Discovery Science

"Reinventing Discovery argues that we are in the early days of the most dramatic change in how science is done in more than 300 years. This change is being driven by new online tools, which are transforming and radically accelerating scientific discovery"--

Described by the philosopher A.J. Ayer as a work of 'great originality and power', this book revolutionized contemporary thinking on science and knowledge. Ideas such as the now legendary doctrine of 'falsificationism' electrified the scientific community, influencing even working scientists, as well as post-war philosophy. This astonishing work ranks alongside The Open Society and Its Enemies as one of Popper's most enduring books and contains insights and arguments that demand to be read to this day.

When first published in 1959, this book revolutionized contemporary thinking about science and knowledge. It remains one of the most widely read books about science to come out of the 20th century.

These are the conference proceedings of the 4th International Conference on Discovery Science (DS 2001). Although discovery is naturally ubiquitous in s- ence, and scientific discovery itself has been subject to scientific investigation for centuries, the term Discovery Science is comparably new. It came up in conn- tion with the Japanese Discovery Science project (cf. Arikawa's invited lecture on The Discovery Science Project in Japan in the present volume) some time during the last few years. Setsuo Arikawa is the father in spirit of the Discovery Science conference series. He led the above mentioned project, and he is currently serving as the chairman of the international steering committee for the Discovery Science c- ference series. The other members of this board are currently (in alphabetical order) Klaus P. Jantke, Masahiko Sato, Ayumi Shinohara, Carl H. Smith, and Thomas Zeugmann. Colleagues and friends from all over the world took the opportunity of me- ing for this conference to celebrate Arikawa's 60th birthday and to pay tribute to his manifold contributions to science, in general, and to Learning Theory and Discovery Science, in particular. Algorithmic Learning Theory (ALT, for short) is another conference series initiated by Setsuo Arikawa in Japan in 1990. In 1994, it amalgamated with the conference series on Analogical and Inductive Inference (AII), when ALT was held outside of Japan for the first time.

The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form. Enjoying tight cooperation with the R & D community, with numerous individuals, as well as with prestigious organizations and societies, LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelligent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes proceedings (published in time for the respective conference) post-proceedings (consisting of thoroughly revised final full papers) research monographs (which may be based on PhD work) More recently, several color-cover sublines have been added featuring, beyond a collection of papers, various added-value components; these sublines include tutorials (textbook-like monographs or collections of lectures given at advance courses) state-of-the-art surveys (offering complete and mediated coverage of a topic) hot topics (introducing emergent topics to the broader community) In parallel to the printed book, each new volume is published electronically in LNCS Online. Book jacket.

This book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019. The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.

This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

Scientific discovery is often regarded as romantic and creative--and hence unanalyzable--whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws. The programs--BACON, DALTON, GLAUBER, and STAHL--are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science.