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C00002 00002		Evans stuff
C00006 00003		Review of Fodor's article in January 81 "Scientific American".
C00012 00004		Critical comments on HEURS paper
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	Evans stuff
Early 1960s - reported in SIP by Minsky

First program to use Analogy (explicitly) 

Task: IQ type A:B :: C:?, where ? one of 5 figures (P 273)

Process:  290-294
 ---- Part 1 ----
  1) Describe all Figures (A-C, 1-5)
	Finds properties, relations of objects in each figure
  2) Find transformations: A→B, C→i ∀i; 
	also A→C, B→C, & A→i, B→i ∀i
	This object-in-1 is like object-in-2, when rotated, scaled, ...
 ---- Part 2 ----
  3) a) Find mapping from A→B, consistent with relations (variabulize)
	[MATCH, ADD, REMOVE objects]
     b) Find mapping from C→i, consistent with relations
  4) Try A→B rule on C; to see if get i.
  5) Iteratively weaken A→B xform (by eliminating conjuncts) 
	until corresponds to unique C→j.
	This is least weak, and j is answer.

Comments:
	General:
  No looping back to description - used seperate modules (IBM cards)
  Form required, but actual relations arbitrary.
	Specific:
  re: 1) - Based on fixed language,
	   Positive properties
	   Really 0th step, for decompostion.
  re: 3) - Non recursive - either equal or not
	   Conjunct of properties
  re: 4) - An optimization:
	3.5) Throw out C→i if #'s don't match.
		If all gone, relax.
  re: 5) - Order heuristic - to correspond to humans
	   If non-unique, try again.

Weaknesses:
  Only conjuncts of positive traits
	[Eg not No object of A is = object of B]
  Unable to recur - objects either EQUAL or not.
  Unable to adapt - ie heuristics used built-into code
	[Eg another order of attempts, how to weaken conjunct]
  Unable to return to decomposing step.
  Limited in scope to discriminary - 
	Able to find best from sample, not "good".
 In summary: totally syntactic - no world knowledge, just other smarts.

Strengths:
  Did work - over large case
  Knew some of these weaknesses - esp 4.
  Arbitrary relations (for part 2, eg shading)
  Some good optimizations

Final comments:
1. This first big LISP program - justifies PLISTs, etc.
2. Granddaddy of most ANALOGY programs
	Worked symbolically.
	Well (objectively) described. 
	  - of problem, future directions and implementation.
	Review of Fodor's article in January 81 "Scientific American".
"The Mind-Body Problem"

Overhead
  Fodor's article in January 81 "Scientific American".
  	(After overviewing this article, I'll discuss its content)
  Prof @ MIT in Psychology & Philosophy/Linguistics

Motivation - Philosophical article 
  Mentioned AI in intro
  I never understood dualism vs functionalism...,
	& these seemed relevant to discussions re: intelligent machines/people

Organization:
	Intro
	Describes & Critiques Prior Explanations
	The answer - functionalism
	Futher issues of Mind
(And I'll just give quick paraphrase of this, in order.
 See diagram)

	Content
-------------------------------------------------------
-------------------------------------------------------

History
	Philosophy recent interest in explaining Psychological states
	Is mind material, or etherial?
  Dualist vs Materialists [Behavior & Identity]
			    ↑ Watson
Dualist: Mind is non-physical
  Problem: Violation of physical laws (conservation...)
	Mind-body causality
	Fallen from favor for other reasons as well.

Radical Behaviorist:
  Character:
	Only stimulus/response
	Role of Psy is catalog S/Rs.
  Plus:
	Better than ghosts
  Problem:
	No talk of mental causes/states, ...

Logical Behaviorist: Provides semantics to mental states:
  Character:
	Equates mental states with behavioral disposition
	Every mental ascription ≡ (in meaning) to  If/Then rule
	Translates mental language into language of S/R
  Plus:
	Provides a materialistic account of mental causation
  Problem:
	Insists there are NO mental causes
	  Does not account for all interactions [between of mental states] 116
	Does not allow abstraction
	  Requires open-ended # behavior hypotheticals
	   to spell out the behavior dispositions expressed by mental term

Central-State Identity Theorist: 
  Character:
	Mental causes ≡ neurophysiological events in brain
  Plus:
	Can have totally interal interactions [NOT leading to behaviour]
	So mental processes really physical
  Problem:
	Need abstraction - above level of neuron!
	Hardware based - how about Software?

Functionalist - from Cognitive Science
  Character:
	Not what stuff is made of, but how arranged.
	View of information processor - with states...
		Stuff comes in, states change, stuff goes out.
  Plus:
	Compatible with best of LB & C-SIT, but independent of material
		(and so extendable to other systems)
  Problem:
	Not untruth, just triviality
	  [If states only functionally defined, then like Homonculi ]
		Answer: must suggest mechanism itself
	Here Turing machine (well defined computation on discrete symbols)

---------------------------------------------
---------------------------------------------
Questions re: Functionalism - 
1) Not limited to minds
2) Qualitative vs Quantitative Content [inverted spectrum - green/red]
	- if functionally the same, how to distinguish?
3) Handling of intentional content of mental states
	(propositions) - Functionalism has done well here.
	Basically: Symbols (they also have intentional content)
4) Issues of representation(al theory of the mind)
	Resemblence, and that set of problems ("tall"ness of John)
	Here Functionalism helps: semantics depending on function
		(↑ This seems central issue to empirical theories of the mind)

------------------------------------------------
------------------------------------------------

Token Physicalism: All mental particular WHICH HAPPEN TO EXIST are
		 neurophysiological.
	Type Physicalism:  The only mental particulars must be neurophysiological.
   Hence Type P. dismisses machines & disembodies spirits, as no neurons
	Critical comments on HEURS paper
∂TO DBL 6-Oct-80
I already mentioned the claring lack of a solid example, with which the
reader can begin to understand these axes.  This might be a tricky task,
as I'm not convinced your diagram will work, for the following reasons:
[I'm not saying it wouldn't; only that you haven't provided a proof of the
following essential points:]

1) Yes, you did justify that for most tasks, any given heuristic will have
a small negative value -- corresponding to the time to evaluate the IF clause.
[I would claim that value is not as negative as one might think, as the fact that
H#37 knows it doesn't apply to Task#44 is itself useful information, which a
meta-level observer might use to great advantage.]

2) Yes, I accept the premise that if the IF part of a heuristic is mis-tuned
then the user will lose by applying that rule -- and so the utility will be
negative, and perhaps fairly large.

3) I do believe that SIMILIAR heuristics will apply to similiar tasks. It
requires a small leap of faith to accept the premise that one could apply
the SAME rule-of-thumb to more than one task, and in more than one domain.
This essential presumption could be established by claiming (as we have)
that each rule is really a fairly general entity, which may be passed 
a set of parameters which fits the general
principle to the specific case. (See UNITS Spec relation.)
Notice an important side-effect springs from the idea of "passing the
domain specifics" as an argument:
This means it is meaningful to discuss a Task axis, as done in the paper,
and helps define it as well; or at least provide
a language in which such a definition can be stated.

4) There are a lot of questionable assumptions you made about the graph
of task as function of utility. (This is one of the problems will applying the 
"When in doubt,  draw a diagram" heuristics to as fuzzy a field as heuristics...)
a) Even granting that there is a well defined domain of tasks, it's not clear
that f#23, which maps the utility of H#23 as a function of task, will be
continuous.
b) Why do you assume that all n*m graphs of all N heuristics will have
a single hump? I'll grant that for H#19, one
can twiddle the axes of the (i,j) graph to move all the humps into one
contiguous region; but this does NOT imply that the other n-1 graphs, using
this same scale for the abscissa, will also have a single peak.
An this says nothing about the other N-1 hueristics - in particular of the shape
of each of their n*m graphs, using these coordinates.

------
One single well-described example will help the reader thru points 1-3 above.
Two such examples are a necessary first stab at defining the space, and
proving (or possibly disproving) your empirical-sounding claims about
the shape and niceness of that space.
	Russ