Symbolic AI

Coordinator: Giorgos Flouris

Managing and reasoning with knowledge is an important prerequisite for artificial agents exhibiting intelligent behaviour, a vision known as Artificial Intelligence (AI). In this context, being able to represent and reason with knowledge, so as to generate new knowledge, inform decisions, and reason about actions and/or the environment is a critical problem. Moreover, managing the inherent complexity, dynamicity and heterogeneity of knowledge necessary for intelligent behaviour in any real-world setting, is important for supporting the AI vision. 
In this context, ISL has been pursuing, and plans to continue pursuing, basic research in knowledge representation and reasoning, knowledge management and other branches of Symbolic AI, towards supporting the vision of intelligent behaviour by artificial agents. Particular objectives of interest are to:

  • Develop monotonic and nonmonotonic Rule-based Reasoning languages and systems for the semantic web or smart environments, with strong emphasis on performance
  • Develop lightweight Rule Reasoners running on small devices and able to be easily integrated with other technologies
  • Study the revision of Knowledge Bases, with emphasis on description logic-based knowledge bases, and apply the results and techniques to ontology change (update, revision, versioning) and the integration of inconsistent ontologies
  • Study methods of Computational Argumentation and Argumentative Persuasion, where agents engage in argumentative reasoning to pursue specific goals, while providing debate management methodologies and tools, that will allow agents to more easily engage in argumentative dialogues, and improve sense-making in long debates
  • Study reasoning about action in dynamic environments, in particular extensions of the Event Calculus in various contexts, including recovering from unexpected observations, or supporting visualisations and analysis for Event Calculus
  • Study misinformation games, a class of games in game theory in which the participating agents have an erroneous view with regards to the game setting

Apply the above methods to Social and Cognitive Intelligent Systems