SBIR/STTR Research Award

Sigma as an Augmented Cognitive Architecture for Deep Space Exploration

Award identifier 7f02b66d7942b2aae3a0f7c5140edc860cbb4274becd13e77946d3bc645b0cd2Awarding agency National Aeronautics and Space Administration

Award amount

$124,870

Program and phase

SBIR · Phase I

Award year

2019

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Award details

Awarding agency
National Aeronautics and Space Administration
Branch
Not reported
Recipient company
NFLUX, INC.
Proposal award date
2019-08-19
Contract end date
2020-02-18
UEI
NSEUYFA4USV1
Solicitation number
SBIR_19_P1
Topic code
H6

Research abstract

nbsp;In recent years, many of the intelligent systems surpass human accuracy or speed in a specific narrow application or domain. However, most of these agents are only capable of a single task and incapable of generalizing or performing minimally intelligent in other tasks. For instance, agents that won a world championship against a human cannot drive a car, and agents that can play games arenrsquo;t capable of carrying out a conversation. The future of our species lies with exploration outside of our mother planet, and such interplanetary explorations will require intelligent systems that can interact and lower the workload of the crew. Herein, we propose Sigma (Sigma;) (Rosenbloom, Demski, amp; Ustun, 2016), a cognitive architecture and intelligent system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures and probabilistic graphical models via its graphical architecture hypothesis. Sigmarsquo;s development is driven by a combination of four desiderata: grand unification, generic cognition, functional elegance, and sufficient efficiency. In particular, Sigma leverages factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also critical non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory of Mind and are perceptual, autonomous, interactive, affective, and adaptive. Sigmarsquo;s graphical architecture has recently been extended to handle neural networks. Sigma has quite general parameter learning capabilities, in that probabilistic, neural, and reinforcement learning capabilities all emerge from a local gradient-descent-based learning mechanism operating at the code of the architecture. The aspiration for uniform grand unification, plus this unique blend of capabilities, make Sigma a well-equipped candidate to tackle the challenge of intelligent agents for deep space exploration.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Research funding history

4 SBIR/STTR records share this UEI (2019–2021). Reported award amounts total $349,552. Reported amounts are not federal obligations.

Phases: Phase I

Agencies: Department of Defense, National Aeronautics and Space Administration

Agency intelligence

National Aeronautics and Space Administration

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