Module overview
This module introduces the fundamental concepts of artificial intelligence. The content is intentionally broad, covering the history of AI from computational, representational and philosophical aspects. The module looks at artificial intelligence through the perspective of learning as one of the most fundamental attributes of intelligent behaviour.
Aims and Objectives
Learning Outcomes
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- Gain facility in creating and comparing simple game playing AIs (e.g. tic-tac-toe with game-trees and RL)
- Gain facility in performing first-order reasoning over knowledge bases using logic/relation programming languages (such as Python Kanren)
- Construct and make predictions with simple classification or regression trees (e.g. using CART) and mine some simple association rules
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Choose and justify approaches and representations for solving particular tasks
- Derive the action or prediction common AI algorithms will make given particular knowledge, problems or states
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- Demonstrate awareness of the philosophical bases of artificial intelligence
- Demonstrate knowledge of the broad ranges of approaches to AI their their classification
- Discuss the responsibilities of AI engineers with respect to ethical aspects of learning technologies
- Understand the achievements and limitations of different approaches to AI
Syllabus
What is intelligence and what is artificial intelligence?
- Tests of AI: The Turing test. Searle's Chinese room argument.
- Classifications of AI: strong vs weak, symbolic vs sub-symbolic, etc.
- Fundamental problems in AI - e.g. grounding
Representation of data
- Symbolic representations
- Sub-symbolic representations
* Distributed representations
- Abstraction of representations
* Representational hierarchies, taxonomies
Representation of knowledge
- Parameters in functional expressions, algorithms and programs
- Decision trees
- Formal grammars
- Production rules
- Formal logic (predicate logic; propositional logic; first order reasoning)
- Frames and schemas
- Taxonomies
- Graphs, networks & ontologies (examples e.g. Wordnet and Cyc)
AI as learning
- Learning from examples
* KNN-classification and regression
* Learning rules from examples; inductive learning; simple decision tree learning; association rule mining
- Learning by correcting mistakes
* Early statistical learning machines - e.g. The MK1 Perceptron
* Linear classifiers
* Neural networks
* Differentiable programming
- Playing games and Learning by playing games
* Early game AIs (e.g. Strachey's "M. U. C. Draughts", Arther Samuel's Draughts, etc)
* Game trees, Tree search (Alpha–beta pruning, monte-carlo tree search)
* Learning state=>action pairs using reinforcement learning
Learning by being told & reasoning (GOFAI)
- Solving problems by searching over representations
* Finding satisfactory paths (e.g. depth-first and breadth-first, iterative deepening, local search and heuristic search).
* Finding optimal paths (e.g. branch and bound, dynamic programming, A*).
Inherent problems in AI
- Biases in data and knowledge representation
- Fragility of symbolic approaches
- Unexplainability of sub-symbolic approaches
Learning and Teaching
Teaching and learning methods
The module consists of:
- Lectures
- Combined tutorials and computing laboratory sessions
Type | Hours |
---|---|
Revision | 12 |
Guided independent study | 66 |
Lecture | 36 |
Specialist Laboratory | 9 |
Completion of assessment task | 27 |
Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Class Test | 10% |
Exam | 50% |
Computing assignment | 20% |
Computing assignment | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
Method | Percentage contribution |
---|---|
Exam | 100% |
Repeat Information
Repeat type: Internal & External