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681.3*I26 --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32} --- 681.3*I26 Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32}
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This unique book on intelligence analysis covers several vital but often overlooked topics. It teaches the evidential and inferential issues involved in 'connecting the dots' to draw defensible and persuasive conclusions from masses of evidence: from observations we make, or questions we ask, we generate alternative hypotheses; we make use of our hypotheses to generate new lines of inquiry and evidence; and we test the hypotheses on the basis of the evidence we are discovering. To facilitate the learning of these issues and enable the performance of complex analyses, the book introduces an intelligent analytical tool, called Disciple-CD. Readers will practice with Disciple-CD and learn how to formulate hypotheses; develop arguments that reduce complex hypotheses to simpler ones; collect evidence to evaluate the simplest hypotheses; assess the relevance, believability, and inferential force of evidence; and finally judge the probability of the hypotheses.
Intelligence service --- Evidence. --- Inference. --- Reasoning --- Argumentation --- Ratiocination --- Reason --- Thought and thinking --- Judgment (Logic) --- Logic --- Ampliative induction --- Induction, Ampliative --- Inference (Logic) --- Proof --- Belief and doubt --- Faith --- Philosophy --- Truth --- Counter intelligence --- Counterespionage --- Counterintelligence --- Intelligence community --- Secret police (Intelligence service) --- Public administration --- Research --- Disinformation --- Secret service --- Methodology. --- Data processing.
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This book presents a significant advancement in the theory and practice of knowledge engineering, the discipline concerned with the development of systems that use expert knowledge and reasoning to solve complex problems. It covers the main stages in the development of a knowledge-based system: understanding the application domain, modeling problem solving in that domain, developing the ontology and the reasoning rules, and testing the system. The book focuses on a special class of systems - learning assistants for evidence-based reasoning that learn complex problem solving expertise directly from human experts, support experts and non-experts in problem solving and decision making, and teach their problem solving expertise to students. A powerful learning agent shell, Disciple-EBR, is included with the book, enabling students, practitioners, and researchers to rapidly develop learning assistants in a wide variety of domains that require evidence-based reasoning, including intelligence analysis, cyber security, law, forensics, medicine, and education.
Expert systems (Computer science) --- Knowledge, Theory of --- Computational learning theory. --- A priori --- Knowledge-based systems (Computer science) --- Systems, Expert (Computer science) --- Artificial intelligence --- Computer systems --- Soft computing --- Apriori --- Logic --- Reasoning --- Machine learning --- Epistemology --- Theory of knowledge --- Philosophy --- Psychology --- Data processing.
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Expert systems (Computer science) --- Knowledge, Theory of --- Computational learning theory --- A priori --- Data processing --- Data processing
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