Heuristics for Spatial Data Descriptions in a Multi-Agent System.

Navigation and an agent’s map representation in a multi-agent system become problematic when agents are situated in complex environments such as the real world. Challenging modifiability of maps, long updating period, resource-demanding data collection makes it difficult for agents to keep pace with rather quickly expanding cities. This study presents the first steps to a possible solution by exploiting natural language processing and symbolic methods of supervised machine learning. An adjusted algorithm processes formalized descriptions of one’s journey to produce a description of the journey. The explication is represented employing Transparent Intensional Logic. A combination of several explications might be used as a representation of spatial data, which may help the agents to navigate. Results of the study showed that it is possible to obtain a topological representation of a map using natural language descriptions. Collecting spatial data from spoken language may accelerate updating and creation of maps, which would result in up-to-date information for the agents obtained at a rather low cost.