=Paper=
{{Paper
|id=Vol-2819/invited4
|storemode=property
|title=The Holistic Battlespace: Why the Key to Resilience for AI/ML Algorithms is to Leverage Complexity Science (Presentation slides)
|pdfUrl=https://ceur-ws.org/Vol-2819/invited4.pdf
|volume=Vol-2819
|authors=Joe Schaff
}}
==The Holistic Battlespace: Why the Key to Resilience for AI/ML Algorithms is to Leverage Complexity Science (Presentation slides)==
https://ceur-ws.org/Vol-2819/invited4.pdf
“The Holistic Battlespace: Why the
Key to Resilience for AI/ML
Algorithms is to Leverage
Complexity Science”.
By Dr. Joe Schaff
Autonomy & Avionics, NAVAIR / NAWCAD Mission Systems
This will certify that all author(s) of the above article/paper are employees of the U.S. Government and performed this work as part of their employment,
and that the article/paper is therefore not subject to U.S. copyright protection. No copyright. Use permitted under Creative Commons License Attribution
4.0 International (CC BY 4.0). In: Proceedings of AAAI Symposium on the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications:
Potentials, Theories, Practices, Tools, and Risks, November 11-12, 2020, Virtual, published at http://ceur-ws.org
Disclaimer: The work and related approaches in these slides are the Joe Schaff, NAVAIR / NAWCAD Mission Systems
opinions of the author, and do not reflect any policy, methods or
approaches used by the US government. DISTRIBUTION STATEMENT A
What is the Battlespace?
• A multidomain operating environment, much like a natural ecosystem.
• To be effective, the dominant force must leverage the environment to:
1. Exploit the weaknesses of an adversary’s environmental dependencies.
2. Strengthen the dominant position by protecting key environmental factors.
• Currently, a battlespace consists of a heterogeneous mix of humans and
machines, some with intelligent autonomous systems.
• Looking forward, the majority will be intelligent autonomy.
• Either of these will have a dependency on the judicious use of information –
there will not be complete, but only limited data.
• To win, a dominant force needs to have awareness of its general objectives,
the force laydown of both sides and any significant changes that may occur.
• Information can and should be communicated in a narrow channel as nature
does – i.e. stigmergy.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Complexity Science – Roles of Scale &
Emergence
• Ecosystem- or Battlespace-sized interactions will by default have unexpected
(emergent) behaviors.
• Intelligent autonomous systems (or Complex Adaptive Systems = CAS) will
need to rapidly learn and adapt to their dynamically changing environment.
Effective learning must occur with limited experiences.
• Below is a list of some key issues with ML in general:
1. The need for adequate (i.e. massive) number of samples for comprehensive
training.
2. Long time scales for adaptive learning, partially due to massive sample size.
3. Large computational resources needed for training.
4. Brittleness due to lack of resilience, emergent misclassifications, and overfitting.
• Most of these are significantly different from human limitations. Let’s look
at the holistic picture to see how we can address some of these:
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Adressing the Autonomous Battlespace
Problem from Both Ends
A “Smart” Battlespace consists of many thousands of elements, each comprised of
smart components:
1. Massive embedded mobile ad-hoc (MANET) radios create the “smart swarm”.
• Both humans and machines, referred to as “entities” communicate = interactions.
• Entities are heterogeneous and need to self-organize and be cognizant of order.
• Mathematically equivalent problem whether you assume either radios or UAVs.
2. Entities each can consist of one or more components.
• Components need to be resilient to attacks – i.e. self-healing and resistant.
• Components are “smart components” that embed AI / ML to augment sensor and route
planning capabilities. World model is the abstract “awareness”.
• What does ETE look like at different scales (1 & 2 above)?
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Complexity is Integral to Battlespace
• Battlespace by necessity must be complex.
• Attempts to over-simplify result in easily targetable entities.
• Emergent behaviors will occur whether you want them
or not.
• Best choice: “when you can’t beat ‘em, join ‘em”:
• leverage these behaviors to produce tactical advantages.
• Use these to create self-healing resilient networks.
• Use the “creativity” that can emerge from nonlinear
classifiers in AI.
• Choose wisely where you use emergent aspects of
complexity, how you apply AI.
• Constrain other systems / components as needed to
make best use – e.g. formal methods.
• Be the “lion tamer” of complexity to gain winning
tactical advantages.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Complexity of Scale: From Swarms to
Components
(Red = degree of complexity being used)
Massive Platform
Platforms Components
Swarms (use of AI/ML)
Swarm Cloud (10,000’s objects) Platform Component Architecture
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Technologies of Scale Must Overlap
Swarm
Components
Platforms
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Technology Overlap: #1 – Massive Smart Swarm:
Self-organizing mathematics = uses ”deterministic chaos”
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
From Random to Order
Video: https://youtu.be/iggsygNPEnU Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
How Can This Possibly Work?
• Randomly generated, but constrained topology.
• Does translation / rotation (mathematically = affine transformation).
• Implicitly self-similar.
• Computationally simple math
• iterations (Iterated Function System = IFS).
• In this particular function only one float multiplication per iteration: e.g. for
determining the topological layout of 10,000 entities, would be 10KFLOPs.
• Any IoT / edge device would have computational power to get topological
picture of battlespace / other in milliseconds or faster (e.g. ESP32 = 400µsec).
• So, what do we do with this? Distributed C2 / Resilient comms in
denied environments? Control massive swarms?
How Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Human Immersion
into Battlespace:
1) Put on Oculus / other
headset
2) Link controls (BCI /
other) to one of the UxVs
in proximity circle.
3) Pass token to first one to
respond / arbitrary
choice.
4) View what it “sees”, and
fly in its “world”.
5) Handoff token when
done / other location
needed.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
OK, but what is it??? DISTRIBUTION STATEMENT A
It’s a Fractal!
Joe Schaff, NAVAIR /
NAWCAD Mission
Further details can be found in the chapter I wrote (Leveraging Deterministic Chaos to Systems
Mitigate Combinatorial Explosions) for the book “Engineering Emergence: A Modeling DISTRIBUTION
and Simulation Approach”, CRC Press ⓒ2019. STATEMENT A
Technology Overlap #2 Components - Resolving
Trust:
Architecture for cyber-hardened smart components to learn &
adapt while creating a greater trust in their autonomous
decisions
• Trust and resilience go
hand-in-hand.
• Must merge Cyber and Cyber Resilience &
A.I. holistically. the desired
• Must allow free-reign of attributes of
A.I. (i.e. creativity) but behaviors
use effective resiliency A.I. creates trust.
constraints.
• Meta-reasoning to Patent disclosure was
prevent A.I. algorithms submitted and
from being deceived. Autonomy
presented to Invention
Evaluation Board
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Adversarial AI: Natural Adversarial
Examples*
• Natural adversarial examples from IMAGENET-A. The red text is a ResNet-50
prediction with its confidence, and the black text is the actual class.
* from: arXiv:1907.07174v2 [cs.LG] 18 Jul 2019
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
How do we avoid some of these issues?
• We may never be able to design “foolproof” resilience into a system.
• There are good strategies to limit some of the weaknesses in AI/ML.
• Some aspects of transfer learning – IF the data is “clean” to begin
with: “An Empirical Evaluation of Adversarial Robustness under
Transfer Learning”
• Others may not be avoidable if data is “poisoned”. See: Poison frogs.
• First steps: Architecting trustworthy resilience and validating these
architectures
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Architecting Resilience – some “Puzzle Pieces”
DARPA started the Assured Autonomy program.
• This program looks at the methods for some AI / ML validation, but does not
look at the battlespace “Big Picture”.
• Early stage - Focused on AI/ML specifically.
• Funding academic research for verifying /validating performance aspects of
primarily NNs
• Example:
• VerifAI/SCENIC = toolkit for design/analysis of AI systems (SCENIC=probabilistic
programming language). D. Fremont, et.al, UCal Berkeley.
• Study uses Grand Theft Auto 5 (GTA5).
• Download software here: https://github.com/BerkeleyLearnVerify/VerifAI
• Many more examples available from other schools.
• Formal Methods Approaches are frequently used.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Formal Methods for Trust(?)…but it doesn’t Scale
well…
Can it work with “smart components”?
• Sometimes- complexity may rule it out
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Component Architecture Background
*Architectures have been designed in the past that address some
but not all of these. Below are some of the attributes of the
proposed architectural approach:
• 1. Is able to use heterogeneous AI/ML technologies.
• 2. Mitigates shortfalls in specific vision/other algorithms.
• 3. Does meta-reasoning (cognitive architecture).
• 4. Is Cyber-resilient.
• 5. Is fully scalable from low-cost expendable to high value platform.
• 6. Has a fully open architecture in hardware and software.
• 7. Allows exploration of algorithm internals for AI/ML and cyber analysis.
• * Note: “architecture” is clearly an overloaded word - if you don’t like the
word “architecture”, replace it with “framework”.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Quick Fix for Minimal Data and “basic” Rapid
Learning
• What about DLNN issues:
1. adequate (i.e. massive) number of samples for comprehensive training?
2. Short time scale for adaptive learning?
• Transfer learning: take the trained weights / other parameters for similar
NN trained on similar problem, load into new NN.
• Issues include: is the problem domain sufficiently similar? Does this limit the item
classified to only those close / exact enough to original training data (i.e.
overfitting)?
• Better way: Use “helper” algorithms and mathematical functions as coarse
classifiers to “pre-train” the DLNN.
• Helper algorithms can work in a complementary manner with algorithms that are
more accurate but challenging to train / adapt.
• More than just ensemble classifiers = these are matched complementary sets. The
sets can also be combined with other classifiers for an ensemble.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Example Helper Algorithm
• Can be solved by
incorporating earlier
AI/ML paradigms
into architecture.
• One simple example
is semantic net:
members of a class
and attributes are
shown by connected
graph.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
But What if We Don’t Know the Categories?
A) what if we don’t know categories or relationships? B) what if the problem space is
nonlinear?
• Example #2: Radial Basis Function (RBF) NN is
an “analogizer” = it can estimate approximately
which class something fits into, even if classes
are not yet defined (unsupervised learning = 1st
stage), then follows with a few good examples
(2nd stage).
• RBFs and some SVMs (Support Vector Machines)
can create categories. RBF also can address
many nonlinear problems, e.g. chaotic time
series. Convergence to control dynamics or
create classes to recognize can be done with < Thing 1, Thing 2, and Swamp Thing
100 examples.
A classic complexity / chaos example is given = logistics equation.
J. Moody and C. J. Darken, "Fast learning in networks of locally tuned processing units," Neural Computation,1, 281-294 (1989).
Joe Schaff, NAVAIR / NAWCAD Mission Systems DISTRIBUTION STATEMENT A
Radial basis function network: Control of
the logistic map.
The system is allowed to evolve
naturally for 49 time steps. At time
50 control is turned on. The desired
trajectory for the time series is red.
The system under control learns
the underlying dynamics and
drives the time series to the
desired output.
Computationally simpler & faster
than DLNN – just not as exact.
Permission details
This work has been released into the public domain by its
author, CommodiCast at English Wikipedia. This applies
worldwide. In some countries this may not be legally
possible; if so: CommodiCast grants anyone the right to use
this work for any purpose, without any conditions, unless
such conditions are required by law.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Helper Function (mathematical type)
• Around mid-1990s I noticed that complex, almost random behaviors of NNs
had some implicit pattern but could not figure it out.
• Looked at weights before, during, after training. Noticed self-similar pattern
(fractal) for adjacent weights and respective inputs.
• Hypothesis: if the fractal pattern of trained weights is saved, then
transformed & applied to similar NN topologies, this will shorten the
training time & data needed.
• What about overfitting to exact fractal parameters? Solution is to use multifractal
= superimpose another similar or possibly different fractal onto original (similar
techniques are used for wave functions in quantum mechanics).
• What if I can determine the inverse of the fractal functions? Superimpose that to
“undo” any learning. (multifractal link:
https://imagej.nih.gov/ij/plugins/fraclac/FLHelp/Multifractals.htm )
Now…put it all together to build a cyber-resilient architecture. Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Running Algorithms in a Meta-reasoning
Component Architecture (an example)
*
Comparison
/ selection Yolo : MXNET: CMU-develo A) Separately
(1-shot net with ped
of multiple convolutional
Containerized
Cloud stochastic
vision NN) sharing hue-based algorithms.
B) Runs on
classifiers
Microkernel.
Classifier selection:
1) Manually selected The Meta-Reasoner:
2) Use Options include: (1) Use OpenCBR case-based reasoner, or (2) Cognitive
Meta-reasoner on architecture. This overlays the Machine Learning algorithms, and each
embedded algo. Is treated as an agent:
processor (Pi) E.g. SOAR (Java version = JSOAR) architecture selected for initial
approach. JSOAR / SOAR is open-source.
Finally, Use a Generative Adversarial Network (GAN) for periodic
learning updates. DISTRIBUTION A
UNCLASSIFIED
*Embedded Helper Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
The Reason for Meta-Reasoning (“adult supervision”):
Detecting Deep Fakes and Adversarial Perturbations1
Misclassifications (noise pattern is already embedded):
African grey Macaw Indian elephant Three-toed sloth
Some embedded noise patterns for different classifiers:
1. Extracted from: “Universal adversarial perturbations”, S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, P. Frossard;
arXiv:1610.08401v1 [cs.CV] 26 Oct 2016.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Build a Scalable Prototype for ML & Cyber, and
Future Advanced Threats.
Prototype for
Real-time Convolutional Neural Networks for expendable
Emotion and Gender Classification (academic pub.) robot with
deep-learning
vision / object
recognition.
Every row starting from the top corresponds respectively to
the emotions {e.g. “angry”, “happy”, “sad”, “surprise”, …}
Both left & right blocks represent same pictures. Cost:
Right=convolved using backpropagation variant algorithm. <$300.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
From Prototype to Production: Overlaying a
Technology Transition Architecture
Even if the ETE architecture is incomplete, now is the time to design a
“universal” production system designed for adaptation and validation.
Questions to be asked:
1. Is the research current state of the art?
2. Who is doing various parts of this research?
3. How do we avoid the “valley of death” common to research transition?
4. Can information flow effectively to / from researchers and customers?
5. What conduits exist for resilient & consistent software to transition to
customer use cases?
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Pipeline Architecture & Taxonomy Connections:
Enhanced DevSecOps = researchers, tools, taxonomy conduits, secure
containers.
Prototype ready
for customer
Customer Use
Taxonomy Cases:
conduits Connected via
automated UML
generated from
selected software
containers
Researchers Secure Joe Schaff, NAVAIR / NAWCAD Mission Systems
containers DISTRIBUTION STATEMENT A
Issues and What’s Next?
The “big picture” is currently incomplete:
• Segments of the ETE architecture exist, satisfy some gaps.
• Other gaps exist: both known and unknown.
• Where does complexity provide advantages? Where are deterministic
solutions better?
• Must work in a multi-domain battlespace – the two ends (swarm,
components) are designed specifically for that.
• What organizations can address the “big picture”?
• Now at critical junction for MUmT and autonomy – incomplete/delayed
response could put us too far behind adversaries to catch up.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Final Assessment
On-going work – things I am doing so far:
• Developed a class of algorithms that manage massive “smart” swarms:
• Similar approach to ecosystems in nature, “stigmergic” communication.
• Leverages “swarm intelligence” = AI, so that any entity “knows” where the others are positioned, as
well as changes when broadcasted.
• Needs only a few bytes of data to reorganize / know relative positioning of all battlespace entities.
• Trivial math – e.g. raspberry Pi can calculate 10,000+ entities positions & dynamics in less than
100µsec.
• Developed the resilient meta-reasoning architecture for components:
• Uses heterogeneous AI / ML algorithms in a complementary manner = weakness of one type of
algorithm is covered by another, + helper functions for learning as needed. Scales from raspberry Pi to
largest available.
• AI algorithms are given free reign in a ”sandboxed” environment to allow the full creativity or
innovative results for most effective tactical decisions.
• Meta-reasoner is the “rationalizer” or “adult supervision” that decides whether an algorithm has been
deceived, choosing another algorithm’s results if needed. Periodically, meta-reasoner learns and
adapts.
• Ongoing collaboration with NASA LaRC Formal Methods laboratory.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
• Ongoing collaboration with academia, DARPA Assured Autonomy, OFFSET programs.
DISTRIBUTION STATEMENT A
• Tech & taxonomy architecture for transition.
BACKUP SLIDES
The Details…
Adversarial AI
Adversarial AI Malware
1. Extracted from Hu and Tan: “Generating Adversarial Malware Examples for
Black-Box Attacks Based on GAN”
• Works even when attackers have no access to the architecture and weights of the
neural network to be attacked.
2. Extracted from paper by UMD researchers: “Poison Frogs! Targeted
Clean-Label Poisoning Attacks on Neural Networks ”
• Data poisoning = attack on machine learning (ML).
• Attacker adds examples to training set to manipulate the behavior of the model.
• Targeted to control the behavior of the classifier on a specific test instance without
degrading overall classifier performance.
• Attacker adds a seemingly innocuous image (that is properly labeled) to a training set
for face recognition, and control the identity of a chosen person.
• Poisons could be entered into the training set simply by leaving them on the web and
waiting for them to be scraped by a data collection bot.
3. Images in nature can confound machines.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Machine Deconstruction:
Deconvolutional Network for Face
Decomposition
• Top-down parts-based image
decomposition with an
adaptive deconvolutional
network. Each column
corresponds to a different
input image under the same
model.
• low-level edges, mid-level
edge junctions, high-level
object parts and complete
objects
{extracted from: Zeiler, Taylor,
Fergus; “Adaptive
Deconvolutional Networks for
Mid and High Level Feature
Learning”}
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Constructing a Deep Fake
Several methods to construct deep fakes – some use Generative Adversarial
Networks (GANs), other methods for deconstruct / reconstruct facial features.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Cyber Resilience
Understanding Differences Between
Cyber - {Security} and {Resilience}
Security:
1) Preserving data ”at rest” and in-transit.
2) Privacy = encryption, least-privilege access.
3) Securing system against external attack – hostile takeover,
network-based attacks, etc.
Resilience:
1) More AI / ML based problems.
2) Resilient to deception / misclassification.
3) Resilient to noise added to data.
4) Recovery from exploitation of known weaknesses in classifiers.
5) Recovery from unanticipated attacks.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Steps 1 and 2: Cyber-secure Kernel, Linux Containers
1) Use a microkernel OS = Example: Fuchsia (by Google – in
development).
a) Based on a new microkernel called "Zircon” secure computing environment.
b) Similar approach used by DARPA High Assurance Cyber Military Systems
(HACMS) program.
2) Use Linux Containers (e.g. “Docker”)
a) Why?
1. It “sandboxes” unstable or vulnerable, yet useful ML algorithms.
2. Sandbox can re-instantiate the algorithm if it “crashes” due to malicious attack or instability.
3. Allows full creativity or “emergent behaviors” of algorithms.
b) Overhead and stability costs?
a) Almost identical to bare metal or native ML application without sandboxing.
b) If container crashes, then microkernel restarts container app with “sandboxed” algorithm.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Pipeline Architecture: R&D to customer
Conduits
Pipeline Architecture:
A Multi-pronged Approach
1. Foundation: create developer pipelines, i.e. - remove any burden
of operations so that researchers concentrate on research.
2. Latest technology advances from all available sources = follow
the taxonomy tree.
3. Identify gaps and unfulfilled needs = where to invest in the
research effort.
4. Map use cases to UML / MBSE language abstraction of software,
for transition pipeline.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
OSD DevSecOps & more?
Containers help
visibility and
● Pipelines to / from developers. sharability of
products.
● Hardened containers for algorithms or other software components.
● MilCloud based = latest research in AI/ML may be shared with other researchers.
● BUT...this pipeline is not enough. Need to insert taxonomy... Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Taxonomy (with technology conduits to domain
experts) Additional labs
(e.g. NAWCAD,
NIWC, DHS)
Additional algorithms (i.e. ant
colony optimization or nature
inspired algorithms with the
swarms, LDA, PCA)
Joe Schaff, NAVAIR /
NAWCAD Mission
add other Systems
architectures
(CNNs)
DISTRIBUTION
STATEMENT A
Pipeline Architecture & Taxonomy Connections:
Enhanced DevSecOps = researchers, tools, taxonomy conduits, secure
containers.
Prototype ready
for customer
Customer Use
Taxonomy Cases:
conduits Connected via
automated UML
generated from
selected software
containers
Researchers Secure Joe Schaff, NAVAIR / NAWCAD Mission Systems
containers DISTRIBUTION STATEMENT A
Human-Robot Interaction Course
(I designed & teach this @ U. of Maryland)
Course Outline1
• Course will cover topics as diverse as the technology for biologically
inspired robots, cognitive robotics, cultural, social and legal aspects of
robotics, data mining, examples of human systems interfacing, machine
learning principles and their limitations with respect to AI.
• Your objective as a student will be to integrate this interdisciplinary
knowledge and perform out of the box thinking, demonstrating this in a
term project.
• We're going to look at the ideas like robot emotion, and collaborative
robots that can form limited social interactions.
• You will design a robot that can implicitly determine the action it needs to
take without explicit commands given to it, by observing its interaction with
people.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A
Course Outline 2
• The term project: Think of creating a Kickstarter where you will be building
the next generation of cognitive human-behaving robots.
• You need to show your product as something investors would buy into.
• I will provide course material and extensive reference sources for both
hardware and software to design these robots.
• These robots could realistically be built with hardware and software for as
little as $2000.
• The Kickstarter is only a goal to shoot for, and if you indeed want to create
an actual one after the course is over, you are encouraged to do so either
alone or in collaboration with others in your class.
• Unlike an actual Kickstarter, there's no penalty for not being sponsored - if
you try and think out of the box, and apply whatever knowledge you're
capable of finding as well as what I will provide, you will succeed.
Joe Schaff, NAVAIR / NAWCAD Mission Systems
DISTRIBUTION STATEMENT A