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Wyszukujesz frazę "reasoning algorithm" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Reasoning algorithm for a creative decision support system integrating inference and machine learning
Autorzy:
Wilk-Kolodziejczyk, D.
Powiązania:
https://bibliotekanauki.pl/articles/305355.pdf
Data publikacji:
2017
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
reasoning algorithm
inferential theory of learning
decision support
rule induction
logic of plausible reasoning
Opis:
In this paper a reasoning algorithm for a creative decision support system is proposed. It allows to integrate inference and machine learning algorithms. Execution of learning algorithm is automatic because it is formalized as aplying a complex inference rule, which generates intrinsically new knowledge using the facts stored already in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. Such a solution makes the reasoning process more creative and allows to continue resoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate knowledge representation and infeence model are proposed. Experimental verification is performed on a decision support system in a casting domain.
Źródło:
Computer Science; 2017, 18 (3); 317-338
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A proposed Evidential Reasoning (ER) Methodology for Quantitative Assessment of Non-Technical Skills (NTS) Amongst Merchant Navy Deck Officers in a Ship’s Bridge Simulator Environment
Autorzy:
Saeed, F.
Bury, A.
Bonsall, S.
Riahi, R.
Powiązania:
https://bibliotekanauki.pl/articles/116485.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
Evidential Reasoning (ER)
quantitative assessment
methodology for quantitative assessment
Non-Technical Skills (NTS)
merchant navy deck officers
ship’s bridge simulator
evidential reasoning algorithm
simulated bridge environment
Opis:
Ship’s bridge simulators are very popular in the worldwide training and assessment of merchant navy deck officers. The examiners of simulator courses presently do not have a method to quantitatively assess the performance of a group or an individual. Some examiners use checklists and others use their gut feeling to grade competence. In this paper a novel methodology is established that uses the Evidential Reasoning algorithm to quantitatively assess the Non-Technical Skills (NTS) of merchant navy officers. To begin with, interviews were conducted with experienced deck officers to develop the taxonomy and behavioural markers that would be used in the assessment process. A random selection of students studying towards their Chief Officer’s Certificate of Competency were recruited to have their NTS to be observed in a ship’s bridge simulator. The participant’s behaviour was rated against five criteria and the subsequent data was entered into the Evidential Reasoning algorithm to produce a crisp number. The results that were generated demonstrate that this approach provides a reliable method to quantitatively assess the NTS performance of merchant navy officers in a simulated bridge environment.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2018, 12, 3; 597-608
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Computational and Pragmatic Approach to the Dynamics of Science
Autorzy:
Marciszewski, Witold
Powiązania:
https://bibliotekanauki.pl/articles/41310393.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Filozofii i Socjologii PAN
Tematy:
algorithm
behavioral (vs declarative) knowledge
computability
corroboration
innate knowledge
intuition
invention
logic gates
oracle
pragmatic (vs classical) rationalism
problem-solving
reasoning
symbolic logic
Turing machine
Opis:
Science means here mathematics and those empirical disciplines which avail themselves of mathematical models. The pragmatic approach is conceived in Karl R. Popper’s The Logic of Scientific Discovery (p. 276) sense: a logical appraisal of the success of a theory amounts to the appraisal of its corroboration. This kind of appraisal is exemplified in section 6 by a case study—on how Isaac Newton justified his theory of gravitation. The computational approach in problem-solving processes consists in considering them in terms of computability: either as being performed according to a model of computation in a narrower sense, e.g., the Turing machine, or in a wider perspective—of machines associated with a non-mechanical device called “oracle” by Alan Turing (1939). Oracle can be interpreted as computertheoretic representation of intuition or invention. Computational approach in another sense means considering problem-solving processes in terms of logical gates, supposed to be a physical basis for solving problems with a reasoning. Pragmatic rationalism about science, seen at the background of classical rationalism (Descartes, Gottfried Leibniz etc.), claims that any scientific idea, either in empirical theories or in mathematics, should be checked through applications to problem-solving processes. Both the versions claim the existence of abstract objects, available to intellectual intuition. The difference concerns the dynamics of science: (i) the classical rationalism regards science as a stationary system that does not need improvements after having reached an optimal state, while (ii) the pragmatical version conceives science as evolving dynamically due to fertile interactions between creative intuitions, or inventions, with mechanical procedures. The dynamics of science is featured with various models, like Derek J. de Solla Price’s exponential and Thomas Kuhn’s paradigm model (the most familiar instances). This essay suggests considering Turing’s idea of oracle as a complementary model to explain most adequately, in terms of exceptional inventiveness, the dynamics of mathematics and mathematizable empirical sciences.
Źródło:
Filozofia i Nauka; 2020, 8, 1; 31-67
2300-4711
2545-1936
Pojawia się w:
Filozofia i Nauka
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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