Behavior planning of intelligent agent with sign world model

Behavior planning is an important function of any complex technical facility intelligent control system. Presently, a symbol paradigm of artificial intelligence offers a variety of planning algorithms, including those that use precedent information, i.e. algorithms based on acquired knowledge. A symbol grounding problem within the exiting approaches of knowledge representation does not allow effective use the developed algorithms together with learning mechanisms for the purpose of solving a wide variety of applied problems by actual intelligent agents (robotics systems). This article presents the original planning algorithm (MAP Planner), which uses a sign world model as the basis for acquisition and maintenance of knowledge for future use in behavior planning. the sign problem approach describes planning as a cognitive function actualized by the world model of a subject of activity. Apart from solving symbol grounding problems and ensuring psychological and biological plausibility, a sign planning process model allows interaction of an intelligent agent with other participants in solving a cooperative task. The article presents the description of the knowledge representation method used, a MAP planning algorithm, and a model experiment in a “block world”.