Computational Creativity

1
ComputationalCreativity

• Richie Abraham(05005010)
• Pramod Mudrakarta(05005030)
• Shashank Samant(05D05011)
• Sumedh Ambokar(05D05013)

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Computational Creativity

• Creativity is a process involving the generation
  of new ideas or concepts, or new associations
  between existing ideas or concepts
• 
  -- Wikipedia.
• Humans are creative. Ability to think out of the
  box.
• Goal of Computational Creativity To model,
  simulate or replicate creativity using a
  computer.
• Reflections are images of tarnished
  aspirations
• --a quotation generated by the program RACTER,
  in 1984

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Roadmap

• Motivation
• Formalizing the notion
• Creative Flexibility
• Meta- level
• Analogy
• Discovery Programs
• Case studies
• AARON
• BRUTUS

4
Why Creativity?

• To construct a program/computer capable of
  human-level creativity.
• To better understand human creativity and to
  formulate an algorithmic perspective on creative
  behavior in humans.
• To design programs that can enhance human
  creativity without necessarily being creative
  themselves.

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Essential Characteristics of a Creative Idea

• Ideas of Newell, Shaw and Simon
• Novelty and usefulness (either for the individual
  or society)
• Rejection of previous ideas
• Results from intense motivation and persistence
• Clarification of vague ideas
• Margaret Bodens view
• P-Creativity (Psychological or Individual)
• H-Creativity (Historical or Collective)

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Formalizing the Philosophical Concepts

• Exploration Within a conceptual space
• Transformation Out of the box(space)
• Formalization
• Conceptual Space C a strict subset of set of all
  concepts U
• Axiom1Every concept c is a distinct member of U
• Axiom2Every conceptual space includes F (empty
  concept)

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Formalization (contd)

• Rules R(Existsence in a space) and
  T(Transformation in a space) R , T subsets of L
• Interpretation function . and Traversal
  function lt.gt
• Exploratory concepual space as a tuple
  (U,L,R,T,E)
• Beginning of exploratory creative process ltR
  Tgt(F)
• Evaluating concepts (E) E(C) value of the
  conceptual space.

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Formalization(contd)

• Partitioning a conceptual space into concepts
  achieved and concepts not achieved yet
• Exploratory search involves experimenting with T
• Transformation search involves experimenting with
  R
• T is the technique of the individual to search
• R is the mutually agreed domain specified.
• Meta level Rules for changing R and T

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Where is the AI?

• RACTER, 1984 generates poems, stories, etc.
• Syntax directives
• Sentences too bizarre at first look
• Deeper meaning on repeated thought
• Creativity is in the readers mind
• Sentences become insignificant soon
• Need for more control

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Need for Flexibility

• Rule-based systems are monotonous
• Example Generating a story (TALE-SPIN)
• Each object tries to satisfy its goals
• Creativity is shown only when the plot turns an
  unexpected way
• Object need not try to reach goals at every step
• Solution Use the meta- approach
• Develop rules for rules

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Using meta- rules

• MUSCADET Theorem prover for linear spaces
• Heuristics used in proving
• Meta- rules over heuristics
• Drawbacks Does not distinguish important and
  trivial issues from a math point of view.
• Example Trying to be creative in proving 11
  versus trying to be creative in proving prime
  factorization

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Another example(problem?)

• DAY-DREAMER planner
• Operates on two interacting domains(personal,
  objective)
• Each works on its own goals.
• Preprocessing Determines situations where
  personal goals are met
• In action Tries to match the succesful plans
  with the objective world situations
• Drawbacks Determining the parameters of personal
  world is hard.

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The meta- question Analogies

• Meta-rule based systems not very different
• Need for meta-meta-rule based systems
• Deja vu?
• Alternate approach Working by analogy
• Concepts from other domains applied

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Analogy contd.

• Mapping (electron, nucleus) to (planet, sun)
• Problem Choosing variables whichdetermine
  similarity
• Planets on orbit
• Planets have moons
• Sun loses energy, emits light
• Drawback Solving the problem is hard

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Discovery Programs

• Shashank

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Discovery Programs Overview

• Able to discover new facts on a domain
• Three major families
• AM Family
• Domain Mathematics
• AM , Euresco , Cyrano
• BACON
• Domain experimental data
• BACON, GLAUBER, STAHL, DALTON
• GT
• Domain Graph Theory

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AM Overview

• Starts with set of concepts arranged in a
  specialization hierarchy
• Concept
• Definition, Examples, Domain, Range,
  Specializations, Worth
• Initial concepts Sets, List, Ordered pairs and
  some operations
• Heuristics
• Fill, Check, Suggest, Interest
• Task
• Applying heuristics on set of concepts
• Output concept as a code

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AM

• Discoveries
• Natural numbers, addition, primes
• Prime factorization, Goldbachs conjecture
• Limitations
• Heuristics too theory specific
• Many theories ignored
• Interpretation of concepts ambiguous

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BACON Family

• Operate on a data driven basis
• Heuristically guided process
• Mostly an ad-hoc curve fitting exercise
• BACON
• Syntactic number games to summarize data
• GLAUBER
• Generalization from specific examples
• STAHL
• Model building using three rules
• Infer, Substitute and Reduce
• DALTON
• Atomic Modelling

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Graph Theorist (GT)

• Discovers and proves properties of graphs
• Graph property
• A property p represents a set of graphs P iff
  every graph in P satisfies p
• Represented by a concept
• Examples TREE, ACYCLIC, COMPLETE.

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Concept

• Defined by a triple ltf, S, sgt
• f operator
• To transform a member to a new member
• S seed set
• Minimal graphs satisfying the property
• s selector
• Restrictions for binding variables appearing in f
• Example
• Acyclic ltAxAyzAz K1 y in V, x, z not in Vgt

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p-Generator

• Exhaustively generates P described by p
• Checks if particular graph is a member of P
• A can be used
• Still quite inefficient
• Not of much interest

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4 Types of Graph Theorems

• If a graph has a property p, then it has property
  q
• A graph has property p if and only if it has
  property q
• If a graph has property p and property q, then it
  has property r
• It is not possible for a graph to have both
  property p and property q

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Subsumption and Merger

• Property p for class P subsumes property q for
  class Q iff Q is a subset of P
• Merger of p and q is the property representing
  intersection of P and Q
• The four rules rewritten as
• q subsumes p
• p subsumes q and q subsumes p
• r subsumes merger of p and q
• merger of p and q is empty

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Proofs of Subsumption

• p1ltf1,S1,s1gt, p2ltf2,S2,s2gt
• p1 subsumes p2 if-
• f2 is a special case of f1
• Every graph in S2 has property p1.
• s2 is more restrictive than s1
• Example
• GRAPH subsumes TREE.

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Proof Involving Mergers

• p1ltf1,S1,s1gt, p2ltf2,S2,s2gt
• P is the merger of p1 and p2
• If p1 subsumes p2, p is p1.
• Three more complex rules.
• Example-
• ACYCLIC merged with CONNECTED is TREE

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Construction of new concepts

• By specialization
• constrain the seed set, operator or selector
• combination of above
• By generalization
• expand the seed set, operator or selector
• combination of above
• By merger

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Final word on GT

• Generates many new concepts and proves
  properties.
• Power increases with increased knowledge base
• Limitations
• does not assign worth to concepts
• only properties of graph theory.

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Case Studies

• Sumedh

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AARON Overview

• By Harold Cohen
• Creates original artistic images
• Since 1973
• Initially only black and white images
• Colored images since 1992
• See it to believe it !

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AARON Techniques

• Structure of core-figures embedded
• Body parts
• Postures
• Starts scribbling randomly
• Next step based on what is drawn so far
• Coloring after sketching
• Core-figures determine colour

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Is AARON creative?

• Can create infinite distinct images
• Cannot learn imagery on its own
• Output follows a noticeable formula
• Real artist is Cohen
• Cohen If it is not thinking, what exactly is it
  doing?

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BRUTUS Overview

• A creative story generator
• Should have wide variability
• Plot, characters, settings, themes, imagery
• Earlier strategy
• Each variable aspect parameterized
• Wide variability not achieved

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BRUTUS approach

• Designed to satisfy seven characteristics
• Capable of raw imagination
• Generate imagery
• Defines mental state and actions of characters
• Mathematize themes
• Interesting stories
• Topics like sex, money and death
• Structured stories
• Avoid mechanical prose

35
Conclusion

• Many philosophical issues
• Lack of universally accepted definition of
  creativity
• Light at the end of tunnel
• One of the fastest growing areas of research in
  AI.

36
Current Research

• IJWCC 2003, Acapulco, Mexico, as part of
  IJCAI'2003
• IJWCC 2004, Madrid, Spain, as part of ECCBR'2004
• IJWCC 2005, Edinburgh, UK, as part of IJCAI'2005
• IJWCC 2006, Riva del Garda, Italy, as part of
  ECAI'2006
• IJWCC 2007, London, UK, a stand-alone event

37
Journals

• Journal of Knowledge-Based Systems, volume 9,
  issue 7, November 2006
• New Generation Computing, volume 24, issue 6,
  2006
• http//www.thinkartificial.org/artificial-creativi
  ty/

38
References

• Learning and Discovery One Systems Search for
  Mathematical Knowledge. Epstein. Computational
  Intelligence 4 (1) 42-53, 1988.
• Creativity A survey of AI approaches, J. Rowe
  and D. Patridge. Artificial Intelligence Review
  7, 43--70, 1993.
• Colouring Without Seeing a Problem in Machine
  Creativity. Harold Cohen, Dept. of visual arts,
  UC San Diego, 2003

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References

• The further exploits of AARON-painter, Harold
  Cohen, 2001.
• Towards a more precise characterisation of
  creativity in AI, IJWCC 2005
• www.wikipedia.org
• www.kurzweilcyberart.com