A New Generation of Rules-based Approach: Mivar-based Intelligent Planning of Robot Actions (MIPRA) and Brains for Autonomous Robots

To create autonomous robots, both hardware and software are needed. If enormous progress has already been made in the field of equipment, then robot software depends on the development of artificial intelligence. This article proposes a solution for creating “logical” brains for autonomous robots, namely, an approach for creating an intelligent robot action planner based on Mivar expert systems. The application of this approach provides opportunities to reduce the computational complexity of solving planning problems and the requirements for the computational characteristics of hardware platforms on which intelligent planning systems are deployed. To theoretically and practically justify the expediency of using logically solving systems, in particular Mivar expert systems, to create intelligent planners, the MIPRA (Mivar-based Intelligent Planning of Robot Actions) planner was created to solve problems such as STRIPS for permutation cubes in the Blocks World domain. The planner is based on the platform for creating expert systems of the Razumator. As a result, the Mivar planner can process information about the state of the subject area based on the analysis of cause-effect relationships and an algorithm for automatically constructing logical inference (finding a solution from “Given” to “Find”). Moreover, an important feature of the MIPRA is that the system is built on the principles of a “white box”, due to which the system can explain any of its decisions and provide justification for the actions performed in the form of a retrospective of the stages of the decision-making process. When preparing a set of robot actions aimed at changing control objects, expert knowledge is used, which is the basis for the functioning algorithms of the planner. This approach makes it possible to include an expert in the process of organizing the work of the intelligent planner and use existing knowledge about the subject area. Practical experiments of this study have shown that instead of many hours and powerful multiprocessor servers, the MIPRA on a personal computer solves the planning problems with the following number of cubes: 10 cubes can be rearranged in 0.028 seconds, 100 cubes in 0.938 seconds, and 1000 cubes in 84.188 seconds. The results of this study can be used to reduce the computational complexity of solving tasks of planning the actions of robots, as well as their groups, multilevel heterogeneous robotic systems, and cyber-physical systems of various bases and purposes. Practical demonstration of MIPRA: https://mivar.org/en/about/contacts/