SITUATION ANTICIPATION AND PLANNING FRAMEWORK FOR INTELLIGENT ENVIRONMENTS

• Published in: Radìoelektronika, informatika, upravlìnnâ no. 2; pp. 94 - 109
• Main Authors: Burov, E. V., Zhovnir, Y. I., Zakhariya, O. V, Kunanets, N. E.
• Format: Journal Article
• Language: English
• Published: 29.06.2025
• ISSN: 1607-3274; 2313-688X
• DOI: 10.15588/1607-3274-2025-2-8

Context. Situation anticipation, prediction and planning play an important role in intelligent environments, allowing to learn and predict the behavior of its users, anticipate maintenance and resource provision needs. The object of study is the process of modeling the situation anticipation and planning in the situation-aware systems.Objective. The goal of the work is to develop and analyze the ontology-based framework for modeling and predicting the situation changes for intelligent agents, allowing for proactive agent behavior.Method. This article proposes a framework for anticipation and planning based on GFO ontology. Each task or problem is considered a situoid, having a number of intermediate situations. Each task or problem is considered a situoid, having several intermediate situations. The framework is focused on the analysis of changes between situations, coming from anticipated actions or events.Contextually organized knowledge base of experiential knowledge is used to retrieve information about possible developments scenarios and is used for planning and evaluation. The framework allows to build and compare trajectories of configuration changes for specific objects, situations or situoids. The planning and anticipation process works in conditions of incomplete information and unpredicted external events, because the projections are constantly updated using feedback from sensor data and reconciliating this information with predicted model.Results. The framework for reasoning and planning situations based on GFO ontology, allowing to model spatial, temporal and structural data dependencies.Conclusions. The situation anticipation framework allows to represent, model and reason about situation dynamics in the intelligent environment, such as intelligent residential community. Prospects for further research include the elaboration of contextual knowledge storing and processing, reconciliation and learning procedures based on real-world feedback and the application of proposed framework in the real-world system, such as intelligent security systems