-horizon challenges in AI research than are supported in typical research grants.

They will accelerate the development of transformational technologies by grounding that research in critical application sectors that can serve as motivation for foundational research advances and provide opportunities for the effective fielding of AI-powered innovation. II. A.

AI Research Institutes Scope The vision of the National AI Research Institutes program is broad and ambitious. It is expected that each AI Research Institute will pursue this vision in ways that are uniquely suited to its selected research focus, facilities, collaborations, and other unique circumstances.

Proposers are encouraged to convey the unique qualities of the proposed Institute, while addressing the following desiderata common to all AI Research Institutes proposed to this program: AI Research Institutes advance foundational AI research that will have broad and lasting impact, adding significant new knowledge and understanding to the disciplinary areas associated with the definition of AI specified above.

Institutes aimed at advancing established AI lines of research should demonstrate the potential to radically advance these areas beyond the state of the art. Institutes might also address new foundational AI research priorities that arise from rapid advances in AI and the increasing ubiquity of AI-enabled technology.

These lines of research should be grounded in and integrated with broader foundational theories, paradigms, and architectures for computing and communication. AI Research Institutes conduct use-inspired research that both informs foundational AI advances and drives innovations in related sectors of science and engineering, segments of the economy, or societal needs.

Effective use-inspired research achieves synergy among a group of researchers to enable transformative advances in AI, related sectors, and the interfaces between these areas.

This dimension of an AI Research Institute will feature clear and compelling goals to advance AI and to accelerate the fielding of AI-powered innovation; it also enhances the transfer of knowledge through the meaningful exchange of scientific and technical information with external stakeholders such as industrial partners, public policy-makers, or international organizations, as well as with the broader scientific and education community.

Through use-inspired research, Institutes have the potential to create and share new community infrastructure, including data and software, to further research, promote reproducibility, and support education. AI Research Institutes actively build the next generation of talent for a diverse, well-trained workforce.

Specifically, AI Research Institutes should leverage the visionary nature of their research foci to drive new and innovative education and development tailored toward the Nation’s undergraduates, graduate students, and post-doctoral researchers, as well as through community colleges and skilled technical workforce training and other opportunities that advance knowledge and education of AI, including public understanding of AI.

This could include innovative pedagogy and instructional materials, advanced learning technologies, project-driven training, cross-disciplinary and collaborative research, industry partnerships, and new career pathways.

Institutes should offer broad, deep, and diverse experiences to build the next generation of the AI workforce, with a focus on broadening participation among the full range of groups traditionally under-represented in science and engineering. AI Research Institutes should maximize their unique position to grow the next generation of talent that will provide new discoveries and leadership.

AI Research Institutes are coherent multidisciplinary groups of scientists, engineers and educators appropriate for a large-scale, long-term research agenda for the advancement of AI and the fielding of AI-powered innovation in application sectors of national importance. The multidisciplinary nature of these Institutes will catalyze foresight and adaptability beyond what is possible in single research projects.

Each Institute will be comprised of multiple organizations working together to create significant new research capabilities . NSF and partner organizations seek to grow the network of National AI Research Institutes in lead organizations distributed throughout the country to grow new centers of AI leadership and leveraging existing centers of excellence as appropriate.

Institutes are strongly encouraged to include organizations that can directly contribute to NSF’s commitment to broadening participation by engaging a diverse, globally engaged research community, integrating research with education and building capacity, and expanding efforts to broaden participation from underrepresented groups and diverse institutions across all geographical regions.

Participants should be meaningfully integrated into a diverse Institute that is more than just the sum of the parts. Each Institute will have a lead PI with demonstrated vision, experience, and capacity to manage a complex, multi-faceted, and innovative enterprise that integrates research, education, broadening participation, and knowledge transfer.

Each Institute will also be staffed with a Managing Director or Project Manager (distinct from the lead PI) and a suitable Management Team to oversee the operations of the Institute. An External Advisory Board is required for all AI Research Institutes. (Potential Advisory Board members should not be approached or identified until the Institute is funded.)

AI Research Institutes are nexus points for collaborative efforts , creating an organization that encourages the continuing growth of collaborations with external partners to bring together people, ideas, problems, and technical approaches for maximum impact.

As nexus points, Institutes have the potential to bring together the best teams and approaches from institutions of higher education, federal agencies, industry, and nonprofits/foundations.

They promote organizational collaborations and linkages within and between campuses, schools, and the world beyond, and further the Institute’s mission to broaden participation in research, education, and knowledge transfer activities through a network of partners and affiliates.

This solicitation will support cooperative agreements of between $16,000,000 and $20,000,000 for between four and five years ($4,000,000 per year on average). Proposals outside this range may be returned without review.

Institute proposals must convey clear and concrete plans for foundational AI research, use-inspired motivation and technology transition opportunities, the education and workforce development activities to be undertaken, and plans for multidisciplinary research community building appropriate to the proposed Institute's vision and mission. In this round of Institutes, proposals are being solicited in the following high-priority areas.

Submissions MUST have as a principal focus one or more of the following themes. It is advisable that a proposal address multiple themes only in cases where significant activity is planned in the six desiderata for Institutes for each theme addressed. Theme 1: Human-AI Interaction and Collaboration AI has significant potential to improve productivity across a growing range of domains.

Many of the most impactful uses of AI are in augmenting human abilities; therefore, it is critical to make human and AI collaboration more productive, robust, and fair.

To that end, research in the Institute will uphold high standards of scientific excellence and ethics, emphasizing aspects such as inclusive design, being socially beneficial, avoiding unjust bias, and being built and tested for safety, accountability and privacy principles.

An Institute for Human-AI Interaction and Collaboration will support research on all of the modalities through which people can collaborate with intelligent machines toward common goals.

Interaction can occur through spoken or written natural language, visual interaction, gesture, body language, affective sensing, tactile and physical interaction, graphical user interfaces, mixed and augmented reality environments, and combinations of these modalities. Most human-AI systems today handle only short, unambiguous exchanges between human and AI.

To surpass this limitation, research at the Institute will develop principles and methods for systems that support multi-step interactions and use rich context. This research should encompass multi-user and multi-AI interaction in order to address teamwork in mixed human-AI groups. AI systems exist across a range of domains, but significant opportunity remains for humans to truly collaborate with AI systems.

A key aspect of all work conducted by the Institute will be establishing confidence that AI systems are operating on fair and transparent principles that can be understood and vetted by the community and appropriately modified by stakeholders. Researchers will develop methods for AI systems to learn the goals and preferences of humans during interaction in order to support trustworthy and safe collaboration.

Principles include, but are not limited to, ethics, fairness, privacy, lack of deception, explainability, protection of vulnerable and protected populations, and participatory and inclusive design.

Research in the Institute should transcend one-size-fits-all solutions for human-AI interaction by, for example, taking an inclusive approach to assessing how personal and social values are embedded in AI systems, accounting for communication conventions that vary among different cultural and linguistic communities, and developing principles and systems that are accessible and adaptive to persons with varying abilities and disabilities.

In addition to developing general methods and technologies, the Institute will include use-inspired research involving multiple domains of human-AI interaction and collaboration. Domains could include healthcare, education, commerce, emergency response, digital assistants, transportation, manufacturing, or any other domain where fluid human-AI interaction and teaming is important.

An important objective of the Institute will be to create new scientific methods, measurement and analysis techniques, effective metrics and engineering best practices for verification, validation, and performance monitoring of human-AI collaboration systems that are robust and generalizable for operation in real-world environments.

The research will also demonstrate the effectiveness of these new testing methodologies using one or more domains of real-world applications as described above. The Institute is encouraged to be broadly multidisciplinary, integrating, as appropriate, fields such as linguistics, robotics, accessibility and human factors, psychology and cognitive science, sociology, ethics, technology studies, and other fields.

Fundamental theory, system development, and responsible transition to practice will be supported in the Institute. In addition to research, the Institute will support programs for training students to work effectively in interdisciplinary teams and to go on to become the next generation of leaders in human-AI interaction. Amazon and Google are each providing partial support for this Institute theme.

Theme 2: AI Institute for Advances in Optimization The AI Institute for Advances in Optimization will bring together researchers in constraint satisfaction and search, machine learning, operations research, theoretical computer science, and related fields (such as automated design, signal processing, circuits and systems, etc.) to develop powerful new tools for solving previously “impossible” large-scale problems in planning, resource allocation, strategic reasoning, network and system design and optimization, hardware and software design and verification, and general combinatorial optimization and search.

The Institute will support a virtuous cycle of fundamental and use-inspired research such that work on challenging real-world problems will inspire the creation of new general approaches that in turn will find applications in diverse domains.

Researchers will explore ways to integrate perspectives from both classical constrained and unconstrained optimization, such as branch and bound and cutting planes, algorithms and modern data-driven approaches such as reinforcement learning.

These methods, along with recent theoretical breakthroughs in AI, theory of computing, and operations research, will enable highly efficient combinatorial search to tackle foundational challenges that include, but are not limited to, highly non-linear constraints and objective functions (including complex logical constraints, mixed discrete and continuous actions, and novel objective functions); very large state spaces and action spaces; multiple objective functions; uncertainty over the state of the world or the objective function itself; constraint-driven design; multiple agents, including antagonistic settings; communication-efficient distributed optimization; and optimization on multiple time scales and time-varying graphs.

Research on data-driven optimization methods will tackle the central problem of improving generalization within and between problem domains to drastically reduce the amount of training data required. The research may include rigorous analysis and provable performance guarantees.

The Institute may also include work on human-assisted design and optimization, in which algorithms can include human interaction or can learn from human demonstration.

End use-cases that inspire these challenges include but are not limited to automated chip design and verification, automated machine learning, infrastructure management, production planning, design of wireless networks, transportation and logistics, and allocation of physical or natural resources.

In order to ensure generality of results, an Institute should include work on multiple use cases, where at least some of the use cases involve automated design or management of computer systems, software, or hardware. Researchers from computational and mathematical disciplines will work closely with domain experts in order to understand the limitations of current modeling and solution approaches and to propose new approaches.

They will seek to develop methods that ensure security, safety, and reliability in order to support mission-critical applications. The Institute will propose ways to capture the value of newly developed algorithms in reproducible tools or platforms that enable the broader community to apply these methods to multiple domains. The Intel Corporation is providing partial support for this Institute theme.

Theme 3: AI and Advanced Cyberinfrastructure Advanced cyberinfrastructure (CI) has become an essential component