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The relationship between process parameters and the finished quality is represented in terms of rules.
Second, its knowledge is represented in terms of extended defeasible logic with modal operators.
The design case is represented in terms of sets of design parameters and associated fuzzy preferences.
The fracture process is represented in terms of a cohesive zone model.
The scalar field is represented in terms of a collection of anisotropic or axisymmetric Gaussian particles.
Irregular geometry is represented in terms of a moving, body-fitted cylindrical coordinate system.
A robust stability condition achieveing good speed of response is represented in terms of linear matrix inequalities (LMIs).
At the level of i∗, deliberation is represented in terms of goals which are satisfiable by different tasks or agents.
The fracture process is represented in terms of a traction-separation law specified on the crack plane.
As is typical for space time DG schemes, the discrete solution is represented in terms of space time basis functions.
The system is represented in terms of a Markov Decision Process (MDP), and a reinforcement learning technique based on Q-learning algorithm is used to control the process.
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