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)

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)

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)