Distributed Intelligent Systems

Lectures:
1st week. Review of typical applications areas of intelligent distributed systems: cyber-physical systems, intelligent embedded systems, ambient intelligent spaces, home care and AAL (Ambient Assisted Living), intelligent sensor networks, robotic team systems, information seeking systems in the Semantic Web environment, etc. (analyzing problems, basic requirements, typical tasks, re-evaluating the man-machine interactions).

2nd week. Knowledge based modelling of distributed systems. Knowledge management: specific representation problems, logical and emotional models, temporal and spatial reasoning.

3rd week. Design of domain models. Ontological knowledge and ontology management, ontology engineering.

4th week. Description languages and platforms, RDF data models, OWL, Protege.

5th week. Problem solving with ontologies, ontology based reasoning.

6th week. Safety and reliability, context aware system technology and information management, problems of information and knowledge fusion.

7th week. Knowledge intensive mechanisms of integration, adaptivity and robustness: sensor level information fusion, fusion architectures, fusion algorithms, semantic fusion with ontologies and ontology based reasoning. SensorWeb standard, SOS (sensor operating system) platform.

8th week. Mining fusion information, data mining tasks, data engineering in distributed heterogeneous environments.

9th week. Basic statistical analysis, visualization and exploratory analysis of heterogeneous data. Using analysis  data in decision support tasks.

10th week. Adaptivity in distributed systems, basic learning schemes.

11th week. Multiagent system architectures, multiagent environments, agent organizations, from centralized system to distributed intelligence.

12th week. Integration via communication, agent systems and the parallel programming paradigm. Agent communication languages, agent platforms, Jason, AgentSpeak.

13th week. Cooperation via communication: distributed reasoning, task-sharing in the market paradigm, cooperative multiagent planning. Handling conflicts in competitive environment, conflict related problems, voting protocols, knowledge intensive conflict resolution, game theoretical schemas, ad hoc solutions.

14th week. Learning knowledge components (believes and goals, or polices), single agent schemas, cooperative learning, learning in competitive environment.

Practice:
Students learn distributed agent environment technologies and their application to embedded environmental problems, (in an intelligent home environment).