
Knowledge engineering mixedinteger linear programming: constraint typology
In this paper, we investigate the constraint typology of mixedinteger l...
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Mathematical Knowledge Representation: Semantic Models and Formalisms
The paper provides a survey of semantic methods for solution of fundamen...
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OAK: OntologyBased Knowledge Map Model for Digital Agriculture
Nowadays, a huge amount of knowledge has been amassed in digital agricul...
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Dire n'est pas concevoir
The conceptual modelling built from text is rarely an ontology. As a mat...
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An Investigation into Mathematical Programming for Finite Horizon Decentralized POMDPs
Decentralized planning in uncertain environments is a complex task gener...
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Interoperability and machinetomachine translation model with mappings to machine learning tasks
Modern largescale automation systems integrate thousands to hundreds of...
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Multiscale modelling and simulation of physical systems as semiosis
It is explored how physicalist mereotopology and Peircean semiotics can ...
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A Knowledge Representation Approach to Automated Mathematical Modelling
Mathematicians formulate complex mathematical models based on user requirements to solve a diverse range of problems in different domains. These models are, in most cases, represented through several mathematical equations and constraints. This modelling task comprises several timeintensive processes that require both mathematical expertise and (problem) domain knowledge. In an attempt to automate these processes, we have developed an ontology for Mixed Integer Linear Programming (MILP) problems to formulate expert mathematician knowledge and in this paper, we show how this new ontology can be utilized for modelling a relatively straightforward MILP problem, a Machine Scheduling example. We also show that more complex MILP problems, such as the Asymmetric Travelling Salesman Problem (ATSP), however, are not readily amenable to simple elicitation of user requirements and the utilization of the proposed mathematical model ontology. Therefore, an automatic mathematical modelling framework is proposed for such complex MILP problems, which includes a problem (requirement) elicitation module connected to a model extraction module through a translation engine that bridges between the nonexpert problem domain and the expert mathematical model domain. This framework is argued to have the necessary components to effectively tackle the automation of modelling task of the more intricate MILP problems such as the ATSP.
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