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
Machine discovery in chemistry: new results
Artificial Intelligence
Knowledge-Based Learning in Exploratory Science: Learning Rules to Predict Rodent Carcinogenicity
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Computer generation of process explanations in nuclear astrophysics
International Journal of Human-Computer Studies - Special issue on collaboration, cooperation and conflict in dialogue systems
Chemical Discovery as Belief Revision
Machine Learning
The Computer-Aided Discovery of Scientific Knowledge
DS '98 Proceedings of the First International Conference on Discovery Science
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In this paper we describe some new results from ASTRA, a computational research aid for the formulation and analysis of process explanations in nuclear astrophysics. The program generates fusion and decay reactions for chemical elements by using its knowledge of quantum theory, and from these reactions constructs all theoretically possible reaction chains as process explanations for the nucleosynthesis of heavier elements. Earlier applications of ASTRA generated reactions of the elements and isotopes from hydrogen to oxygen, and found novel reactions and reaction chains for these elements. We have recently extended the system's knowledge base for the elements from oxygen to sulphur. The new applications of ASTRA generated a series of hydrogen burning and helium burning reactions involving heavier elements such as fluorine, neon, sodium, magnesium, aluminium, silicon and sulphur. The program also generated a complete series of carbon, nitrogen and oxygen burning reactions. The new results of ASTRA lead to interesting details about the origin of the elements between oxygen and sulphur.