Structuring intelligence, How Knowledge representation and reasoning shape decision-making systems

Room C005

In today’s data-driven world, organizations face unprecedented complexity and volatility, making adaptive, informed and explained decisions more vital than ever. Intelligent decision systems needs a structured knowledge representation (ontologies and expert models) in order to organize information and capture domain expertise, and to generate, and evaluate optimal strategies in real time. Ontologies not only enable efficient access to relevant knowledge but also support advanced reasoning processes. Hybrid Artificial intelligence architectures, particularly multi-agent systems combined with ontologies, further enhance decision-making by distributing knowledge, reasoning, and control among autonomous, interactive agents. These agents can collaborate, negotiate, and adapt to evolving situations, ensuring flexibility, scalability, and robustness in complex socio-technical environments. By integrating ontological knowledge representation with the decentralized intelligence of multi-agent systems, we can design decision support systems that are both powerful and adaptive, capable of tackling real-world challenges across diverse domains. This seminar will analyze the convergence of these approaches in the development of intelligent decision systems, emphasizing research works in knowledge engineering and artificial intelligence architectures, and incorporating empirical insights derived from practical implementations.