  Agents, whether software or hardware, perceive their environment through
sensors and act using actuators, often operating in dynamic, partially
observable settings. They face challenges like incomplete and noisy data,
unforeseen situations, and the need to adapt goals in real-time. Traditional
reasoning and ML methods, including Reinforcement Learning (RL), help but are
limited by data needs, predefined goals, and extensive exploration periods.
Ontologies offer a solution by integrating diverse information sources,
enhancing decision-making in complex environments. This thesis introduces an
ontology-enhanced decision-making model (OntoDeM) for autonomous agents.
OntoDeM enriches agents' domain knowledge, allowing them to interpret
unforeseen events, generate or adapt goals, and make better decisions. Key
contributions include: 1. An ontology-based method to improve agents' real-time
observations using prior knowledge. 2. The OntoDeM model for handling dynamic,
unforeseen situations by evolving or generating new goals. 3. Implementation
and evaluation in four real-world applications, demonstrating its
effectiveness. Compared to traditional and advanced learning algorithms,
OntoDeM shows superior performance in improving agents' observations and
decision-making in dynamic, partially observable environments.
