1. How does one design a mind? (In 4 billion years or less)
Troy Kelley
U.S. Army Research Laboratory
Human Research and Engineering Directorate
Aberdeen, MD
USA

2. 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

3. 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?

4. Brain needs Hierarchical organization
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”

5. Approaches to needs Neurological Systems Symbolic Systems
Simulate every neuron
Symbolic Systems
Traditional AI systems
Complete sub-symbolic systems
Reactive architecture
Cognitive Architectures
ACT-R, Soar

6. Computing power of a mouse!
Simulating Every Neuron
Computing power of a mouse!
Source: Dr. Ray Kurzweil, Kurzweil Technologies
Approach – Neurological approach

7. Human brain is 100 times more powerful
Supercomputer and the Human Brain
How does Blue Gene, today’s most powerful supercomputer, compare with the human brain?
Supercomputer
100,000 lbs
5,000 cubic ft
2,000,000 watts
100 trillion cycles
per second
Human Brain
4 lbs
0.06 cubic ft
?????
10 quadrillion cycles
per second
Human brain is 100 times more powerful
*Data provided by Lawrence Livermore National Laboratory

8. 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

9. 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
How do we determine the fitness of our cell clusters?

10. Adaptation in Nature is essential!
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!

11. 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!

12. 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
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?

13. Sensor problem?
The creature with the best sensor wins?

14. 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]
Signal amplification mediated by elements that fit together by precise lock-and-key molecular recognition

15. Approaches Neurological Systems Symbolic Systems Simulate every neuron
Traditional AI systems

16. AI Approach Computationally intensive Task specific
Not necessarily biologically based
Suffers from brittleness and lack of robust behaviour in dynamic environments

17. AI answer
"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
Approach – Traditional AI approach

18. Approaches Neurological Systems Symbolic Systems
Simulate every neuron
Symbolic Systems
Traditional AI systems
Complete sub-symbolic systems
Reactive architecture

19. Reactive Architecture
Anti-symbolic
Tight pairing between sensing and reaction
Current system for the military (4DRCS)
No representation of the environment

20. Humans do play chess, and perhaps we want to build robots that
“Elephants Don’t Play Chess” – Rodney Brooks
Humans do play chess, and perhaps we want to build robots that
can play chess
Approach – Reactive Architecture

21. Approaches Neurological Systems Symbolic Systems
Simulate every neuron
Symbolic Systems
Traditional AI systems
Complete sub-symbolic systems
Reactive architecture
Cognitive Architectures
ACT-R, Soar

22. 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

23. Complexity
A software mind should be at least as complex as an operating system?
1993 Windows NT million lines of code
1994 Windows NT million lines of code
1996 Windows NT million lines of code
2000 Windows 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?

24. Approaches Neurological Systems Symbolic Systems
Simulate every neuron
Symbolic Systems
Traditional AI systems
Complete sub-symbolic systems
Reactive architecture
Cognitive Architectures
ACT-R, Soar
Hybrid approach
How do we merge a symbolic and sub-symbolic system?

25. Knowledge Architectures
Symbolic
Sub-symbolic

26. Architectures for Modeling Cognition
Symbolic
Complex cognition
= Serial in nature
Localized representation
Cognitive Architectures
X + Y = Z
Subsymbolic
Simple cognition
= Parallel in nature
Distributed representation
Neural Networks

27. Intellectual continuum within the human anatomy
Frontal Lobes
The actions of the Frontal Lobes are similar to complex
Symbolic processing architectures
Reflexes
The actions of reflexes are similar to a simple feed-forward
Neural Network
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.

28. 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.”

29. “Attention” is the highest
Goals
“Attention” is the highest
level goal
Symbolic
Production system
operates on memories
Production System
Results go to memory
Semantic network
Parallel processing
all of the inputs
simultaneously
Subsymbolic
Subsymbolic processing
Camera inputs
Laser inputs
Sound inputs
SS-RICS
Stimuli

30. 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

32. 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