Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Conflict Resolution as Discovery in Particle Physics
Machine Learning
Automated discovery of empirical laws
Fundamenta Informaticae - Special issue: rough sets
Scientific discovery and simplicity of method
Artificial Intelligence - Special issue on scientific discovery
From contingency tables to various forms of knowledge in databases
Advances in knowledge discovery and data mining
Machine Discovery
Rough Sets, Fuzzy Sets and Knowledge Discovery
Rough Sets, Fuzzy Sets and Knowledge Discovery
Determining Arguments of Invariant Functional Descriptions
Machine Learning
Introduction: Cognitive Autonomy in Machine Discovery
Machine Learning
An Integrated Framework for Empirical Discovery
Machine Learning
Discovery as Autonomous Learning from the Environment
Machine Learning
Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Automated Discovery of Empirical Equations from Data
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Discovering admissible models of complex systems based on scale-types and identity constraints
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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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.