Artificial Intelligence
Learning Outcomes:
- Understanding the foundational concepts of AI and its historical background
- Gaining an overview of the course structure and the focus on symbolic (model-based) AI
- Learning the basic features of Prolog, including logic programming and declarative programming techniques
- Acquiring skills in writing Prolog programs and utilizing SWI-Prolog for AI development
- Formulating search problems using states, actions, and transitions in AI problem-solving
- Differentiating between uninformed and informed search algorithms, and understanding adversarial search for game-playing
- Modeling world aspects using propositional and predicate logic, and understanding their computational representations
- Applying symbolic logic for non-monotonic reasoning and decision-making in AI systems
- Utilizing situation calculus for reasoning about actions and their effects over time
- Comparing situation calculus with event calculus for state transition modeling in dynamic environments
- Understanding inductive learning and its role in AI, focusing on high-level symbolic learning
- Exploring decision tree learning and inductive logic programming, assessing their pros and cons
- Mastering SWI-Prolog for implementing AI solutions and completing course-related tasks
- Engaging with coursework to apply theoretical concepts in practical scenarios, receiving feedback to enhance learning
Skills for module:
Artificial Intelligence
Algorithms
Logics
Problem Solving
Critical Thinking
Time Management
Artificial Intelligence
CS2910
Learning Outcomes
- Understanding the foundational concepts of AI and its historical background
- Gaining an overview of the course structure and the focus on symbolic (model-based) AI
- Learning the basic features of Prolog, including logic programming and declarative programming techniques
- Acquiring skills in writing Prolog programs and utilizing SWI-Prolog for AI development
- Formulating search problems using states, actions, and transitions in AI problem-solving
- Differentiating between uninformed and informed search algorithms, and understanding adversarial search for game-playing
- Modeling world aspects using propositional and predicate logic, and understanding their computational representations
- Applying symbolic logic for non-monotonic reasoning and decision-making in AI systems
- Utilizing situation calculus for reasoning about actions and their effects over time
- Comparing situation calculus with event calculus for state transition modeling in dynamic environments
- Understanding inductive learning and its role in AI, focusing on high-level symbolic learning
- Exploring decision tree learning and inductive logic programming, assessing their pros and cons
- Mastering SWI-Prolog for implementing AI solutions and completing course-related tasks
- Engaging with coursework to apply theoretical concepts in practical scenarios, receiving feedback to enhance learning