1. Machine Creativity

2. Outline BackgroundBackground –The problem and its importance. –The known algorithms and systems. Summary of the Creativity Machine conceptSummary of the Creativity Machine concept ExtensionsExtensions Pros and ConsPros and Cons Scaling upScaling up ImprovementImprovement – of the AI decision-making systems previously presented. – of maze domain algorithm(s).

3. Creativity (Defined) The ability to transcend traditional ideas, rules, patterns, relationships, to create meaningful new ideas, forms, methods, interpretations. Originality, progressiveness, or imagination. (AllWords.com Dictionary) DiscoveryDiscovery - The act of finding something hidden or unknown. (Webster’s Dictionary)  Use creativity for the purpose of knowledge discovery.

4. Conventional Approaches to Knowledge Discovery Data Mining Simulated Annealing Genetic Algorithms (GA), and (GP) –E.g. Evolving gaits for AIBO robots

5. Systems AM > Eurisko > Cyrano – mathematical concept discovery BACON – discovered mathematical relations by heuristically searching for numerical patterns within astronomical data (rediscovered Kepler’s 3 rd Law of planetary motion). STAHL, GLAUBER – qualitative law discovery (primarily in chemistry)

6. Relevance to Decision Making Handle unknown/unforeseen situations within a dynamically changing environment. Creativity can be viewed as Exploration based on previous knowledge, within a domain. Sense Propose actions Select an action Execute

7. Creativity Machine Paradigm

8. Creativity Machine Paradigm (cont) Governed by the following parameters n – the number of perturbations made (mutation rate). σ – the magnitude of each perturbation (mutation factor). N – the number of connections in the IE network.  Empirically determined that best results are produced near nσ/N = 0.06 (I.e. on average each connection weight gets mutated by a magnitude of 0.06)

9. Human and Machine Creativity Empirical results show that both cognitive and ANN creativity is similar in its rhythm. Simple and Incremental adjustments to existing concepts requires lots of intermediate scale perturbations distributed though out the network. (I.e n-large, σ-small) New and Complex idea generation requires few large-scale perturbations localized with a particular area of the network. (n-small, σ-large)

10. Flavors of the Creativity Machine Type I –No critic Net –Only inputs are perturbed producing a stochastic search engine for best outputs. Type II –Auto-Associative Network + Critic Network –Imagination Engine (IE) reset after n perturbations of magnitude σ Type III –Type II with recurrent connections –IE not reset but is degraded further and further by sequential perturbations.

11. Extensions (topology control) Generalize into a complete Genetic Algorithm –Create a population of clones (all initially identical) created from a template NN that needs improvement. –Perturb (mutate) each individual and evaluate using the critic on test data. –Select the fittest individuals compared to each other and the original NN. –Repeat.

12. Pros & Cons Pros –Empirically established that the mechanism for creativity in ANN’s parallels that of human creativity. Cons –Evaluation of new ideas may be difficult or impossible. (I.e there may be no way to construct a good critic module) –Time critical systems may not be able to wait for the creative process to come up with a valid action.

13. Usability of Concept The Creativity Machine has invented new control techniques, ultra hard materials, dance moves, drinks, … Can be viewed as an oracle working in conjunction with an existing policy to aid decision making.

14. Applications Control Policy modification in Robots with lots of degrees of freedom. –AIBO trying modified gaits to see if they will work (on-line learning).

15. Acknowledgements www.imagination-engines.com –L.Thaler, “ A Quantitative Model of Seminal Cognition: The Creativity Machine Paradigm”, 1997. –L.Thaler,” Neural Networks That Autonomously Create and Discover”, 1996. –L.Thaler,” Principles and Applications of the Self-Training Artificial Neural Network Object ”, 1996. www.wam.umd.edu/~zben/Web/JournalPrint –L.Darden, “Recent Work in Computational Scientific Discovery”, 1996. www.media.mit.edu/~haase/thesis –K.Haase, “Invention and Exploration in Discovery”, 1996

16. Creativity Machine Paradigm (cont) Concept generation rhythm.