The book Model-Based Reasoning in Scientific Discovery, aims to explain how specific modeling practices employed by scientists are productive methods of ...

The total irrelevance of absolute space to scientific observation and experiment led him early to a most radical conclusion: experience cannot teach us anything about the true structure of space; consequently, the choice of a geometry for the ...

The study of creative, diagnostic, visual, spatial, analogical, and temporal reasoning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of traditional notions of reasoning such as classical logic. Understanding the contribution of modeling practices to discovery and conceptual change in science requires expanding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic (...) ways of reasoning is situated at the crossroads of philosophy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model-based reasoning. The term "model" comprises both internal and external representations. The models are intended as interpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain. Moreover, in the modeling process, various forms of abstraction are used. Evaluation and adaptation take place in light of structural, causal, and/or functional constraints. Model simulation can be used to produce new states and enable evaluation of behaviors and other factors. Several of the papers in this volume aim at increasing epistemological knowledge about the role of model-based reasoning in various scientific tasks, other papers address fundamental cognitive issues related to model-based reasoning and illustrate novel analyses of cognitive "logical" models of model-based reasoning and of the interplay abduction/model-based reasoning/creative inferences. The volume is based on the papers that were presented at the International Conference Model-Based Reasoning in Science and Engineering: Abduction, Visualization, Simulation, held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 2004. (shrink)

First of all I would like to describe inductive and abductive reasoning in the light of the agent–based framework to the aim of clarifying their fallacious character and the role of the so-called ideal systems (logical and computational). Then I will analyze some inductive and abductive types of reasoning that in the perspective of classical and informal logic are defined fallacies. I will describe how in an agent-based reasoning this kind of fallacious reasoning can in some cases be redefined and (...) considered as a good way of reasoning. Finally, I will illustrate how what I call manipulative abduction can be interpreted as a form of practical reasoning a better understanding of which can furnish a description of human beings as hybrid reasoners in so far they are users of ideal and computational agents. (shrink)

From the perspective of distributed cognition I will stress how abduction is essentially multimodal, in that both data and hypotheses can have a full range of verbal and sensory representations, involving words, sights, images, smells, etc., but also kinesthetic – related to the ability to sense the position and location and orientation and movement of the body and its parts – and motor experiences and other feelings such as pain, and thus all sensory modalities. The presence of kinesthetic and motor (...) aspects demonstrates that abductive reasoning can be manipulative. We can also see, in this regard, how implicit factors take part in the abductive procedure, which consequently acquires the character of a kind of “thinking through doing”. This paper further describes 1) the fact that hypotheses in science can be built through different cognitive mediators and so they can also model the same cognitive aspect in different ways; how they can be carriers/ producers of knowledge in a multimodal way; 2) the problem of the possible non-explanatory and instrumental nature of abductive reasoning and the analysis of the consequences for induction; 3) the role of manipulative abduction in building new evidence/ experiments and how they trigger smart inductive inferences. (shrink)

In this paper we present some algorithms for computing minimal conflicts. First of all we discuss the relationship between minimal conflicts and minimally inconsistent subsets. Then we introduce an algorithm for computing all minimally inconsistent subsets, which is applied to generating all minimal conflicts. Furthermore, an algorithm for computing all minimal conflicts using structured description is introduced, and its correctness is proved; its time complexity is also shown. The algorithm using structured description terminates in polynomial time for some special system, (...) such as the tree–structured systems. The algorithms are also complared with related works. (shrink)

In this article I will take advantage of the logical and cognitive studies I have illustrated in my recent book The Abductive Structure of Scientific Creativity. An Essay on the Ecology of Cognition, in which the process of building new hypotheses is clarified thanks to my eco-cognitive model of abduction. Also resorting to a new interpretation of Aristotle’s seminal work on abduction, I will emphasize the crucial role played in abductive cognition by the so-called “optimization of eco-cognitive openness and situatedness”. (...) Indeed, in the chapter B25 of Prior Analytics concerning, we can see some of the current well-known distinctive characters of abductive cognition already expressed, which are in tune with the EC-Model: Aristotle is still pointing to the fundamental inferential role in reasoning of those externalities that substantiate the process of “leading away” (). Hence, we can gain a new positive perspective about the “constitutive” eco-cognitive character of abduction, just thanks to Aristotle himself. Situatedness is related to eco-cognitive aspects: to favor the solution of the abductive problem input and output of the formula Λ1,…,Λi,?I⊩LXΥ1,…,.Υj\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \Lambda _1,\ldots,\Lambda _i,?_I\Vdash _L^X\Upsilon _1,\ldots,.\Upsilon _j \end{aligned}$$\end{document}have to be thought as optimally positioned: indeed I also contend than a disregarded issue concerning abduction is related to the current lack of knowledge about what I call “discoverability” and “diagnosticability”. In the formula above ⊩LX\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Vdash _L^X$$\end{document} indicates that inputs and outputs do not stand each other in an expected relation and that the modification of the inputs?I\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$?_I$$\end{document} can provide the abductive solution. In general, in this characterization the direction is not from evidence/premises to abductive result but the forward fashion is adopted, where the inferential parameter ⊩\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Vdash $$\end{document} sets some appropriate logical relationship between an input which consists in both the abductive guess to be found and a background theory, and an output—for example an evidence, a novel phenomenon to be abductively “explained” through facts, rules, or even new theories. Further, in the case of scientific settings, this optimality is made possible by a maximization of changeability of both input and output: not only inputs have to be enriched with the possible solution but, to do that, other inputs have usually to be changed and/or modified. This changeability first of all refers to a wide epistemological openness. (shrink)

Le sentenze che oggi vengono pronunciate contro i criminali psicopatici evitano accuratamente di far leva su considerazioni “morali”. Solitamente l’attribuzione di responsabilità morale ai criminali si basa spesso sul concetto cognitivo di infermità mentale, in maniera tale che il giudizio morale sulla condotta morale dei “criminali psicopatici” in questi casi venga tendenzialmente sterilizzato. La posizione che qui vorrei proporre individua oscurità e limiti epistemici nelle teorie e nei metodi correntemente impiegati nelle società occidentali per alleggerire le responsabilità morali, le quali, (...) pertanto, risultano in parte inaffidabili. Per supportare questa conclusione intendo far leva sui miei più recenti studi di carattere cognitivo, vertenti sulla dimensione plurale dei contesti morali, sulla co-evoluzione geni/nicchia cognitiva e sul concetto di libero arbitrio. (shrink)

L. Albertazzi, G. J. van Tonder, and D. Vishwanath (eds): Perception Beyond Inference: The Information Content of Visual Processes Content Type Journal Article Pages 53-55 DOI 10.1007/s11023-011-9253-z Authors Lorenzo Magnani, Department of Philosophy and Computational Philosophy Laboratory, University of Pavia, Pavia, Italy Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.

This is an excerpt from the contentMost academics have heard of deduction and induction, but much less familiar is the kind of inference that the American philosopher Charles Peirce called abduction. Abductive inference is the generation and evaluation of explanatory hypotheses, a kind of reasoning that is far more common and important than deductions, which rarely occur outside of mathematics, and inductions from examples to generalizations. Lorenzo Magnani has produced a magnum opus on abduction that brilliantly spans its philosophical, (...) neuropsychological, computational, and ecological dimensions. In this review I will briefly highlight some of the substantial contributions of Abductive Cognition and raise a few objections to it. I finish by briefly discussing the relevance of abduction to concerns about mind and society. Magnani’s treatise is by far the most comprehensive discussion of abductive thinking to date. In the first chapter, he reviews various ways of thinking about abduction, ranging from sentential approac. (shrink)

This volume sets out to give a philosophical "applied" account of violence, engaged with both empirical and theoretical debates in other disciplines such as cognitive science, sociology, psychiatry, anthropology, political theory, ...

Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as (...) AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by _locked_ abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead _unlocked_. (shrink)

This book contains contributions presented during the international conference on Model-Based Reasoning (MBR'012), held on June 21-23 in Sestri Levante, Italy. Interdisciplinary researchers discuss in this volume how scientific cognition and other kinds of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. Some of the contributions analyzed the problem of model-based reasoning in technology and stressed the issues of scientific and technological innovation. The book is divided in (...) three main parts: models, mental models, representations; abduction, problem solving and practical reasoning; historical, epistemological and technological issues.. (shrink)