274 research outputs found
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Theory formation by abduction : initial results of a case study based on the chemical revolution
Abduction is the process of constructing explanations. This chapter suggests that automated abduction is a key to advancing beyond the "routine theory revision" methods developed in early AI research towards automated reasoning systems capable of "world model revision" — dramatic changes in systems of beliefs such as occur in children's cognitive development and in scientific revolutions. The chapter describes a general approach to automating theory revision based upon computational methods for theory formation by abduction. The approach is based on the idea that, when an anomaly is encountered, the best course is often simply to suppress parts of the original theory thrown into question by the contradiction and to derive an explanation of the anomalous observation based on relatively solid, basic principles. This process of looking for explanations of unexpected new phenomena can lead by abductive inference to new hypotheses that can form crucial parts of a revised theory. As an illustration, the chapter shows how some of Lavoisier's key insights during the Chemical Revolution can be viewed as examples of theory formation by abduction
Automated reasoning in metabolic networks with inhibition
International audienceThe use of artificial intelligence to represent and reason about metabolic networks has been widely investigated due to the complexity of their imbrication. Its main goal is to determine the catalytic role of genomes and their interference in the process. This paper presents a logical model for metabolic pathways capable of describing both positive and negative reactions (activations and inhibitions) based on a fragment of first order logic. We also present a translation procedure that aims to transform first order formulas into quantifier free formulas, creating an efficient automated deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive reasoning
A Bayesian Abduction Model For Sensemaking
This research develops a Bayesian Abduction Model for Sensemaking Support (BAMSS) for information fusion in sensemaking tasks. Two methods are investigated. The first is the classical Bayesian information fusion with belief updating (using Bayesian clustering algorithm) and abductive inference. The second method uses a Genetic Algorithm (BAMSS-GA) to search for the k-best most probable explanation (MPE) in the network. Using various data from recent Iraq and Afghanistan conflicts, experimental simulations were conducted to compare the methods using posterior probability values which can be used to give insightful information for prospective sensemaking. The inference results demonstrate the utility of BAMSS as a computational model for sensemaking. The major results obtained are: (1) The inference results from BAMSS-GA gave average posterior probabilities that were 103 better than those produced by BAMSS; (2) BAMSS-GA gave more consistent posterior probabilities as measured by variances; and (3) BAMSS was able to give an MPE while BAMSS-GA was able to identify the optimal values for kMPEs. In the experiments, out of 20 MPEs generated by BAMSS, BAMSS-GA was able to identify 7 plausible network solutions resulting in less amount of information needed for sensemaking and reducing the inference search space by 7/20 (35%). The results reveal that GA can be used successfully in Bayesian information fusion as a search technique to identify those significant posterior probabilities useful for sensemaking. BAMSS-GA was also more robust in overcoming the problem of bounded search that is a constraint to Bayesian clustering and inference state space in BAMSS
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Recent advances in machine learning and AI, including Generative AI and LLMs,
are disrupting technological innovation, product development, and society as a
whole. AI's contribution to technology can come from multiple approaches that
require access to large training data sets and clear performance evaluation
criteria, ranging from pattern recognition and classification to generative
models. Yet, AI has contributed less to fundamental science in part because
large data sets of high-quality data for scientific practice and model
discovery are more difficult to access. Generative AI, in general, and Large
Language Models in particular, may represent an opportunity to augment and
accelerate the scientific discovery of fundamental deep science with
quantitative models. Here we explore and investigate aspects of an AI-driven,
automated, closed-loop approach to scientific discovery, including self-driven
hypothesis generation and open-ended autonomous exploration of the hypothesis
space. Integrating AI-driven automation into the practice of science would
mitigate current problems, including the replication of findings, systematic
production of data, and ultimately democratisation of the scientific process.
Realising these possibilities requires a vision for augmented AI coupled with a
diversity of AI approaches able to deal with fundamental aspects of causality
analysis and model discovery while enabling unbiased search across the space of
putative explanations. These advances hold the promise to unleash AI's
potential for searching and discovering the fundamental structure of our world
beyond what human scientists have been able to achieve. Such a vision would
push the boundaries of new fundamental science rather than automatize current
workflows and instead open doors for technological innovation to tackle some of
the greatest challenges facing humanity today.Comment: 35 pages, first draft of the final report from the Alan Turing
Institute on AI for Scientific Discover
The Road to General Intelligence
Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book
Scientific Reasoning in Science Education: From Global Measures to Fine-Grained Descriptions of Students’ Competencies
This book is a reprint of the Special Issue "Scientific Reasoning in Science Education: From Global Measures to Fine-Grained Descriptions of Students’ Competencies" published in the journal Education Sciences. It compiles all manuscripts of the special issue
Peirce e cibernética: retrodução, erro e autopoiesis no pensamento futuro
O objetivo deste artigo Ă© associar a lĂłgica de abdução de Peirce Ă cibernĂ©tica de sistemas vivos. Sistemas vivos nĂŁo podem ser entendidos atravĂ©s de uma epistemologia causalista, pois se comportam conforme os efeitos e nĂŁo conforme as causas. A cibernĂ©tica analisou o modo pelo qual máquinas nĂŁo triviais (ou seja, máquinas capazes de se reproduzirem por auto-correção) se movem atravĂ©s de retroação, ou circuito de retorno: a cada etapa, o sistema (efetor) corrige a etapa anterior, dependendo de atĂ© onde a etapa anterior pode ir em relação a um possĂvel equilĂbrio. A dinâmica implica uma possĂvel inibição da energia excessiva seguida de um possĂvel aumento da energia insuficiente. Cada uma dessas condições Ă© um erro que se corrige automaticamente, atingindo um estado temporário de homeostase. A auto-correção significa que Ă© preciso adicionar Ă saĂda um impulso adicional "criativo" que seja singular e imprevisĂvel (ou um novo algoritmo). A lĂłgica que Ă© capaz de fazer sentido dessa dinâmica Ă© a abdução de Peirce, tambĂ©m chamada retrodução, onde o ato inferencial retroage sobre um fato incompreensĂvel, um "erro" epistemolĂłgico, inventando sua causa. O pensar e a auto-poiesis de sistemas vivos percorrem o mesmo caminho, substituindo estratĂ©gias ocasionais por causas universais atemporais. A abdução Ă© a evidĂŞncia de que Ă© possĂvel atingir uma condição de "conhecimento" sem apelar para causalidade. A possibilidade que as "leis da natureza", (a prĂłpria ciĂŞncia) possa seguir este caminho conclui o ensaio ao adicionar a noção do fĂsico Lee Smolin do "princĂpio de precedĂŞncia", conforme foi inspirado pela ideia de Peirce da evolução das leis da natureza e seu caráter temporal, ou seja, contingente
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