Experimenting and theorizing in theory formation
ISMIS '86 Proceedings of the ACM SIGART international symposium on Methodologies for intelligent systems
Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Data-driven approaches to empirical discovery
Artificial Intelligence
A robust approach to numeric discovery
Proceedings of the seventh international conference (1990) on Machine learning
Application of empirical discovery in knowledge acquisition
EWSL-91 Proceedings of the European working session on learning on Machine learning
Determining repeatability and error in experimental results by a discovery system
Methodologies for intelligent systems, 5
Scientific discovery: a view from the trenches
DS'06 Proceedings of the 9th international conference on Discovery Science
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An architecture which controls automated discovery of empirical knowledge must integrate a robotic component which interacts with the real world, knowledge network which accumulates knowledge, a discovery method, and the currently executed discovery tasks. We describe such an architecture implemented in the FAHRENHEIT system, concentrating on the integration and interaction between elements. The robotic component includes hardware connections to the external instruments and manipulators, operational procedures to control the hardware, and a process that controls outstanding requests for measurements and manipulations. Knowledge network describes the actual state of knowledge about the world, including all discoveries that have been made. The state of knowledge expands as the discovery process continues. Operational procedures are expanded and refined as a result of discoveries. Better procedures, in turn, allow FAHRENHEIT to collect better data and to improve its knowledge. The discovery method consists of a network of generic goals and plans for goal execution. Discovery goals require search, so most of the generic plans are solution schemes to search problems. All search problems are defined by the application of a uniform search template. Actual goals and plans executed at any given time form a recursive network of instances of generic goals and plans. The instances are selected based on the generic network of goals and plans, and the current state of knowledge. The selected plans are search instances. At any given time a number of search instances of various types may be in progress, each of them plan leading to new discoveries.