Course overview
The course will provide students with knowledge about theoretical underpinnings and practical applications of technologies currently studied in knowledge representation and applied Artificial intelligence research. The course will cover different approaches suitable to a number of important application areas and real world domains. It will assume prior knowledge of basic AI concepts. The course includes both the study of theoretical aspects as well as practical modelling with current knowledge representation techniques. This includes: Qualitative Reasoning - Approaches to Qualitative Reasoning, Naïve Physics Manifesto; Qualitative Simulation and applications; Model-based Reasoning - Foundations of Model-based Diagnosis; Fault models and hierarchical diagnosis; Constraint Satisfaction Problems (CSPs) - Solution and filtering algorithms; Configuration and design problem solving; Ontologies - The concept of representing knowledge; Description Logics, OWL; Denotational semantics - Alternate Logics; Basic Machine Learning topics: Decision trees, Induction, Genetic algorithms, genetic programming.
Course learning outcomes
- Investigate and report on current research areas in Artificial Intelligence
- Present principles of reasoning with declarative knowledge representation
- Create new knowledge bases using AI techniques
- Critically assess the expressiveness of AI techniques and describe why they are superior to other approaches