Download A. Introduction to Real Intelligence

A. Introduction to Real Intelligence
The following sections are presented as dipoles designed to help us to think about various issues. (Could
replicate these for the AI section… Align?).
Why Computing has had a history of association with Cognitive Science and AI.
Memory and Forgetting
Mailing a letter to a friend is communicating information. Mailing a letter to yourself is storing information.
EEEPROM vs LTM
DRAM vs STM?
Neural Nets:
STM via recurrence
LTM via synaptic modification.
The need to forget. Synaptic pruning.
7 items in STM: Large number experiment. List of names experiment. Telephone numbers, why 6 digit plus 4
digit area code plus 2 digit country code. Telephone numbers for the Solar system, how many digits for the
“planetary code” ?
Workshop:
Nottingham online experiment.
Knowledge. We have an intuitive knowledge of the extent of our knowledge. We can call this “metaknowledge”. For example we “know” if we “don’t know” a fact. Also, the “tip-of-the-tongue” phenomenon,
where we know we know something but we cannot recall it.
Information and Data
“How much can be compress a page of text without losing any information?” Here text is data so what is
information ? Use a short discussion of compression (methods) to explore the meaning of the word
“information”.
Shannon S = k log(N)
Information is the “removal of uncertainty”
Brain and Mind
Location of the Mind. Popper and Eccles.
Structure (neuroanatomy) of the brain. Major areas. Human Visual System in Detail. Walking.
Neurophysiology of the brain: Neurons and synapses.
Thinking and Believing
Rational – Irrational – “Being in love”
Intelligence and (Reaction) / (Expertism)
IQ Tests
Bongard Problems
Gardner
Reactive Systems
What does it mean “to be an expert” ?
Learning and Reacting
Our reactive behaviour: Breathing. Walking? Horses – gaits.
Learning theories
Dialectic Aristotle.
Perception and Blindness
Smelling Books
B. Introduction to Artificial Intelligence
Representation of Knowledge
Semantic Nets
Concept Maps
What is knowledge? = facts + beliefs + heuristics (Shapiro)
Reasoning
Rule-based Systems Prolog? Yes! (Shapiro) Depth-first backward chaining.
Expert Systems (Prolog – R&N p289)
MYCIN
Chess
How can these programs explain their reasoning.
Learning Systems
Artificial Neural Networks
Hardware
Silicon Neurons
Biological Neurons on Silica (in Silicio)
Tests for Artificial Intelligence
Turing Test. Set up using Unreal + Headphones
Searle’s “Chinese Room” Test
Historical
Japanese 5th Generation
Moravec Growth Curves.
Robotics
Subsumption and AGVs
An Historical Approach
EPOCH
PERSONALITIES APPROACHES
Gestation (1943-1955)
McCulloch and
Pitts
Birth (1956)
Teenager Enthusiasm,
Great Expectations
(1952-1969)
Physiology and function of
neurons in our brains.
Analysis of Russell’s logic
and Turing’s theory of
Computation
Donald Hebb
How brains learn
Minsky & Edmonds SNARC – First neural
computer (1950)
Alan Turing
1950 paper “Computing
Machinery and Intelligence”
John McCarthy
Newell & Simon
Automata, neural nets,
human intelligence
“Logical Theorist”
Newell & Simon
“General Problem Solver”
Newell & Simon
Physical symbol system
hypothesis
Coined the term “Artificial
Intelligence”
Invented “Lisp”
McCarthy
McCarthy (1958)
Marvin Minsky
Winograd
Rosenblatt
Microworlds
SHRDLU
Neural Networks
“Perceptrons”
Hopfield,
Rumelhart, Hinton
Back-propagation. Parallel
Distributed Processing.
THIS MODULE
Applet
Paper here.
Paper Programs with
Common Sense here.
Applet/programme
Applet
Reality Sets In (19661973)
Knowledge-based
Systems: A hope for
solution (1969-1979)
Into Industry (1980present)
Return of Neural
Networks (1986present)
Applet
A Science of AI? (1987present)
The Emergence of
Artificial Agents (1995present)
Allen Newell, John
Laird, Paul
Rosenbloom
Price, Moore
SOAR
Coupling of SOAR and UT
Integration of AI and UT
GPS Demo.
A child is given a load of black and white blocks and is asked to arrange them in the alternating sequence
bwbwbw…
The child must use only the following rules :
1
2
3
4
-
Two black blocks can
Two white blocks can
A black block can be
A white block can be
be added adjacent to the rightmost block.
be added adjacent to the rightmost block.
removed from the right.
removed from the right.
GPS Approach:
Transform the initial state to the goal state as follows :
(Step 1) Select the operator that will reduce the difference between the initial
state, S1, and the goal state the most .
(Step 2) Apply the operator to the initial state giving a new state, S2
(Step 3)If the new state, S2, is identical to the goal, we are done; otherwise,
repeat the "Transform” process, using S2 as the new initial state.
Application to the blocks problem
Goal state is b w b w b w b w
Initial state is b
Rules:
1
2
3
4
-
<pattern>  <pattern>bb
<pattern>  <pattern>ww
<pattern>b  <pattern>
<pattern>w  <pattern>
add two black boxes
add two white boxes
remove black box
remove white box
Demo
But, in fact, a more sophisticated approach would be to remember that from b we
got bwb and that therefore we could add the rule :
5 - (pattern)b  (pattern)bwb
We have learned something ! Thus, once we have reached bwb after three applications of the procedure, our
next application of the transform algorithm is :
bwbwb (rule 5)
We can apply the same rule again
bwbwbwb (rule 5 )
which gives us our goal state more rapidly.
Resources
Applet for breadth – depth search
http://www.cs.rmit.edu.au/AI-Search/Product/
http://www.cs.mcgill.ca/~dprecup/courses/AI/lectures.html
A* with heuristics
http://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html#S1
Pathfinding in general inc A*
http://theory.stanford.edu/~amitp/GameProgramming/AStarComparison.html#S3
Eliza test
http://www.chayden.net/eliza/Eliza.html