Methodology 2

1
Methodology 2

• Design research programs
• Computational Philosophy of Science
• Formation of Concepts and Hypotheses

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Design research programsSect. 10

• PM Structure-components in research programs
• domain
• problem/goal
• idea/hard core (incl. vocabulary)
• positive heuristic
• model as positive heuristics

3
P.M. Kinds of Research Programs
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Design (research) programs

• Examples new materials, medical drugs, education
  programs, computer programs
• other RPs as supply-RPs
• CORE IDEA descriptive meta-RP (Weeder c.s.)
• development of a research-RP systematic
  attempt to reach agreement between
• properties of available materials
• demands derived from intended applications
• elaboration confusion of 2 distinctions

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Naive model standard problem situation

• RP relevant properties
• W wished for properties
• x prototype
• O(x) factual/operational properties
• W/O(x) wished for resp. operational profile
• NB
• 1 P ór not-P in RP, not both!
• 2 W individual- or group related

RP
O(x)
O(x)-W
W-O(x)
W
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Standard problems

• W-O(x)
• not-realized desired properties
• O(x)-W
• realized undesired properties
• to be established by experimentally testing of
  the claim WO(x)
• forming the options for revision and negotiation

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Evaluation criteria of state transitions

• I x2 is a (qualitative) improvement of x1
• W-O(x2) subset of W-O(x1)
• O(x2)-W subset of O(x1)-W
• at least once a proper subset
• RP

O(x1)
O(x2)


W
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Continued concessionscf. Drugs looking for
diseases

• I W2 is a (qualitative) concession wrt W1
• W2-O(x) subset of W1-O(x)
• O(x)-W2 subset of O(x)-W1
• at least once a proper subset
• RP

O(x)
W2


W1
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Concretizations etc of the naive model

• Distinction structural/functional properties to
  be presented
• potential applications/ potential realizations
• extension with possibly relevant properties
• refinement YES/NO character of properties
• ,,,,,,,,,,,,,,, with degrees of relevance of
  properties
• quantitative versions of the criteria
• partial analogy with truth approximation
• extrapolation to products on the market

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S/F-MODEL

• S/F structural/functional properties (S U
  FRP)
• OS(x)/OF(x)operational structural/functional
  profile of x
• WF wished for functional profile
• AS(WF) for WF appropriate profile
  ASOS(x) ?cc OF(x)WF
• -gtcc has causal consequence
• NB1 explicitely room for functional equivalents
• NB2 evaluation criteria now in terms of
  F-properties

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Diagram S/F-model

• minimal causality/ maximal room for negotation
• (a) OS(x)OS(x) ?cc OF(x)OF(x)
• (b) all members of SRP-F are necessary
• to make (a) generally true

F
S
OF(x)
OS(x)
?cc
WF
AS(WF)
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HEURISTIC PRINCIPLES(invalid, but useful as
default rules)

• HP1 increasing structural similarity
  (presumably) leads to increasing functional
  similarity
• HP2 and vice versa, though with more exceptions
  due to causal asymmetry
• NB1 no HP's for functional concessions
• NB2 Weeber c.s. confused W/O and S/F

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Computational Philosophy of Science (CPS)
Formation of Concepts and HypothesesSection 11,
Appendices 11A-D

• CPS co-production Philosophy of Science and
  Cognitive Science (notably CPAI)
• Intended results computer programs for
• (re-)discovery of concepts and laws
• formation, testing and revision of hypotheses
• proposing experiments
• separate and comparative evaluation of theories
• Using heuristics based on cognitive structures

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Points of Departure

• Context of Justification (CoJ) and Discovery
  (CoD) can both be systematised and programmed
• Scientific research is a form of problem solving,
  with paradigm heuristic search
  (Newell/Shaw/Simon)
• Possible aims
• historical/psychological/philosophical adequacy
• practical relevance computer-assisted DJ

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BACON-4 (Simon c.s.)

• P.M. BACON-1(-3) searching q-laws between data
  sequences for (more than) two variables
• BACON-4 assign intrinsic properties to nominal
  variables and subsequently form hypothesis
• by trial and error and Bacon1/3 heuristics
• resistance (Ohm), volume (Archimedes), specific
  heat (Black), inertial mass (conservation of
  momentum), gravitational mass (law of
  gravitation)
• by searching common divisor
• atomic weights (Cannizaro), e-charge (Millikan)

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Example Postulating the property of conductance
based on ideal data for I
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GLAUBER (Simon c.s.)

• Given properties and reaction equations of some
  (pure) substances
• Goal classes of substances and reaction
  equations in terms of these classes
• Heuristic Operations
• form the largest possible classes
• quantify universally if possible, otherwise
  existentially

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Example

• Given 8 substances
• 3 taste similarities sour, bitter, salt,
• 4 reaction equations
• Results
• three classes ACID, ALKALI(BASE), SALT
• ACIDS and ALKALIS form SALTS
• Similar programs, e.g. CLUSTER

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Processes of Induction (PI, Thagard, c.s.)

• Explanation problem, start with knowledge base
  (KB)
• When matching fails, try Induction and
  Evaluation generalisation, abduction,
  concepttheory formation
• Example concept formation
• Why does sound propagate?
• Initially activated concept sound, with
  spherical propagation
• Sec.act. concept (water-)wave, with propagation
  in plane
• Form concept sound-wave, with spherical
  propagation rule
• Form theory sounds are sound-waves
• Evaluate separately and comparatively, and select
  the best

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General scheme

• General explanation problem
• C1 initially activated concept
• C2 secondarily activated concept
• if C1- and C2-rules are partially incompatible
• form combination concept C with all C1-rules
  plus all compatible C2-rules
• form theory all C1 are C
• evaluate separately and comparatively
• select the best one

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Other approaches

• For pre-1990, see Shrager and Langley (1990),
• spec. Chs 3, 6-11 on theory evaluation resp.
  revision
• ECHO quasi connectionist evaluation (also
  Thagard, 1992)
• PINEAS A unified approach to explanation and
  theory formation
• AbE (Abduction Engine) Theory formation by
  abduction
• COAST A computational approach to theory
  revision
• KEKADA Experimentation in machine discovery
• HYPGENE Hypothesis formation as design
• (TRANSGENE) Diagnosing and fixing faults in
  theories
• Indications in Darden (1997) Kuipers (2001)

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Meta-analysis Computational support of
discovery (Langley, 2000)

• Stages of the discovery process
• formation of taxonomies
• CLUSTER, AUTOCLASS, RETAX
• discover qualitative laws
• GLAUBER (PI)
• discover quantitative laws ( intrinsic
  properties)
• BACON
• creation of structural models ( unobservable
  entities)
• DALTON, STAHL, GELL-MANN
• creation of process models (mechanisms)
• MECHEM, ASTRA
• hybrid AM, IDS, KEKADA

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Steps at which the developer or user can
influence system behavior

• problem formulation
• representation engineering
• data manipulation
• algorithm manipulation
• algorithm invocation
• filtering and interpretation

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8 computer-aided scientific discoveriesordered
by the 5 stages, and presented in terms of the 6
steps

• stellar taxonomies from infrared spectra
  AUTOCLASS
• qualitative factors in carcinogenesis RL
• chemical predictors of mutagens PROGOL
• quantitative laws of metallic behavior
  DAVICCAND
• quantitative conjectures in graph theory
  GRAFFITI
• temporal laws of ecological behavior LAGRAMGE
• structural models of organic molecules DENDRAL
• reaction pathways in catalytic chemistry MECHEM

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Interactive discovery with DAVICCAND quantitative
laws of metallic behavior
A trace interaction between a metallurgist (M)
and system developer (S) jointly using DAVICCAND
to analyze data about the behavior of iron slags.
Note the Strathclyde data set has two
slightly different groups that more or less fall
on a line, but the fits are better if each group
is treated separately
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M Okay, can you bring up the Strathclyde data
set? S Loads and displays the data set. M Can
you highlight all those points that contain less
than 10 silicon actually Si02? S Creates
and displays the new group. M Can you draw a
line through those points? S Straight line or
curve? M A straight line. S Invokes module that
fits and displays a line. M What about those
points with more than 10 silicon? S Creates and
displays the new group. M That doesn't look
quite right. Can you change the value to
20? S Removes old groups from display, then
creates and displays the new groups and
lines. M Still not quite right.
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S Do you want to try a curve? Or we could try
searching for the two lines. M Let's try
searching. S Where abouts in the data set do you
want to search for the lines? M From 10 to 70
silicon? S We're currently looking at log sulphur
vs optical basicity. To do that I need to
change the visualization or, if you can say
roughly where on the screen you want to search
from, I can do that without changing the
visualization. M Points at screen, showing
start and stop points. From here to here. S
Invokes the search process.
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M That looks interesting. Can you show me what
the groups look like? S Displays the group
definitions. M It looks like the bottom group
silicon less than 44 is not a straight line.
Can you draw a curve through that? S What degree
of polynomial? M Two or three. S Invokes
curve-fitting module.
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Progress and prospects

• so far mainly historical cases, but some novel
  discoveries
• great potential for aiding the scientific process
• requires substantial interaction of developer and
  researcher
• researchers show not much enthusiasm for
  collaboration
• If we want to encourage synergy between human
  and artificial scientists, then we must modify
  our discovery systems to support their
  interaction more directly
• We predict that, as more developers realize the
  need to provide explicit support for human
  intervention, we will see even more productive
  systems and even more impressive discoveries that
  advance the state of scientific knowledge

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Knowledge Discovery in Science(Raúl
Valdés-Pérez, 1999)

• Basic concepts
• Heuristic search in combinatorial spaces
• Data-driven and knowledge driven approaches
• Enhancing human discovery
• The goals of scientific discovery
• Three examples of successful Discovery Programs
• ARROSMITH (med), GRAFFITI (math), MECHEM (chem)
• Guiding questions for automating discovery
• Patterns of successful user/computer
  collaboration next

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Patterns of successful user/computer collaboration

• Search a combinatorial space comprehensively
• report the simplest solutions first
• design a search space with highly understandable
  elements
• if knowledge-driven
• let relevant knowledge be inputted interactively
• solutions should respect that knowledge
• if data-driven
• use abundant data if possible
• use permutation tests if the data are scarce
• Finally cultivate interdisciplinary
  collaboration

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Challenge 1 Law laden concept formation?

• Example (Kuipers, 2001, Ch.2) Empirical absolute
  temperature T and the ideal gas constant R can be
  explicitly defined on the basis of 3 empirical
  (asymptotic) gas laws dealing with V(olume),
  P(ressure) and thermal states, based on the eq.
  rel. of thermal equilibrium. The laws provide the
  necessary existence and uniqueness conditions.
  After the definition the 3 laws can be
  summarised in the standard form PVRT
• BACON-4/5/BLACK seem not yet able to do this
• NB B-5 /BLACK operate theory (symmetry,
  conservation) driven

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Challenge 2 Belief revision aiming at truth
approximation

• Belief revision theory revision in the face of
  evidence generation and evaluation of a revised
  theory abduction of a revision
• Peirce,Thagard, Aliseda, etc.
• General characterization
• in search of an acceptable explanatory hypothesis
  for a surprising or anomalous (individual or
  general) observational fact

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The four main abduction tasks 1 / 2 Peirce /
Aliseda

• Task 1 surprising observation B / E
• expand B with H such that
• BHE consistent, H / E, and
• B,H E
• Task 2 anomalous observation B not-E
• contract B to B' and expand with H such that
  B'HE consistent, B /E, H/ E, and
• B',H E
• alternative 'concretize B'

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Generalizations

• Task 3 theory (or domain) revision aiming at
  empirical progress
• Task 4 theory (or domain) revision aiming at
  truth approximation
• Claim 1 Task 1 and 2 are special cases of Task 3
• Claim 2 Task 4 amounts to Task 3 and test tasks
  
• Hence Task 3 is THE abduction task
• 1. Atocha Aliseda and Joke Meheus made a start,
  by using semantic tableaux upside down adaptive
  logic, resp.
• 2. Genuine novelty remains in Task 4

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References Darden, L. (1997) "Recent work in
computational scientific discovery", in
Proceedings of the Nineteenth Annual Conference
of the Cognitive Science Society, eds. M.
Shafto and P. Langley, Lawrence Erlbaum,
Hillsdale, pp. 161-166. Web www.cs.umd.edu/zbe
n/demo/dist/papers/darden97.r.html Kuipers, T.
(1999), Abduction Aiming at Empirical Progress
or Even at Truth Approximation, Foundations
of Science, 4, 307-23. Kuipers, T. (2001)
Computational Philosophy of Science, Ch. 11,
Structures in Science, Synthese Libarary 301,
Kluwer AP Langley, P. (2000) The computational
support of scientific discovery, International
Journal of Human-Computer Studies, 53, 393-410.
Web www.isle.org/langley/pubs.html. Langley,
P., H. Simon, C.Bradshaw, J. Zytkow (1987)
Scientific Discovery, Computational
Explorations of the Creative Processes, MIT,
Cambridge, Mass. Shrager, J. and P. Langley
(1990) Computational models of scientific
discovery and theory formation, Kaufmann, San
Mateo Thagard, P. Computational Philosophy of
Science, MIT-Press, Cambridge, 1988/1993 Thagard,
P. Conceptual revolutions, Princeton University
Press, 1992 Valdés-Pérez, R., (1999), "Principles
of human computer collaboration for knowledge
discovery in science", Artificial Intelligence,
107 (2), 335-346. Web www.cs.cmu.edu/sci-disc