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Human Research and Engineering Directorate Troy Kelley U.S. Army Research Laboratory Human Research and Engineering Directorate Aberdeen, MD USA How does.

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Presentation on theme: "Human Research and Engineering Directorate Troy Kelley U.S. Army Research Laboratory Human Research and Engineering Directorate Aberdeen, MD USA How does."— Presentation transcript:

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

2 Human Research and Engineering Directorate 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 Human Research and Engineering Directorate What is cognition? (cont) 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 Human Research and Engineering Directorate 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 Cant really create a black box Brain needs

5 Human Research and Engineering Directorate Approaches to needs Neurological Systems –Simulate every neuron Symbolic Systems –Traditional AI systems Complete sub-symbolic systems –Reactive architecture Cognitive Architectures –ACT-R, Soar

6 Human Research and Engineering Directorate Simulating Every Neuron Source: Dr. Ray Kurzweil, Kurzweil Technologies Approach – Neurological approach

7 Human Research and Engineering Directorate How does Blue Gene, todays 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 cycles per second Human Brain 4 lbs 0.06 cubic ft ????? 10 quadrillion cycles per second

8 Human Research and Engineering Directorate 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 cant use one type of perceptron or neural network

9 Human Research and Engineering Directorate 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 cant use one type of perceptron or neural network How do we determine the fitness of our cell clusters?

10 Human Research and Engineering Directorate 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 Human Research and Engineering Directorate 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 Human Research and Engineering Directorate 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 cant 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 Human Research and Engineering Directorate Sensor problem? The creature with the best sensor wins?

14 Human Research and Engineering Directorate 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 Human Research and Engineering Directorate Approaches Neurological Systems –Simulate every neuron Symbolic Systems –Traditional AI systems

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

17 Human Research and Engineering Directorate "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

18 Human Research and Engineering Directorate Approaches Neurological Systems –Simulate every neuron Symbolic Systems –Traditional AI systems Complete sub-symbolic systems –Reactive architecture

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

20 Human Research and Engineering Directorate Elephants Dont Play Chess – Rodney Brooks Approach – Reactive Architecture Humans do play chess, and perhaps we want to build robots that can play chess

21 Human Research and Engineering Directorate Approaches Neurological Systems –Simulate every neuron Symbolic Systems –Traditional AI systems Complete sub-symbolic systems –Reactive architecture Cognitive Architectures –ACT-R, Soar

22 Human Research and Engineering Directorate Cognitive Architectures Cognitive architectures have ignored the perceptual problem Cognitive architectures grew out of the symbolic tradition of AI Newell and Simons General Problem Solver production system served as the birth of AI as well as the birth of cognitive architectures Cognitive Architectures are complex

23 Human Research and Engineering Directorate 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 Human Research and Engineering Directorate Approaches Neurological 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 Human Research and Engineering Directorate Knowledge Architectures

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

27 Human Research and Engineering Directorate Intellectual continuum within 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 complex Symbolic 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.

28 Human Research and Engineering Directorate 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 Human Research and Engineering Directorate Stimuli Subsymbolic processing Production System Goals Camera inputs Laser inputs Sound inputs Parallel processing all of the inputs simultaneously Results go to memory Production system operates on memories Attention is the highest level goal Semantic network

30 Human Research and Engineering Directorate 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

31 Human Research and Engineering Directorate

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


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