CS7050 - Artificial Intelligence (2025/26)
| Module specification | Module approved to run in 2025/26 | ||||||||||||
| Module title | Artificial Intelligence | ||||||||||||
| Module level | Masters (07) | ||||||||||||
| Credit rating for module | 20 | ||||||||||||
| School | School of Computing and Digital Media | ||||||||||||
| Total study hours | 200 | ||||||||||||
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| Running in 2025/26(Please note that module timeslots are subject to change) |
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Module summary
This module introduces the essential principles, methods and techniques in AI. It covers a broad range of topics such as search, planning, logic, knowledge representation and inference. It discusses examples of intelligent systems and studies how to develop intelligent applications such as expert systems, natural language systems, and autonomous mobile and robotic systems. Students will be offered lectures, which introduces the important concepts, explain the principles and techniques, and demonstrate how to apply them to solve problems in the related topics. The workshops will provide practical sessions to help students understand the content of the lectures and build the necessary skills to develop intelligent systems.
Syllabus
The main topics of the study will include:
Introduction to Artificial Intelligence (AI). Definition, history, and problematics of AI. Rational reasoning and intelligent behaviour. Rational agent architectures.
Search and Problem Solving in state space. Uninformed search: breadth-first, depth-first, iterative deepening. Informed search: Greedy search, Heuristic search. A* algorithm. Empirical comparison of the search algorithms.
Complex state space strategies. Minimax Search and Constraint Propagation
Language, logic, and reasoning. Propositional logic, First-order predicate logic (FOL), Non-classical logics. Comparison of the logical languages for knowledge representation.
Logic Inference and logical theories. Deduction, induction, case-based reasoning. Forward and backward inference. Comparison of the strategies for deduction automation.
Knowledge-based Systems (KBS). Knowledge Representation and Interaction with KBS. Inference engine and automated deduction. Comparison of the algorithms for automated deduction.
Knowledge-based planning. Linear and Non-Linear Planning. Sussman Anomaly. Contingent and continuous planning.
Utility-based agents and sequential decision making. Markov Decision Process (MDP) and partially observable Markov decision process (POMDP).
Solving sequential decision processes. Principle of optimality and Bellman equations. Value iteration and policy iteration. Linear Programming. Comparison of the methods.
Reinforcement learning (RL). Model-based and model free reinforcement. Q-learning. MDP and POMDP problem solvers. Comparison between the solutions.
Natural Language Processing (NLP). Language Grammars and parsing. Syntactic analysis, semantic disambiguation, and machine translation. Application of NLP to data processing.
Legal, Ethical, Social & Professional Issues in AI. AI and law. National and international regulations related to AI. AI and ethical behaviour. Impact of AI on the society.
Balance of independent study and scheduled teaching activity
The module will be taught by a mixture of lectures, supervised workshops, and unsupervised self-study which includes both studying the theory and working on programming tasks
The lectures (2 hours) will normally be used to introduce the various concepts and principles of the module’s topics.
The workshops (2 hours) will specify practical tasks for building intelligent agents which utilize the methods of AI. During the workshop sessions, students will expand the materials from the lectures and tutorials, and practise solving programming problems (or simulation related experiments) using Python as a working language, with examples from both the lectures and exercises set for the supervised tutorial sessions.
The coursework will cover the topics introduced and exercised in the module and will require implementation and experimental comparison of different algorithms for building rational agents.
Students are expected to spend time on unsupervised work in the computer laboratories and in private study to do their coursework.
All teaching materials for students will be placed on WebLearn.
Learning outcomes
On completing the module, the student will be able to:
LO1 Understand and critically analyse the essential concepts, principles, methods, techniques, and problems of AI.
LO2 Have working knowledge of the methods for state space search, qualitative and quantitative assessment of the progress towards goal state, heuristic information representation, retrieval and application to problem solving.
LO3 Demonstrate the understanding of knowledge engineering and ability to develop a prototype of knowledge-based systems which can use knowledge representation and automated logical inference
LO4 Differentiate between different methods for decision making and action planning applicable to the task for building agents which can learn from their own behaviour
LO5 Develop decision making skills based on theoretical and empirical comparison of the different methods and algorithms for building intelligent agents
LO6 Understand the Legal, Ethical & Professional Issues brought by AI and their impact on the society
Bibliography
Core:
Russell SJ, Norvig P. Artificial intelligence a modern approach. 4th Edition, Pearson; 2021, ISBN: 1292401176, 9781292401171