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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Computational intelligence 8 (1992), S. 0 
    ISSN: 1467-8640
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Computer Science
    Notes: Sacks & Doyle provide an excellent overview of the fundamental limitations of the SPQR representations for reasoning about the qualitative properties of dynamic systems. We take this opportunity to outline some new directions for qualitative reasoning. In this paper, we provide a rigorous mathematical characterization for the term “qualitative property” in the context of static and dynamic systems. Based on these characterizations, we show that interval representations are well suited for reasoning about the qualitative properties of static systems such as qualitative comparative statics and qualitative stability. Moreover, we also show that symbolic computations help in the derivation of useful global properties of dynamic systems which can be used to guide numerical sampling of differential equations. The integration of symbolic and numeric methods provides a powerful approach for automating the qualitative analysis of differential equations.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Zoomorphology 6 (1926), S. 645-693 
    ISSN: 1432-234X
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 0885-6125
    Keywords: scientific discovery ; componential models ; recovering from inconsistencies ; history of chemistry ; phlogisten theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract One of the major goals of 18th century chemistry was to determine the components of substances. In this paper we describe STAHL, a system that models significant portions of 18th century reasoning about compositional models. The system includes a number of heuristics for generating componential models from reactions, as well as error recovery mechanisms for dealing with inconsistent results. STAHL processes chemical reactions incrementally, and is therefore capable of reconstructing extended historic episodes, such as the century-long development of the phlogiston theory. We evaluate STAHL’s heuristics in the light of historical data, and conclude that the same reasoning mechanisms account for a variety of historical achievements, including Black’s models of mild alkali and Lavoisier’s oxygen theory. STAHL explains the generation of competing accounts of the same reactions, since the system’s reasoning chain depends on knowledge it has accumulated at earlier stages.
    Type of Medium: Electronic Resource
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  • 4
    ISSN: 0885-6125
    Keywords: scientific discovery ; componential models ; recovering from inconsistencies ; history of chemistry ; phlogisten theory
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract One of the major goals of 18th century chemistry was to determine the components of substances. In this paper we describe STAHL, a system that models significant portions of 18th century reasoning about compositional models. The system includes a number of heuristics for generating componential models from reactions, as well as error recovery mechanisms for dealing with inconsistent results. STAHL processes chemical reactions incrementally, and is therefore capable of reconstructing extended historic episodes, such as the century-long development of the phlogiston theory. We evaluate STAHL’s heuristics in the light of historical data, and conclude that the same reasoning mechanisms account for a variety of historical achievements, including Black’s models of mild alkali and Lavoisier’s oxygen theory. STAHL explains the generation of competing accounts of the same reactions, since the system’s reasoning chain depends on knowledge it has accumulated at earlier stages.
    Type of Medium: Electronic Resource
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  • 5
    Electronic Resource
    Electronic Resource
    Springer
    Synthese 〈Dordrecht〉 13 (1961), S. 173-174 
    ISSN: 1573-0964
    Source: Springer Online Journal Archives 1860-2000
    Topics: Natural Sciences in General , Philosophy
    Type of Medium: Electronic Resource
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  • 6
    Electronic Resource
    Electronic Resource
    Springer
    Synthese 〈Dordrecht〉 74 (1988), S. 65-90 
    ISSN: 1573-0964
    Source: Springer Online Journal Archives 1860-2000
    Topics: Natural Sciences in General , Philosophy
    Notes: Abstract New computer systems of discovery create a research program for logic and philosophy of science. These systems consist of inference rules and control knowledge that guide the discovery process. Their paths of discovery are influenced by the available data and the discovery steps coincide with the justification of results. The discovery process can be described in terms of fundamental concepts of artificial intelligence such as heuristic search, and can also be interpreted in terms of logic. The traditional distinction that places studies of scientific discovery outside the philosophy of science, in psychology, sociology, or history, is no longer valid in view of the existence of computer systems of discovery. It becomes both reasonable and attractive to study the schemes of discovery in the same way as the criteria of justification were studied: empirically as facts, and logically as norms.
    Type of Medium: Electronic Resource
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  • 7
    ISSN: 1573-0964
    Source: Springer Online Journal Archives 1860-2000
    Topics: Natural Sciences in General , Philosophy
    Notes: Abstract This paper uses a proof of Gödels theorem, implemented on a computer, to explore how a person or a computer can examine such a proof, understand it, and evaluate its validity. It is argued that, in order to recognize it (1) as Gödel's theorem, and (2) as a proof that there is an undecidable statement in the language of PM, a person must possess a suitable semantics. As our analysis reveals no differences between the processes required by people and machines to understand Gödel's theorem and manipulate it symbolically, an effective way to characterize this semantics is to model the human cognitive system as a Turing Machine with sensory inputs. La logistique n'est plus stérile: elle engendre la contradicion! – Henri Poincaré ‘Les mathematiques et la logique’
    Type of Medium: Electronic Resource
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  • 8
    ISSN: 1573-0964
    Source: Springer Online Journal Archives 1860-2000
    Topics: Natural Sciences in General , Philosophy
    Type of Medium: Electronic Resource
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  • 9
    Electronic Resource
    Electronic Resource
    Springer
    Minds and machines 7 (1997), S. 365-385 
    ISSN: 1572-8641
    Keywords: formal logic ; logic and thought ; production systems ; inference rules ; natural deduction ; tautologies
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Philosophy
    Notes: Abstract Rips, in The Psychology of Proof, argues that, through the processes of evolution, logic (e.g., modus ponens) has become established in the human mind as the basis for thinking, and that production systems rest on this foundation. In this paper we defend the converse argument that, through evolution, a production system architecture has become the basis for human thinking, and that formal logics rest on this production system and the accompanying mechanisms for recognition and search. It is through the “automaticity” of the execution of productions that we experience the compellingness of deductive arguments.
    Type of Medium: Electronic Resource
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  • 10
    Electronic Resource
    Electronic Resource
    Springer
    Foundations of science 1 (1995), S. 171-200 
    ISSN: 1572-8471
    Keywords: Machine discovery ; Heuristic search ; Concept discovery ; Psychology of discovery ; Representation ; Analogy ; Mutilated Checkerboard ; Surprise heuristic ; Learning from examples ; Intuition
    Source: Springer Online Journal Archives 1860-2000
    Topics: Natural Sciences in General
    Notes: Abstract Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an “instance space” (empirical exploration) and a “hypothesis space” (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new problem representations and new concepts, which are relatively new domains for research on discovery. Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the “blackboard” of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology.
    Type of Medium: Electronic Resource
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