Searching for Surprise
Inhaltsverzeichnis
Reference
Georgios N. Yannakakis and Antonios Liapis: Searching for Surprise. In: Computational Creativity 2016 ICCC 2016, 25-32
DOI
Abstract
Inspired by the notion of surprise for unconventional discovery in computational creativity, we introduce a general search algorithm we name surprise search. Surprise search is grounded in the divergent search paradigm and is fabricated within the principles of metaheuristic (evolutionary) search. The algorithm mimics the self-surprise cognitive process of creativity and equips computational creators with the ability to search for outcomes that deviate from the algorithm’s expected behavior. The predictive model of expected outcomes is based on historical trails of where the search has been and some local information about the search space. We showcase the basic steps of the algorithm via a problem solving (maze navigation) and a generative art task. What distinguishes surprise search from other forms of divergent search, such as the search for novelty, is its ability to diverge not from earlier and seen outcomes but rather from predicted and unseen points in the creative domain considered.
Extended Abstract
Bibtex
@inproceedings{ author = {Georgios N. Yannakakis and Antonios Liapis}, title = {Searching for Surprise}, booktitle = {Proceedings of the Seventh International Conference on Computational Creativity}, series = {ICCC2016}, year = {2016}, month = {Jun-July}, location = {Paris, France}, pages = {25-32}, url = {http://www.computationalcreativity.net/iccc2016/wp-content/uploads/2016/01/Searching-for-Surprise.pdf http://de.evo-art.org/index.php?title=Searching_for_Surprise }, publisher = {Sony CSL Paris}, }
Used References
Adami, C.; Ofria, C.; and Collier, T. C. 2000. Evolution of biological complexity. Proceedings of the National Academy of Sciences 97(9).
Angeline, P. J., and Pollack, J. B. 1994. Competitive environments evolve better solutions for complex tasks. In Proceedings of the International Conference on Genetic Algorithms.
Boden, M. 1995. Creativity and unpredictability. Constructions of the Mind: Artificial Intelligence and the Humanities. Stanford Electronic Humanities Review 4(2).
Boden, M. A. 2004. The Creative Mind: Myths and Mechanisms. Routledge.
Channon, A. 2001. Passing the alife test: Activity statistics classify evolution in geb as unbounded. In Advances in Artificial Life. Springer.
Compton, K., and Mateas, M. 2015. Casual creators. In Proceedings of the International Conference on Computational Creativity.
Donchin, E. 1981. Surprise! surprise? Psychophysiology 18(5):493–513.
Ekman, P. 1992. An argument for basic emotions. Cognition & emotion 6(3-4).
Goldberg, D. E., and Holland, J. H. 1988. Genetic algorithms and machine learning. Machine learning 3(2).
Grace, K., and Maher, M. L. 2015. Specific curiosity as a cause and consequence of transformational creativity. Proceedings of the International Conference on Computational Creativity June.
Grace, K.; Maher, M. L.; Fisher, D.; and Brady, K. 2014. Modeling expectation for evaluating surprise in design creativity. In Design Computing and Cognition.
Gravina, D.; Liapis, A.; and Yannakakis, G. N. 2016. Surprise search: Beyond objectives and novelty. In Proceedings of the Genetic and Evolutionary Computation Conference. ACM.
Horvitz, E. J.; Apacible, J.; Sarin, R.; and Liao, L. 2005. Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service. In Proceedings of the 2005 Conference on Uncertainty and Artificial Intelligence. AUAI Press.
Itti, L., and Baldi, P. F. 2005. Bayesian surprise attracts human attention. In Advances in neural information processing systems, 547–554.
Kaplan, F., and Hafner, V. V. 2006. Information-theoretic framework for unsupervised activity classification. Advanced Robotics 20(10).
Kulkarni, D., and Simon, H. A. 1988. The processes of scientific discovery: The strategy of experimentation. Cognitive science 12(2):139–175.
Lehman, J., and Stanley, K. O. 2010. Revising the evolutionary computation abstraction: Minimal criteria novelty search. In Proceedings of the Genetic and Evolutionary Computation Conference.
Lehman, J., and Stanley, K. O. 2011a. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary computation 19(2).
Lehman, J., and Stanley, K. O. 2011b. Evolving a diversity of virtual creatures through novelty search and local competition. In Proceedings of the Genetic and Evolutionary Computation Conference.
Lehman, J., and Stanley, K. O. 2012. Beyond openendedness: Quantifying impressiveness. In Proceedings of the International Conference on Artificial Life.
Lehman, J.; Stanley, K. O.; and Miikkulainen, R. 2013. Effective diversity maintenance in deceptive domains. In Proceedings of the Genetic and Evolutionary Computation Conference.
Liapis, A.; Mart´ınez, H. P.; Togelius, J.; and Yannakakis, G. N. 2013. Transforming exploratory creativity with De- LeNoX. In Proceedings of the International Conference on Computational Creativity.
Liapis, A.; Yannakakis, G. N.; and Togelius, J. 2015. Constrained novelty search: A study on game content generation. Evolutionary Computation 23(1):101–129.
Lorini, E., and Castelfranchi, C. 2007. The cognitive structure of surprise: looking for basic principles. Topoi 26(1).
Macedo, L., and Cardoso, A. 2001. Modeling forms of surprise in an artificial agent. In Proceedings of the nnual Conference of the Cognitive Science Society.
Macedo, L., and Cardoso, A. 2002. Assessing creativity: the importance of unexpected novelty. Structure 1(C2):C3. Macedo, L.; Cardoso, A.; Reisenzein, R.; Lorini, E.; and Castelfranchi, C. 2009. Artificial surprise. Handbook of research on synthetic emotions and sociable robotics: New applications in affective computing and artificial intelligence 267–291.
Maher, M. L.; Brady, K.; and Fisher, D. H. 2013. Computational models of surprise in evaluating creative design. In Proceedings of the fourth international conference on computational creativity.
Maher, M. L.; Fisher, D. H.; et al. 2012. Using AI to evaluate creative designs. In 2nd international conference on design creativity, Glasgow, UK, 45–54.
Maher, M. L. 2010. Evaluating creativity in humans, computers, and collectively intelligent systems. In Proceedings of the 1st DESIRE Network Conference on Creativity and Innovation in Design.
Merrick, K. E., and Maher, M. L. 2009. Motivated reinforcement learning: curious characters for multiuser games. Springer Science & Business Media.
Meyer, W.-U.; Reisenzein, R.; and Sch¨utzwohl, A. 1997. Toward a process analysis of emotions: The case of surprise. Motivation and Emotion 21(3).
Michalski, R. S.; Carbonell, J. G.; and Mitchell, T. M. 2013. Machine learning: An artificial intelligence approach. Springer Science & Business Media.
Ortony, A., and Partridge, D. 1987. Surprisingness and expectation failure: what’s the difference? In Proceedings of the 10th international joint conference on Artificial intelligence-Volume 1, 106–108. Morgan Kaufmann Publishers Inc.
Oudeyer, P.-Y.; Kaplan, F.; and Hafner, V. V. 2007. Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation 11(2).
Pease, A., and Colton, S. 2011. Computational creativity theory: Inspirations behind the face and the idea models. In Proceedings of the Second International Conference on Computational Creativity.
Reisenzein, R. 2000. The subjective experience of surprise. The message within: The role of subjective experience in social cognition and behavior 262–279.
Ritchie, G. 2007. Some empirical criteria for attributing creativity to a computer program. Minds and Machines 17(1).
Saunders, R., and Gero, J. S. 2004. Curious agents and situated design evaluations. AI EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18(02):153–161.
Schmidhuber, J. 2010. Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Transactions on Autonomous Mental Development 2(3).
Secretan, J.; Beato, N.; D’Ambrosio, D. B.; Rodriguez, A.; Campbell, A.; Folsom-Kovarik, J. T.; and Stanley, K. O. 2011. Picbreeder: A case study in collaborative evolutionary exploration of design space. Evolutionary Computation 19(3):373–403.
Stanley, K. O., and Miikkulainen, R. 2002. Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2).
Stanley, K. O. 2006. Exploiting regularity without development. In Proceedings of the 2006 AAAI Fall Symposium on Developmental Systems.
Vinhas, A.; Assuncao, F.; Correia, J.; Machado, P.; and Ek´art, A. 2016. Fitness and novelty in evolutionary art. In Proceedings of Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMusArt). Springer.
Wessing, S.; Preuss, M.; and Rudolph, G. 2013. Niching by multiobjectivization with neighbor information: Trade-offs and benefits. In Proceedings of the Evolutionary Computation Congress.
Whitley, L. D. 1991. Fundamental principles of deception in genetic search. In Foundations of Genetic Algorithms. Morgan Kaufmann.
Wiggins, G. A. 2006. A preliminary framework for description, analysis and comparison of creative systems. Knowledge-Based Systems 19(7).
Yaeger, L. 1994. Poly world: Life in a new context. Proc. Artificial Life 3.