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Intelligent Systems 1 - COM00020I

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  • Department: Computer Science
  • Module co-ordinator: Dr. James Stovold
  • Credit value: 10 credits
  • Credit level: I
  • Academic year of delivery: 2020-21

Module summary

Search and Representation

Related modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Module will run

Occurrence Teaching cycle
A Autumn Term 2020-21

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

Assessment

Task Length % of module mark
University - closed examination
Intelligent Systems 1 Closed Exam
2 hours 100

Special assessment rules

None

Reassessment

Task Length % of module mark
University - closed examination
Intelligent Systems 1 Closed Exam
2 hours 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



The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.