Features of Context-Redefined Language Synthesis for Intelligent Learning Agents (Resource Consumption Behavior Prediction Tasks)
Abstract
Features of Context-Redefined Language Synthesis for Intelligent Learning Agents (Resource Consumption Behavior Prediction Tasks)
Incoming article date: 14.11.2018This work deals with the design and application questions of context-redefined computer languages for new information technologies. Realization problems of such languages are discussed for intelligent learning agents (ILA), which were applied for solving of resource consumption behavior prediction tasks in communal services. The approach is in the application of context-redefined language and it support system for problem solution. We concentrate attention to principal unpredicted changing of source function algorithms. Built-in context-redefined computer language is an essential tool for this kind algorithms support. We interested in context-redefined language synthesis. This language is used for the conditions and methods context forming for every component of intelligent agent. We pay extra attention to methods of constructive function interpretation, which can be varied or can be also changed. This synthesis is based on the interest to prediction system demands and their variations during functioning. The main idea of built-in language synthesis is to use main parts of the algorithm for ILA components with proper modification by means another algorithms and context connection. Due to this connection, the original algorithm can be changed directly or indirectly in the process of ILA functioning. As a result, We have to extract changing parts of component algorithms and organize proper interaction between every part and the context which can change it directly or indirectly. Required adaptive algorithm variation takes place on the base of obtained knowledge. At the same time, the algorithm must be implemented as quickly as possible, and the language must be simple and clear. Algorithm efficiency is based on flexibility and modifiability of the language.
Keywords: programming languages, embedded languages, context-redefined languages, intelligent agents, computer languages