The Scienceomatic 7b Environment for Scientific Representation, Reasoning and Discovery

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The Scienceomatic 7bEnvironment for Scientific
Representation, Reasoningand Discovery

• Joseph Phillips
• jphillips_at_cdm.depaul.edu
• 2008 November

2
Overview

• Motivation
• Computational Scientific Discovery
• System Overview
• SciRep and Scienceomatic 7b details
• Applications
• Conclusion

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Computers need tounderstand science!

• Little problem gt
• Big consequence
• Mars Climate Orbiter
• NASA asked for thrust in Newton-seconds
• Lockeed Martin gave thrust in pound-seconds
• Spacecraft burned up in Mars' atmosphere
• 327.6 million wasted

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Computers need tounderstand science! (2)?

• Big problem gt Big consequence
• Understanding mortgage-backed securities mess

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Computational Scientific Discovery

• Computational Scientific Discovery combines
• Artificial Intelligence (Machine Learning,
  Knowledge Representation and Reasoning, KDD)?
• Science
• Philosophy of Science
• Q Why not use conventional Artificial
  Intelligence and Machine Learning techniques?
• A Because AI and ML see the world differently
  than science.

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AI vs. CSD

• Artificial Intelligence (and logicians) like nice
  consistent knowledge bases
• Science is happily inconsistent
• Relativity
• Newton p mv
• Einstein p mv/sqrt(1-v2/c2)
• Quantum mechanics
• Rumford, Coriolis, Poncelet, Mohr, et al
• The energy of a particle varies with velocity
  and potential
• Niels Bohr
• The energy of an electron varies with velocity
  and potential . . . and it is quantized

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ML vs CSD

• ML emphasizes the data
• CSD emphasizes the bias
• Q ML folks consider this cheating, why do it?
• A Because that's what scientists do!

Data
Model
Bias

gt
Bias
Model
Data
gt

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Our Approach

• The Scienceomatic 7b
• Designed to answer question What is object O's
  value for attribute A given context C?
• Knowledge bases written in SciRep
• Compiled to Lisp
• Supports deductive, stochastic reasoning
• Queries return justification objects that trace
  reasoning and contextualize it in a story.
• Open architecture, future modules can support
• Inductive, abductive, and analogical reasoning
• Scientific visualization
• Model modification
• Model comparison

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System Overview Diagram
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Implement Lisp in Java (?)?
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Autonomous and Distributed
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Extendable Open Architecture
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System Features (1)?

• Object oriented architecture into scientific and
  meta-scientific entities

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System Features (2)?

• Justification Traces
• Trace through knowledge base of path taken to
  answer question
• Gives you
• An explanation of the answer (if one is found)?
• A Lisp program that computes an answer (may be
  stochastic)?
• Justification Stories
• For comparison among justification traces
• To argue why one trace is better than others

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System Features (3)?

• Automatic conversion and unification of units and
  dimensions
• 100 cm 1 meter 2 meters
• (100 cm 1 meter) true
• (101 cm 1 meter) false
• 100 cm 1 second Error!
• (100 cm 1 second) Error!

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System Features (4)?

• Values as distributions
• object.length 3.97, 3.99, 4.00, 4.00, 4.01 cm
• Integers, ratios, floats and symbols are
  represented as bags
• Like sets they are unordered
• Like lists they may contain the same item more
  than once
• By convention, distribution members are listed in
  ascending order
• Two unsampled, analytical distributions exist
• Normal( ltmeangt, lttwice_std_devgt )?
• Uniform( ltlowgt, lthighgt )?

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System Features (5)?

• Stochastic Computation
• Can take 3 paths
• (1) Case p(A) lt p(B)?
• (2) Case p(A) p(B)?
• (3) Case p(A) gt p(B)?
• 3 different justification traces
• Decision is memorized in context for consistency

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System Features (6)?

• Preference for symbolic computation over
  comparison computation
• Box.chocolateSet.setSizeAttr lt
  Box.binSet.setSizeAttr
• (The number of chocolates in a box is less than
  or equal to the number of bins in the same box)?
• No need to compute with number of chocolates or
  number of bins

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How to the Scienceomatic computes

• Issue 1
• There is more than one way to compute something.
• Example What is the weight of Joe Phillips?
• Empirical scientist
• Weigh him!
• Theoretical scientist
• Apply the proper formula to get ab initio value
• ?(particles p in JoePhillips) ?
  mass(p)gravitationalAcceleration

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Dealing with Issue 1

• First, label knowledge by type
• define For cultural definitions
• There are 100 cm in 1 meter.
• expect Like define, but for we only expect
• As a biologist, I believe H. sapiens is one of
  many species.
• data For measurements.
• Tycho Brahe's astronomical observations.
• generalize Generalized data explained by theory
• Kepler's Laws
• theory Explains generalizations
• Newton's Laws of Motion and Gravitation
• analytical True by mathematics.
• Pythagorean theorem.

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Dealing with Issue 1, cont'd

• Second, search by knowledge type
• abInitio define/expect, theory, analytic
• readData define/expect, data, analytic
• theorize define/expect, theory, generalize,
  data, analytic
• empiricize define/expect, data, generalize,
  theory, analytic
• given define/expect, analytic
• define/expect always first consistency w/culture
• analytical always last recast problem

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How to the Scienceomatic computes (2)?

• Issue 2
• There are multiple sources of knowledge
• Example What is the weight of Joe Phillips?
• We can answer base on . . .
• what Joe Phillips is undergoing
• Joe Phillips' environment's
• by virtue of who and what Joe Phillips is

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Dealing with Issue 2

• For each search-by-knowledge type search in this
  order
• Processes
• Returns an dynamic answer
• Based on what Joe Phillips is undergoing
• Stases
• Returns a static answer
• Based on Joe Phillips' environment
• Object
• Returns a identity answer
• Based on who and what Joe Phillips is

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Application 1 Reasoning

• Query
• What's Joe Phillips' weight on 28 Sept 2008?
• Any active processes? No process specified.
• Any active stases? Yes, assume on Earth
• Search type? Empiricize (favor weighing him
  over computing the mass of all of his particles)?
• Full query
• What's Joe Phillips' weight on 28 Sept 2008?
• Context (search_typeempiricize, stasis_set
  contemporaryEarthSurfaceGravitation)?

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Application 1 (1)?
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Application 1 (2)?
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Application 1 (3)?
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Application 1 (4)?
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Application 1 justification trace
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Application 2 Explanation Documentation

• Why is the Sky Blue?
• Traditional computational approach
• Measure solar output above Earth's atmosphere
  (e.g. by satellite or very high-altitude
  balloon)?
• Measure composition of Earth's atmosphere
  (molecules, particles, etc.)?
• Measure interactions of photons by wavelength on
  molecules, particles (refraction, scatter,
  absorption)?
• Do a simulation
• Gives you everything . . .
• Blue sky and . . .
• Clouds, fog, smog, rainbows, yellow sunsets/rises

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Application 2 Explanation Documentation

• Why is the Sky Blue?
• But it's not how humans explain it to each other!
• We want
• An approach that mirrors how humans explain to
  each other
• An approach that can be expanded to the fuller
  computational approach
• An approach that can support multiple explanation
  approaches

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Application 2 Explanation Documentation

• Why is the Sky Blue?
• How humans explain this to other humans
• Philips Gibbs, 1997
• http//math.ucr.edu/home/baez/physics/General/Blue
  Sky/blue_sky.html
• 1. Sunlight has (among other things) the colors
• redorangeyellowgreenblueviolet
• 2. The shorter the wavelength, the higher the
  probability of scatter upon hitting air molecule
• p(red)ltp(orange)ltp(yellow)ltp(green)ltp(blue)ltp(viol
  et)?
• 3. Photons scattered by object determine its color

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Application 2 Explanation Documentation

• Why is the Sky Blue?
• HOWEVER, the sky is not violet because
• There are less violet photons than blue ones
• Violet photons are absorbed more than blue ones
• Our eyes are more sensitive to blue than violet
• Overall justification story . . .
• Both blue and violet scatter more than red,
  orange, yellow, and green
• Violet scatters more than blue, but violet is
  less prevalent because violet is absorbed, less
  numerous, and we're less sensitive
• The sky appears blue

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Tracing Explanations for Humans

• One notation to cover it all
• lthowExplainPredgt(ltwhatExplainedgt,predArgs,ltautho
  rgt)?
• lthowExplainPredgt
• Tells which explanatory approach was used
• ltwhatExplainedgt
• Assertion that was explained
• ltpredArgsgt
• List of arguments need by lthowExplainPredgt to
  compute ltwhatExplainedgt
• ltauthorshipgt (optional)?
• Who gave us the raw data? Who said to use the
  reasoning approach? When? Why?

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Tracing Explanations for Humans

• Why is the Sky Blue?
• I.e. (sky.color blue)?
• ifThen
• (sky.color blue,
• ltifObjScattersColoredPhotonsThenIsThatColorgt,
• ltdominantScatteredPhotonColorOfSkyIsBluegt
• )?

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Tracing Explanations for Humans

• Why is the dominant scattered photon color of the
  sky Blue?
• I.e. ltdominantScatteredPhotonColorOfSkyIsBluegt
• thisButThatTherefore
• (ltdominantScatteredPhotonColorOfSkyIsBluegt,
• ltvioletAndBlueScattersMoreThanRedOrangeEtcgt,
• ltvioletLessPrevalentgt
• )?
• (No, I am not happy with this predicate's name)?

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Tracing Explanations for Humans

• Why do blue and violet photons scatter more than
  red, orange, etc.?
• I.e. ltvioletAndBlueScattersMoreThanRedOrangeEtcgt
• ifThen
• (ltvioletAndBlueScattersMoreThanRedOrangeEtcgt,
• ltifWavelengthIsSmallerThenHigherProbOfScattergt
  ,
• ltvioletAndBluePhotonsHaveSmallerWavelengthsgt
• )?

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Tracing Explanations for Humans

• Why do blue and violet photons have smaller
  wavelengths than other colors?
• stated
• (bluePhoton.wavelenltredPhoton.wavelen AND
• bluePhoton.wavelenltorangePhoton.wavelen AND
• . . .,
• )?
• All proper explanations have stated() predicates
  at leaves

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Grounding explanation in computation

• For most people one can stop at the assertion
• ltifWavelengthIsSmallerThenHigherProbOfScattergt
• Physicists might want an explanation of this too!
• SciRep can either
• Represent the assertion as stated() (from the
  generalizations)?
• Further explain it in terms of theory, like
• Lorenz-Mie-Debye theory
• Rayleigh and Raman scattering
• Plug wavelens and particle sizes in the eqns

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Supporting other reasoning approaches

• There are several ways of explaining . . .
• Logic
• Schematic (e.g. Structure mapping engine)?
• Bayesian Networks
• Neural networks

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Example Logic
Resolution Proof
Resolution Outlined in SciRep
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Example Neural Nets
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Conclusion

• The Scienceomatic 7b is . . .
• Designed for scientific models through support
  for
• dimension and units unification and conversion
• explicit, normal or uniform distributions
• Deterministic and stochastic
• Prefers symbolic computation over numeric
• Extendable, open architecture
• Supportive of different types of queries
• Ab-initio, empirical, theoretical, etc.
• Supportive multiple reasoning approaches
• Able to represent . . .
• Models
• Explanations of answers
• Stories about which justifications are preferred