Planning polyphase behavior of autonomous intelligent mobile systems in uncertain environments

Abstract

Introduction: We discuss the modern ways of developing intelligent problem solvers, focusing on their shortcomings in terms of the efficiency of their application for planning purposeful behavior of autonomous mobile intelligent systems in a priori undescribed conditions of a problem environment. Purpose: Developing a model of knowledge representation and processing which would provide the ways to organize purposeful activity of autonomous intelligent mobile systems in uncertain environment. Methods: Synthesis of frame-like behavior scenarios in the form of polyvariable conditionally dependent predicates whose structure includes complex variables as well as related variables of types “object”, “event” and “relationship”; synthesis of heuristic rules for knowledge representation in the process of purposeful behavior planning. In order to represent complex variables in polyvariable conditionally dependent predicates, fuzzy semantic networks are used which can represent knowledge of variously purposed intelligent systems without regard to particular knowledge domains, being adaptable to a priori undescribed operational conditions. Results: We have proposed a structure of various polyvariable conditionally dependent predicates. On their base, an autonomous intelligent mobile system can organize various activities in a priori undescribed and unstable problem environments. Specially developed knowledge processing tools allow such a system to automatically plan its purposeful behavior in a space of subtasks during the fulfilment of tasks formulated for it. Practical relevance: The obtained results can be efficiently used in building intelligent problem solvers for autonomous intelligent mobile systems of various purpose, capable of performing complex tasks in a priori undescribed operational conditions.