Efficient Translation of Safety LTL to DFA Using Symbolic Automata Learning and Inductive Inference

This chapter introduces the book, describes the key arguments of each chapter, and summarizes the master argument for medical nihilism. It offers a brief survey of prominent articulations of medical nihilism throughout history, and describes the contemporary evidence-based medicine movement, to set the stage for the skeptical arguments. The main arguments are based on an analysis of the concepts of disease and effectiveness, the malleability of methods in medical research, and widespread empirical findings which suggest that many medical interventions are barely effective. The chapter-level arguments are unified by our best formal theory of inductive inference in what is called the master argument for medical nihilism. The book closes by considering what medical nihilism entails for medical practice, research, and regulation.

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Accelerating Legacy String Kernels via Bounded Automata Learning

Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems ◽

10.1145/3373376.3378503 ◽

2020 ◽

Author(s):

Kevin Angstadt ◽

Jean-Baptiste Jeannin ◽

Westley Weimer

Keyword(s):

String Kernels ◽

Automata Learning

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Naturalizing Logic: How Knowledge of Mechanisms Enhances Inductive Inference

Philosophies ◽

10.3390/philosophies6020052 ◽

2021 ◽

Vol 6 (2) ◽

pp. 52

Author(s):

Paul Thagard

Keyword(s):

Causal Inference ◽

Scientific Knowledge ◽

Inductive Inference ◽

Counterfactual Reasoning

This paper naturalizes inductive inference by showing how scientific knowledge of real mechanisms provides large benefits to it. I show how knowledge about mechanisms contributes to generalization, inference to the best explanation, causal inference, and reasoning with probabilities. Generalization from some A are B to all A are B is more plausible when a mechanism connects A to B. Inference to the best explanation is strengthened when the explanations are mechanistic and when explanatory hypotheses are themselves mechanistically explained. Causal inference in medical explanation, counterfactual reasoning, and analogy also benefit from mechanistic connections. Mechanisms also help with problems concerning the interpretation, availability, and computation of probabilities.

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