research lines

Artificial Intelligence in Education

Data Mining and Machine Learning

Agentic AI, Multiagent Systems and Intelligent Autonomous Agents

In the field of Artificial Intelligence, one of the strategies for solving complex problems is based on the use of distributed components endowed with intelligence, called agents, which in turn interact with a common environment to achieve common or conflicting objectives, giving rise to multi-agent systems. These systems, whose origins date back to the 1970s, decompose complex problems into smaller, more specific parts that perform specific tasks autonomously and intelligently. In recent years, the advent of language modeling (LLM) has transformed classical multi-agent systems into what is known as Agentic AI, where agents become more sophisticated, autonomous and strategic in their decision making thanks to this type of models.

Our group has been involved in research on multi-agent systems since the 1990s. In this line, we are currently working on projects to implement digital twins for crime, mobility and sustainability in the urban environment, combining Agentic AI with other lines of AI such as multi-agent reinforcement learning or deep learning models, and with data engineering techniques for intelligent data extraction and fusion.

Keywords: distributed AI, agent-based modeling and simulation, multiagent reinforcement learning, LLM agents, large action models

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