scholarly journals Functorial Model Management

2019 ◽  
Vol 1 (1) ◽  
pp. 1963-1972
Author(s):  
Spencer Breiner ◽  
Blake Pollard ◽  
Eswaran Subrahmanian
Keyword(s):  
Category Theory ◽  
Multiple Representations ◽  
Model Management ◽  
Model Integration ◽  
Logical Semantics ◽  
Support Model

AbstractIn this paper we use formal tools from category theory to develop a foundation for creating and managing models in systems where knowledge is distributed across multiple representations and formats. We define a class of models which incorporate three different representations---computations, logical semantics, and data--as well as model mappings (functors) to establish relationships between them. We prove that our models support model merge operations called colimits and use these to define a methodology for model integration.

CATEGORIZATION OF DISASTER DECISION SUPPORT NEEDS FOR THE DEVELOPMENT OF AN INTEGRATED MODEL FOR DMDSS

2008 ◽  
Vol 07 (01) ◽  
pp. 115-145 ◽  
Author(s):  
SOHAIL ASGHAR ◽  
DAMMINDA ALAHAKOON ◽  
LEONID CHURILOV
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Disaster Management ◽  
Integrated Model ◽  
Model Management ◽  
Model Integration ◽  
Management Decision ◽  
Support Needs ◽  
Management Area ◽  
Disaster Scenario

The wide variety of disasters and the large number of activities involved have resulted in the demand for separate Decision Support System (DSS) models to manage different requirements. The modular approach to model management is to provide a framework in which to focus multidisciplinary research and model integration. A broader view of our approach is to provide the flexibility to organize and adapt a tailored DSS model (or existing modular subroutines) according to the dynamic needs of a disaster. For this purpose, the existing modular subroutines of DSS models are selected and integrated to produce a dynamic integrated model focussed on a given disaster scenario. In order to facilitate the effective integration of these subroutines, it is necessary to select the appropriate modular subroutine beforehand. Therefore, subroutine selection is an important preliminary step towards model integration in developing Disaster Management Decision Support Systems (DMDSS). The ability to identify a modular subroutine for a problem is an important feature before performing model integration. Generally, decision support needs are combined, and encapsulate different requirements of decision-making in the disaster management area. Categorization of decision support needs can provide the basis for such model selection to facilitate effective and efficient decision-making in disaster management. Therefore, our focus in this paper is on developing a methodology to help identify subroutines from existing DSS models developed for disaster management on the basis of needs categorization. The problem of the formulation and execution of such modular subroutines are not addressed here. Since the focus is on the selection of the modular subroutines from the existing DMDSS models on basis of a proposed needs classification scheme.

The SWS-stress/support model.

1999 ◽  
Author(s):  
Robert F. Ostermann
Keyword(s):  
Support Model

THE IMPLEMENTATION OF PROBLEM BASED LEARNING WITH MULTIPLE REPRESENTATIONS IN WORK AND ENERGY

2019 ◽  
Vol 1 (2) ◽  
pp. 95
Author(s):  
Fitria Arifiyanti
Keyword(s):  
Effect Size ◽  
Research Result ◽  
Test Validity ◽  
Multiple Representations ◽  
Problem Based Learning ◽  
Learning Model ◽  
Percentage Increase ◽  
Implementation Problem ◽  
Essay Test ◽  
Reduction Percentage

The purpose of this research was to find out the effectiveness of the implementation of problem based learning model with multiple representations to reduce the percentage of students’ difficulty in XIth Science SMAN 1 Pontianak. The research design was one group pretest-posttest design, and the instrument used was an essay test. Test reliskill (0, 5) was classified as medium, and test validity (3,56) was classified as a medium. The effect size of this research (2,18) was classified high, but the reduction percentage of the student’s difficulty (41,33%) was classified as a medium. The percentage increase in the students’ skill in multiple representations (52,38%) was classified as a medium. The research doesn’t find a significant correlation between the posttest result of students’ difficulty and the posttest result of studentS’ skill in multiple representations (C = 0,935, p = 0.348). The research result was expected to the development of the implementation problem based learning model with multiple representations approach.Keywords: Implementation, Multiple representations, Problem Based Learning

不定自然変換理論に基づく比喩理解モデルの計算論的実装の試み

2020 ◽  
Author(s):  
Shunsuke Ikeda ◽  
Miho Fuyama ◽  
Hayato Saigo ◽  
Tatsuji Takahashi
Keyword(s):  
Category Theory ◽  
Analogical Reasoning ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
Metaphor Comprehension ◽  
Weighted Directed Graph ◽  
Learning Techniques ◽  
Descriptive Validity ◽  
Association Data ◽  
Human Participants

Machine learning techniques have realized some principal cognitive functionalities such as nonlinear generalization and causal model construction, as far as huge amount of data are available. A next frontier for cognitive modelling would be the ability of humans to transfer past knowledge to novel, ongoing experience, making analogies from the known to the unknown. Novel metaphor comprehension may be considered as an example of such transfer learning and analogical reasoning that can be empirically tested in a relatively straightforward way. Based on some concepts inherent in category theory, we implement a model of metaphor comprehension called the theory of indeterminate natural transformation (TINT), and test its descriptive validity of humans' metaphor comprehension. We simulate metaphor comprehension with two models: one being structure-ignoring, and the other being structure-respecting. The former is a sub-TINT model, while the latter is the minimal-TINT model. As the required input to the TINT models, we gathered the association data from human participants to construct the ``latent category'' for TINT, which is a complete weighted directed graph. To test the validity of metaphor comprehension by the TINT models, we conducted an experiment that examines how humans comprehend a metaphor. While the sub-TINT does not show any significant correlation, the minimal-TINT shows significant correlations with the human data. It suggests that we can capture metaphor comprehension processes in a quite bottom-up manner realized by TINT.

Differentiating scalar implicature from exclusion inferences in language acquisition

2017 ◽  
Author(s):  
Jess Sullivan ◽  
Kathryn Davidson ◽  
Shirlene Wade ◽  
David Barner
Keyword(s):  
Language Acquisition ◽  
Young Children ◽  
Scalar Implicature ◽  
Scalar Implicatures ◽  
Mutual Exclusivity ◽  
Logical Semantics

When acquiring language, children must not only learn the meanings of words, but also how to interpret them in context. For example, children must learn both the logical semantics of the scalar quantifier some and its pragmatically enriched meaning: ‘some but not all’. Some studies have shown that this “scalar implicature” that some implies ‘some but not all’ poses a challenge even to nine-year-olds, while others find success by age three. We asked whether reports of children’s early successes might be due to the computation of exclusion inferences (like contrast or mutual exclusivity) rather than an ability to compute scalar implicatures. We found that young children (N=214; ages 4;0-7;11) sometimes prefer to compute symmetrical exclusion inferences rather than asymmetric scalar inferences when interpreting quantifiers. This suggests that some apparent successes in computing scalar implicature can actually be explained by less sophisticated exclusion inferences.

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