Abstract
What contributions can Cognitive Science offer to the understanding of nature of providing creativity to artificial life forms? For this discussion, it is necessary to investigate creative processes from a mechanistic perspective as well as involve subjective elements which cannot, in principle, be described from this perspective. These two basic approaches will be investigated here, focusing the artificial creative process on the nature of artificial abductive reasoning. As an initial hypothesis we will characterize creativity as a self-organizing process in which abductive reasoning occurs using self-organizing, semantic topical maps in conjunction with an abductive neural network, allowing the creation and expansion of a well-structured set of beliefs within the artificial system. This process is considered here as part of the establishment of order parameters in the flow of information available to allow artificial life forms to self-organize and infer on sensory information. In this sense, we will argue that a deeper understanding of how self-organizing processes involving abductive reasoning may take place in artificial dynamic systems, and how this can assist in the creation of an artificial creative process within an artificially intelligent artificial life form we refer to as a Synthetic, Evolving Life Form (SELF).
Here we present a self-evolving, abductive, hypothesis-based reasoning framework called the Advanced Learning Abductive Network (ALAN) that provides the ability to mimic human experience-based reasoning.
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References
Taylor, J. M., & Raskin, V. (2010). Fuzzy ontology for natural language. In 29th International Conference of the North American Fuzzy Information Processing Society, Toronto, Ontario, Canada.
Fodor, J. (1983). Modularity and the mind: An essay on faculty psychology. Cambridge, MA: MIT Press.
Garcia, C. (2007). Cognitive modularity, biological modularity, and evolution. Biological Theory: Integrating Development, Evolution and Cognition, 2(1), 62–73.
Gibbs, R., & Van Orden, G. (2010). Adaptive cognition without massive modularity. Journal of Language and Cognition, 2(2), 149–176.
Hutchins, E., & Lintem, G. (1995). Cognition in the wild. Cambridge, MA: MIT Press.
Rogers, Y. (1997). A brief introduction to distributed cognition. Brighton: University of Sussex Press.
DeRaedt, L. (1992). Interactive theory revision: An inductive logic programming approach. Cambridge, MA: Academic Press.
Roberts, S., & Tarassenko, L. (1994). A probabilistic resource allocating network for novelty detection. Neural Computation, 6, 270–284.
Newell, A. (2003). Unified theories of cognition. Cambridge, MA: Harvard University Press.
Nishimori, T., Nakamura, T., & Shiino, M. (1990). Retrieval of spatio-temporal sequences in asynchronous neural networks. Physical Review A, 41, 3346–3354.
Cooper, G., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309–347.
Dimopoulos, Y., & Kakas, A. (1996). Abduction and inductive learning. In L. De Raedt (Ed.), Advances in inductive logic programming (pp. 144–171). Amsterdam: IOS Press.
Jones, B. (1999). Bounded rationality. Annual Review of Political Science, 2, 297–321.
Crowder, J., & Friess, S. (2012). Artificial psychology: The psychology of AI. In Proceedings of the 3rd Annual International Multi-Conference on Informatics and Cybernetics, Orlando, FL.
Bonarini, A. (1997). Anytime learning and adaptation of structured fuzzy behaviors. Adaptive behavior (Vol. 5). Cambridge, MA: The MIT Press.
Crowder, J. (2012). Possiblistic, abductive neural networks (PANNs) for decision support in autonomous systems: The advanced learning, abductive network (ALAN). In Proceedings of the 1st International Conference on Robotic Intelligence and Applications, Gwanju, Korea.
Crowder, J. (2010). Flexible object architectures for hybrid neural processing systems. In Proceedings of the 12th Annual International Conference on Artificial Intelligence, Las Vegas, NV.
Raskin, V., Taylor, J. M., & Hemplemann, C. F. (2010). Ausocial engineering. Concord, MA: New Security Paradigms Workshop.
Hutchinson, J., Koch, C., Luo, J., & Mead, C. (1988). Computing motion using analog and binary resistive networks. Computer, 21(3), 52–63.
Crowder, J., & Carbone, J. (2011). Recombinant knowledge relativity threads for contextual knowledge storage. In Proceedings of the 13th Annual International Conference on Artificial Intelligence, Las Vegas, NV.
Zadeh, L. (2004). A note of web intelligence, world knowledge, and fuzzy logic. Data and Knowledge Engineering, 50, 291–304.
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Crowder, J.A., Carbone, J., Friess, S. (2020). Artificial Creativity and Self-Evolution: Abductive Reasoning in Artificial Life Forms. In: Artificial Psychology. Springer, Cham. https://doi.org/10.1007/978-3-030-17081-3_6
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DOI: https://doi.org/10.1007/978-3-030-17081-3_6
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