Algorithms for Runtime Generation of Homogeneous Classes of Objects
This work provides incremental improvements for developers of knowledge-based intelligent systems by offering specific algorithms for runtime class generation in object-oriented dynamic networks.
The paper tackled the problem of dynamically generating new classes of objects during program execution by developing algorithms for union, intersection, difference, and symmetric difference operations on homogeneous classes, which can be integrated into knowledge-based intelligent systems using object-oriented dynamic networks.
This paper contains analysis of main modern approaches to dynamic code generation, in particular generation of new classes of objects during program execution. The main attention was paid to universal exploiters of homogeneous classes of objects, which were proposed as a part of such knowledge representation model as object-oriented dynamic networks, as the tools for generation of new classes of objects in program runtime. As the result, algorithms for implementation of such universal exploiters of classes of objects as union, intersection, difference and symmetric difference were developed. These algorithms can be used knowledge-based intelligent systems, which are based on object-oriented dynamic networks, and they can be adapted for some object-oriented programming languages with powerful metaprogramming opportunities.