Abstract
After two decades of research on automated discovery, many principles are shaping up as a foundation of discovery science. In this paper we view discovery science as automation of discovery by systems who autonomously discover knowledge and a theory for such systems. We start by clarifying the notion of discovery by automated agent. Then we present a number of principles and discuss the ways in which different principles can be used together. Further augmented, a set of principles shall become a theory of discovery which can explain discovery systems and guide their construction. We make links between the principles of automated discovery and disciplines which have close relations with discovery science, such as natural sciences, logic, philosophy of science and theory of knowledge, artificial intelligence, statistics, and machine learning.
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References
1995 Working Notes AAAI Spring Symposium on Systematic Methods of Scientific Discovery. Stanford, March 27–29.
1998 ECAI Workshop on Scientific Discovery. Brighton, August 24.
1999 AISB Symposium on Scientific Creativity. Edinburgh. April 6–9.
Chaudhuri, S. & Madigan, D. eds. 1999. Proceedings of the Fifth ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining, ACM, New York.
Dzeroski, S. & Todorovski, L. 1993. Discovering Dynamics, Proc. of 10th International Conference on Machine Learning, 97–103
Edwards, P. ed. 1993. Working Notes MLNet Workshop on Machine Discovery. Blanes, Spain.
Kocabas, S. 1991. Conflict Resolution as Discovery in Particle Physics. Machine Learning, 6, 277–309.
Kocabas, S. & Langley, P. 1995. Integration of research tasks for modeling discoveries in particle physics. Working notes of the AAI Spring Symposium on Systematic Methods of Scientific Discovery, Stanford, CA, AAAI Press. 87–92.
Kokar, M.M. 1986. Determining Arguments of Invariant Functional Descriptions, Machine Learning, 1, 403–422.
Komorowski, J. & Zytkow, J.M. 1997. Principles of Data Mining and Knowledge Discovery, Springer.
Kulkarni, D., & Simon, H.A. 1987. The Process of Scientific Discovery: The Strategy of Experimentation, Cognitive Science, 12, 139–175
Langley, P., Simon, H.A., Bradshaw, G., & Zytkow J.M. 1987. Scientific Discovery; Computational Explorations of the Creative Processes. Boston, MIT Press.
Nordhausen, B., & Langley, P. 1993. An Integrated Framework for Empirical Discovery. Machine Learning 12, 17–47.
Piatetsky-Shapiro, G. ed. 1993. Proc. of AAAI-93 Workshop on Knowledge Discovery in Databases.
Piatetsky-Shapiro, G. & Frawley, W. eds. 1991. Knowledge Discovery in Databases, Menlo Park, Calif.: AAAI Press.
Scott, P.D., Markovitch, S. 1993. Experience Selection and Problem Choice In An Exploratory Learning System. Machine Learning, 12, p.49–67.
Shen, W.M. 1993. Discovery as Autonomous Learning from Environment. Machine Learning, 12, p.143–165.
Shrager J., & Langley, P. eds. 1990. Computational Models of Scientific Discovery and Theory Formation, Morgan Kaufmann, San Mateo:CA
Simon, H.A. 1979. Models of Thought. New Haven, Connecticut: Yale Univ. Press.
Simon, H.A., Valdes-Perez, R. & Sleeman, D. eds. 1997. Artificial Intelligence 91, Special Issue: Scientific Discovery.
Valdés-Pérez, R.E. 1993. Conjecturing hidden entities via simplicity and conservation laws: machine discovery in chemistry, Artificial Intelligence, 65, 247–280.
Valdés-Pérez, R.E. 1994. Human/computer interactive elucidation of reaction mechanisms: application to catalyzed hydrogenolysis of ethane, Catalysis Letters, 28, p.79–87.
Washio, T., & Motoda, H. 1997, Discovering Admissible Models of Complex Systems Based on Scale-Types and Identity Constraints, Proc. IJCAI’97, 810–817.
Wu, Y. and Wang, S. 1989. Discovering Knowledge from Observational Data, In: Piatetsky-Shapiro, G. (ed.) Knowledge Discovery in Databases, IJCAI-89 Workshop Proceedings, Detroit, MI, 369–377.
Zembowicz, R. & Żytkow, J.M. 1991. Automated Discovery of Empirical Equations from Data. In Ras. Z. & Zemankova M. eds. Methodologies for Intelligent Systems, Springer-Verlag, 1991, 429–440.
Zembowicz, R. & Żytkow, J.M. 1996. From Contingency Tables to Various Forms of Knowledge in Databases, in Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. & Uthurusamy eds. Advances in Knowledge Discovery & Data Mining, AAAI Press. 329–349.
Ziarko, W. ed. 1994. Rough Sets, Fuzzy Sets and Knowledge Discovery, Workshops in Computing, Springer-Verlag.
Żytkow, J.M. & Quafafou, M. 1998. Principles of Data Mining and Knowledge Discovery, Springer.
Żytkow, J.M. ed. 1992. Proceedings of the ML-92 Workshop on Machine Discovery (MD-92). National Institute for Aviation Research, Wichita, KS.
Żytkow, J.M. ed. 1993 Machine Learning, 12.
Żytkow, J.M. 1996. Automated Discovery of Empirical Laws, Fundamenta Informaticae, 27, p.299–318.
Żytkow, J.M. ed. 1997 Machine Discovery, Kluwer.
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Żytkow, J.M. (1999). The Melting Pot of Automated Discovery: Principles for a New Science. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_1
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DOI: https://doi.org/10.1007/3-540-46846-3_1
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