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Intelligent Systems 1: Search & Representation - COM00020I

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• Department: Computer Science
• Credit value: 10 credits
• Credit level: I
• Academic year of delivery: 2022-23

Module summary

Search and Representation

Related modules

Module will run

Occurrence	Teaching period
A	Autumn Term 2022-23

Module aims

This module introduces the field of Artificial Intelligence, key approaches within the field and philosophical questions such as what it means for a machine to understand and whether humans themselves can be viewed as machines. Students will learn the theory and practice of classical AI techniques covering: problem representation, search-based AI, knowledge representation using propositional and first order logic and satisfiability. Practical work will include both pen and paper exercises and implementation using basic Python.

Module learning outcomes

I101	Explain the difference between strong, weak and general AI; understand the relationship between computation and AI; describe the Turing test and Searle's Chinese room argument
I102	Represent a problem symbolically in terms of states, operators and goals
I103	Distinguish between uninformed, heuristic and adversarial search paradigms and explain the key algorithms in each paradigm
I104	Select and apply an appropriate search algorithm for a given problem
I105	Define local search and describe how hill climbing and genetic algorithms can be used to perform local search for discrete search and optimisation problems
I106	Represent knowledge using propositional and first order logic
I107	Explain the notion of satisfiability within propositional logic and apply a SAT solving algorithm to determine if a given formula is satisfiable; recognise the connection between SAT solving and search
I108	Perform inference in first order logic using forward and backward chaining
I109	Deconstruct ethical arguments relating to AI and its applications

Indicative assessment

Task	% of module mark
Online Exam -less than 24hrs (Centrally scheduled)	100

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Online Exam -less than 24hrs (Centrally scheduled)	100

Module feedback

Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines.

Indicative reading

• Artificial Intelligence: A Modern Approach by Russell and Norvig