Complex systems are often difficult to understand because of various issues that influence the behavior and interaction of entities in the system. Agent based simulation is a computational approach for modelling these complex systems by modelling individuals as software agents. This provides us with an opportunity to explain behaviour in complex systems as well as predict how minor or large changes in e.g. regulation can affect the overall system. We investigate how large scale complex agent based simulations can be developed.
The study of argumentation concerns two aspects that both provide natural mechanisms with regards to specific aspects of argumentation: reasoning and dialogue. The former makes explicit the reasons for conclusions and how conflicting reasons are resolved. The latter concerns negotiating or collaborating agents with conflicting opinions. We study both aspects of argumentation as well as its application in different domains.
Emotion is generally understood as a (cognitive) mechanism that directs an agent’s thought and attention to what is relevant, important, and significant. Such a mechanism is crucial for the design of software agents that must operate in highly-dynamic, semi-predictable environments and which need mechanisms for allocating their computational resources efficiently.
Engineering Multi-Agent Systems
The development of multi-agent systems requires the development of individual agents, multi-agent organizations, and multi-agent environments. Our group develops programming languages and frameworks that supports support the development and engineering of multi-agent systems.
The world of software systems increasingly consists of autonomous software agents that interact in open, dynamic and unpredictable environments. In such environments it is often impossible to anticipate on all interactions beforehand. Therefore, intelligent adaptation of behaviour among artificial agents is a key research issue in artificial intelligence and agent technology. Research in multi-agent learning (MAL) studies software agents that learn and adapt to the behaviour of other software agents.
Logics for Intelligent Agents and Multi-Agent Systems
We have been involved in the field of logics for intelligent agents and multi-agent systems since its inception in the beginning of the 1990’s. Since those early days, it has become a popular field in between computer science and artificial intelligence (AI). We investigate and develop various logics and formal tools that support specification and verification of autonomous agents and multi-agent systems, and have contributed to several developments in the field.
The emergence of autonomous systems and multi-agent systems requires effective and flexible control and coordination mechanisms to guarantee the overall system properties. Theories and concepts from social sciences explain how the behavior of individuals in human societies are controlled and coordinated by means of social concepts and mechanisms. Inspired by social theories and mechanisms we develop computational mechanisms for control and coordination of autonomous and distributed software systems.