Computational Creativity

1. ComputationalCreativity Richie Abraham(05005010) Pramod Mudrakarta(05005030) Shashank Samant(05D05011) Sumedh Ambokar(05D05013)

2. 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

3. 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.

5. 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 Boden’s view • P-Creativity (Psychological or Individual) • H-Creativity (Historical or Collective)

6. 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 • Axiom1:Every concept c is a distinct member of U • Axiom2:Every conceptual space includes F (empty concept)

7. Formalization (contd) • Rules : R(Existsence in a space) and T(Transformation in a space) R , T subsets of L • Interpretation function: [.] and Traversal function: <.> • Exploratory concepual space as a tuple : (U,L,R,T,E) • Beginning of exploratory creative process : <R + T>({F}) • Evaluating concepts (E) : E(C) = value of the conceptual space.

8. 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

9. 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 reader’s mind • Sentences become insignificant soon • Need for more control

10. 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

11. 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 “1=1” versus trying to be creative in proving prime factorization

12. 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.

13. 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

14. 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

15. Discovery Programs Shashank

16. 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

17. 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

18. AM • Discoveries • Natural numbers, addition, primes • Prime factorization, Goldbach’s conjecture • Limitations • Heuristics too theory specific • Many theories ignored • Interpretation of concepts ambiguous

19. 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

20. 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.

21. Concept • Defined by a triple <f, S, σ> • f : operator • To transform a member to a new member • S : seed set • Minimal graphs satisfying the property • σ: selector • Restrictions for binding variables appearing in f • Example • Acyclic <Ax+AyzAz; {K1}; y in V, x, z not in V>

22. 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

23. 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

24. 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

25. Proofs of Subsumption • p1=<f1,S1,σ1>, p2=<f2,S2,σ2> • p1 subsumes p2 if- • f2 is a special case of f1 • Every graph in S2 has property p1. • σ2 is more restrictive than σ1 • Example: • GRAPH subsumes TREE.

26. Proof Involving Mergers • p1=<f1,S1,σ1>, p2=<f2,S2,σ2> • 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

27. 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

28. 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”.

29. Case Studies Sumedh

30. 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 !

31. 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

32. 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?“

33. 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

34. 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-creativity/

38. References • Learning and Discovery: One System’s 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

39. 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