# Project 1501 — Autonomy/AI

**Program element:** 0601153N — Defense Research Sciences  
**Project:** 1501  
**Component:** U.S. Navy  
**Appropriation:** 1319 — RDT&E, Navy  
**Budget Activity:** 1 — Basic Research  
**Vintage:** President's Budget PB2027  
**Canonical URL:** https://hitchintel.com/programs/0601153N/1501  
**Parent:** https://hitchintel.com/programs/0601153N

## Summary

Project 1501 — Autonomy/AI requests $27.7M in FY2027, 5.3% of the $525.4M requested for program element 0601153N, up 842% on FY2026. 4 R-2A activities decompose the request, 2 new this cycle.

**Markets:** Uncrewed & Autonomy — matched on the project title only, and no market size is quoted.

## Funding profile

| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 0.0 |
| FY2026 | Enacted | 2.9 |
| FY2027 | Request | 27.7 |
| FY2028 | Outyear | 24.3 |
| FY2029 | Outyear | 26.4 |
| FY2030 | Outyear | 25.9 |
| FY2031 | Outyear | 25.5 |

> Estimate types are not summed. This project is one leaf of PE 0601153N; the PE total is the sum of its projects, never added to them.

## What project 1501 buys

The Autonomy/ AI Project conducts foundational research in: machine learning, reasoning and intelligence; automated image, scene, and signal understanding; large-scale distributed decision making; transferring learning and sharing knowledge across domains; creating trusted and explainable artificial intelligence; and human-AI collaboration techniques. Results of this research will lead to trusted, advanced AI techniques that will solve more complex tasks and enhance autonomy for DON systems and missions.

## Activities (R-2A) — 4

| Activity | FY2025 | FY2026 | FY2027 | Move | Page |
|---|---|---|---|---|---|
| Science of Artificial Intelligence | 0.0 | 2.0 | 13.4 | +583% | [a1](https://hitchintel.com/programs/0601153N/1501/a1) |
| Science of Autonomy | 0.0 | 0.0 | 10.0 | new | — |
| Intelligent & Collaborative Agents | 0.0 | 0.0 | 4.3 | new | — |
| Expeditionary Decision Superiority | 0.0 | 1.0 | 0.0 | −100% | — |

> Activities carry the prior, current and budget year only — no five-year plan. In the request year they partition this project exactly; in earlier years they can under-cover it.

### Science of Artificial Intelligence

- Continue research exploring integration of domain knowledge and physical models with machine learning for fast, robust learning of diverse complex concepts and tasks without the need for labeled data. - Continue research efforts regarding the use of artificial intelligence to advance the scientific understanding of AI-machine…

Full year-by-year narrative: https://hitchintel.com/programs/0601153N/1501/a1

### Science of Autonomy — NEW START

**FY2027 planned work.** Science of Autonomy and Control of Unmanned Systems Research investigations regarding critical multidisciplinary autonomy challenges that cut across areas/domains, including air, sea, undersea and ground. Research efforts include the following: Continue: - Investigating the scalable and robust distributed collaboration among autonomous systems. - Research on human/unmanned system collaboration. - Work on perception-based adaptation across uncertain naval environments. - Investigating embodied and situated intelligence and architectures. - Developing theory-based tools and methods for safe, assured, robust, verifiable, and trustable autonomy - Both exploratory and confirmatory research with future naval application in the following research areas; Sea-based Aviation, Air Vehicle Sustainment, and Collaborative Autonomy. Initiate: - Developing new general foundational theory and methods for adversarial autonomy in naval domains

**FY2026 to FY2027 change.** The funding increase from FY 2026 to FY 2027 is due to an internal realignment of resources within PE 0601153N from Proj 1507 (Naval Aerospace) to Proj 1501 (Autonomy/AI) for research area prioritization on developing new general foundational theory and methods for adversarial autonomy in naval domains. Planned programs have been updated to align to current focus area. This is not a new start.

### Intelligent & Collaborative Agents — NEW START

**FY2027 planned work.** Machine Reasoning and Intelligence: - Continue developing the science base and computational methods for building versatile intelligent agents, which can function autonomously in uncertain, unstructured, uncontrolled, open-world environments, and can collaborate seamlessly with humans and other agents. - Continue basic research for developing robust computer vision systems, based on human vision, for automated understanding of surveillance imagery, perception for autonomous agents, and managing image/video libraries for after-action analysis and planning. - Continue basic research in machine self-learning for intelligent agents, inspired by human learning, for understanding real-world environments. - Continue basic research in learning and decision-making in multi-agent systems in dynamic, uncertain settings where there are many competitive and cooperative agents and information about intentions and rewards are not fully known. This research area has a wide range of applications in tactical and strategic planning, economic planning, etc. - Continue basic research in exploiting generative models, such as auto-encoders and diffusion models, to solve inverse problems. This research area has a wide range of applications in robot perception and automated understanding of surveillance imagery. - Continue basic research in developing rigorous mathematics, formal methods, and new analytical approaches for evaluating performance of deep generative models and predicting their performance. This research area has the potential for developing analytical tools for verification and validation of generative AI capabilities and for drastically reducing the need for empirical evaluation of such systems such that they can be deployed in safety critical applications. - Complete basic research in developing new mathematical methods for principled design of deep learning architectures and analysis of their behavior. This program is expected to develop techniques for predicting performance of learning-based systems, to improve their generalization abilities, and to reduce the need for empirical verification. -Initiate basic research in control and decision-making in networks of dynamic agents under uncertainties in perception and network models. This is of critical importance for robust and reliable prediction and control of network agents particularly in long-duration missions.

**FY2026 to FY2027 change.** The funding increase from FY 2026 to FY 2027 is due to an internal realignment of resources within PE 0601153N from Project Unit 1502: C5ISR/Naval Space to Project Unit 1501: Autonomy/AI in support of basic research in control and decision-making in networks of dynamic agents under uncertainties in perception and network models.

### Expeditionary Decision Superiority

**FY2026 to FY2027 change.** The decrease in funding from FY 2026 to FY 2027 is due to the realignment and consolidation of all expeditionary funding under PE 0601153N to the new Expeditionary Campaigns project number 1510 within this PE. Planned programs have been updated to align to current expeditionary and naval priorities.

**FY2026 plans — current year.** - Complete a focused research effort for discovery research on multi-class, multi-objective deep reinforced learning algorithms with automated training. (Expeditionary Warfare) - Complete research into deception, degradation, and manipulation of artificial intelligence algorithms in perception/action loops. (Expeditionary Warfare) - Complete research on understanding generalized theoretical foundations and limitations of predicting, deceiving, and disrupting artificial intelligence algorithms embedded in the perception/action loops of autonomous systems. (Expeditionary Warfare) - Complete effort to lay the theoretical and methodological foundations for detecting threats using social media data. (Expeditionary Warfare) - Complete research to understand the influence of mis/dis/mal information in social media. (Expeditionary Warfare) - Complete effort to understand how hate and a potential for violence or protest present themselves at a community level. (Expeditionary Warfare)

## What is NOT on this page

Congressional marks, the R-2 mission description and acquisition strategy, the industry vs government split of the whole request, and related program elements are recorded at **program-element** grain — an NDAA mark lands on a PE, never on a project. They are at https://hitchintel.com/programs/0601153N.

## Source & machine access

- **Source:** FY2027 Department of the Navy RDT&E Budget Justification, Exhibits R-2/R-2A/R-3, PE 0601153N project 1501 (PB PB2027).
- **MCP:** `mcp.hitchintel.com` — `budget_get_program_element(pe="0601153N")`.

*HitchAI is an independent intelligence service, not affiliated with the U.S. Department of Defense. Budget figures are requests/estimates, not obligations.*