DEVELOPMENT OF A DYNAMIC ONTOLOGY-BASED FRAMEWORK FOR ENHANCING THE INTELLIGENCE AND AUTONOMY OF ROBOTIC SYSTEMS

Abstract

In recent years, intelligent agents driven by ontologies have shown significant promise in learning from their experiences and adapting to dynamic environments. These agents have been successfully applied to a variety of domains, including autonomous systems, robotics, and decision support systems, where their ability to process and adapt to changing circumstances is crucial. However, despite their potential, these agents often encounter significant challenges when confronted with unpredictable or hostile conditions, such as high uncertainty, evolving adversarial behaviors, or environmental disturbances. In such scenarios, traditional models may struggle to maintain effective learning and decision-making, leading to suboptimal performance and failure to achieve desired outcomes. This limitation arises from the inability of many existing models to fully account for anomalies or unexpected changes that deviate from anticipated patterns. As a result, the agents' capacity for robust decision-making and adaptability is compromised, hindering their performance in real-world applications. Addressing these challenges requires models that are not only capable of learning from experience but also flexible enough to deal with unexpected events and changes in the environment. To overcome these limitations, this thesis introduces a novel dynamic, ontology-driven agent model that emphasizes continuous learning from past experiences. The model integrates an adaptive reasoning framework capable of adjusting its strategies and planning processes in response to new, unforeseen challenges. By leveraging the rich contextual information embedded in ontologies, the proposed agent model can enhance its action planning and reasoning abilities, enabling it to identify and adapt to anomalies more effectively. Furthermore, the model is designed to improve its adaptability by dynamically updating its ontological knowledge base based on real-time data, ensuring that it remains resilient in hostile and unpredictable environments. This research seeks to advance the field of intelligent agents by proposing a more robust and adaptive framework that not only learns from past experiences but also evolves and adjusts its strategies in response to novel and challenging conditions. The expected outcome is a highly adaptable agent model capable of improving decision-making, planning, and performance in dynamic, uncertain, and adversarial environments, thus expanding the potential applications of intelligent agents across various complex domains.