Semiotic network editing software for robot control systems

The problem of processing data structured as a graph is important for applications in many domains, including natural language text processing. For voice control of a robot, command interpretation is based on matching between semantic networks that describe a robot's world model and a received instruction. The simplest and most understandable way for an expert to describe such matching process is a system of inference rules that determine constructs recognizable for a robot in the results of the semantic analysis of text. Since the existing processing tools and methods are either overly complex or do not support the debugging of complex rule systems well enough, there is a need in a special solution. The proposed software tool for network data processing facilitates the development of such inference rules for modifying a world model. The joint representation of data and their processing methods is implemented within the framework of a semiotic approach, which has signs that combine information about the state of entities in the real world and possible changes in this state as the main entities of the model. Application of the semiotic approach to organizing a rule-based system allows linking data and rules within a single world model of an intelligent agent. Providing a user with a rationale for the decisions made in the form of easily interpretable lists of applied parameterized rules facilitates extension, debugging and maintenance of the system. The developed software can also be useful in other domains where it is convenient to describe the system state modification by logical inference.