human research and engineering directorate troy kelley u.s. army research laboratory human research...

Human Research and Engineering Directorate

Troy KelleyU.S. Army Research Laboratory

Human Research and Engineering DirectorateAberdeen, MD

USA

How does one design a mind?(In 4 billion years or less)


What is cognition?

• Cognition is a collection of pre-programmed algorithms developed during evolution– This is both high level

• Language• Searching

– And low level • Reflexes• Movement toward light


What is cognition? (con’t)

• Cognition is also changes in neurological connections based on experience – Learning at the low levels (reflexes) – And the high level as well (language)

• If I know what cognition is, does that mean I can recreate a cognitive system?


• At the very least a cognitive system needs:• Perceptual System

– Visual– Auditory– Tactile– SICK, IR, LADAR

• Memory System– LTM, STM, Working memory, visual spatial memory,

auditory memory (memory for each sensor?)• Hierarchical organization

– Some kind of hierarchical organization process• Can’t really create a “black box”

Brain needs


Approaches to needs• Neurological Systems

– Simulate every neuron• Symbolic Systems

– Traditional AI systems • Complete sub-symbolic systems

– Reactive architecture• Cognitive Architectures

– ACT-R, Soar


Simulating Every Neuron

Source: Dr. Ray Kurzweil, Kurzweil Technologies

Approach – Neurological approach


How does Blue Gene, today’s most powerful supercomputer, compare with the human brain?

*Data provided by Lawrence Livermore National Laboratory

Supercomputer and the Human Brain

Human brain is 100 times more powerful

Supercomputer

100,000 lbs

5,000 cubic ft

2,000,000 watts

100 trillion cyclesper second

Human Brain

4 lbs

0.06 cubic ft

?????

10 quadrillion cyclesper second


Approaches• Neurological Systems

– Simulate every neuron?• How do we program all of those neurons?

– Are they all basically the same or are they different?– We know from biological systems that different cells have

different functions even within the neurological system– So we can’t use one type of “perceptron” or neural network




– Are they all basically the same or are they different?

– We know from biological systems that different cells have different functions even within the neurological system

– So we can’t use one type of “perceptron” or neural network

• How do we determine the fitness of our cell clusters?


Charles Darwin Said….

“It is not the strongest of the species that survives..…but rather the one

most responsive to change.”

Adaptation in Nature is essential!


Evolutionary approach

• How to determine fitness?• Organisms evolved in conjunction with

the earth evolving• Evolving a complex organism needs to be

done using a complex environment!




– Are they all basically the same or are they different?

– We know from biological systems that different cells have different functions even within the neurological system

– So we can’t use one type of “perceptron” or neural network

• How do we determine the fitness of our cell clusters?• Much of evolution has revolved around motor/sensor

optimization – is that the answer for robotics?


Sensor problem?

• The creature with the best sensor wins?


Moth Sense and Control System• Biological sensors exhibit unequaled

sensitivity, specificity, speed and refresh-rate – The chemical sensors of the moth can

detect a single molecule of the sex pheromone of the female up to a mile away

[Bazan lab, ICB, UCSB][Bazan lab, ICB, UCSB]

Signal amplification mediated by elements that fit together by precise lock-and-key molecular recognition


Approaches

• Neurological Systems– Simulate every neuron

• Symbolic Systems– Traditional AI systems


AI Approach

• Computationally intensive

• Task specific

• Not necessarily biologically based

• Suffers from brittleness and lack of robust behaviour in dynamic environments


• "In from three to eight years, we'll have a machine with the general intelligence of an average human being... a machine that will be able to read Shakespeare [or] grease a car."

• Marvin Minsky, Life magazine, 1970

AI answer

Approach – Traditional AI approach


Approaches



• Complete sub-symbolic systems– Reactive architecture


Reactive Architecture

• Anti-symbolic

• Tight pairing between sensing and reaction

• Current system for the military (4DRCS)

• No representation of the environment


“Elephants Don’t Play Chess” – Rodney Brooks

Approach – Reactive Architecture

Humans do play chess, and perhaps we want to build robots that can play chess


Approaches




• Cognitive Architectures– ACT-R, Soar


Cognitive Architectures

• Cognitive architectures have ignored the “perceptual problem”

• Cognitive architectures grew out of the symbolic tradition of AI

• Newell and Simon’s General Problem Solver production system served as the birth of AI as well as the birth of cognitive architectures

• Cognitive Architectures are complex


ComplexityA software mind should be at least as complex as an

operating system?

– 1993 Windows NT 3.1 6 million lines of code– 1994 Windows NT 3.5 10 million lines of code– 1996 Windows NT 4.0 16 million lines of code– 2000 Windows 2000 29 million lines of code– 2002 Windows XP 40 million lines of code

• 40 million lines of code and 9 years of development• Imagine this development cycle, except that, due to sensor error,

you never knew exactly where the user was clicking with the mouse, or you never knew exactly what key was being selected on the keyboard. How would this affect the development cycle?



– Simulate every neuron



• Cognitive Architectures– ACT-R, Soar

• Hybrid approach• How do we merge a symbolic and sub-symbolic system?


Knowledge Architectures


Architectures for Modeling Cognition

X + Y = ZX + Y = Z

SymbolicComplex cognition

= Serial in natureLocalized representationCognitive Architectures

SubsymbolicSimple cognition

= Parallel in natureDistributed representationNeural Networks


Intellectual continuumwithin the human anatomy

Reflexes

The actions of reflexes are similar to a simple feed-forward Neural Network

Frontal Lobes

The actions of the Frontal Lobes are similar to complexSymbolic processing architectures

Kelley, T. D., (2003), “Symbolic and sub-symbolic representations in computational models of human cognition: What can be learned from biology?” Theory and Psychology, TAP 13(6), December.


Robotics Architectures

• In a DARPA report (2001) by Singh and Thayer of the CMU Robotics Institute the authors concluded that: – “a mixed strategy [hybrid] provides a more

reasonable method for robot coordination for a general case where there are natural constraints during operation in a complex environment.”


Stimuli

Subsymbolic processing

Production System

Goals

Camera inputsLaser inputsSound inputs

Parallel processingall of the inputssimultaneously

Results go to memory

Production systemoperates on memories

“Attention” is the highestlevel goal

Semantic network


Sub-symbolic• How to develop pre-programmed

algorithms that look for one item?– Algorithms for corners, gaps, lines

– Two programmers (graduate level) working for one year

– Still problems with these low level algorithms


Conclusions

• Complex behavior requires a complex approach to cognition

• Hybrid architectures offer one solution to a complex problem

• Combinations of symbolic and sub-symbolic architectures offer one approach

human research and engineering directorate troy kelley u.s. army research laboratory human research...

Documents

human brain human brain

neurological systems

second human brain

biological systems

second slide

soar slide

light slide

neural network slide