Studying online

There are now 2 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Click on an offering mode for more details.

Unit Overview

Description

In the broad context of Artificial Intelligence a key feature of intelligent beings is their abilities for perceiving their surrounds and making rational decisions on how to act, for example, deciding on the next move on a board game. This unit extends the classical problem-solving focus of algorithmic design to autonomous decision making, through introducing the key fundamental concepts and principles of intelligent autonomous agents. We introduce ideas such as decision making, goal-directed behaviour, heuristic search, action selection, environment with uncertainties, performance (self-evaluation), expected return and learning. These concepts are explored and reinforced through practical designs and implementations of intelligent agents situated in environments such as game playing.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Details for undergraduate courses
  • Level 3 core unit in the Artificial Intelligence; Automation and Robotics Engineering; Quantum Computing major sequences
  • Level 3 option in the Computer Science major sequence
  • Level 3 elective
Outcomes

Students are able to (1) explain various ways in which algorithms can learn, their relationships, and their potential power and pitfalls; (2) assess the performance of algorithms and appreciate the extra requirements of algorithms operating in an autonomous context; (3) develop and implement a software agent in a suitable software engineering framework; and (4) research a relevant area of interest and effectively communicate the results through scientific writing and experimental analysis.

Assessment

Indicative assessments in this unit are as follows: (1) mid-semester test; (2) projects and labs; and (3) final examination. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Andrew Gozzard
Unit rules
Prerequisites
Successful completion of
CITS2200 Data Structures and Algorithms
Advisable prior study
CITS2211 Discrete Structures (ID 986)
Contact hours
lectures: 2 hours per week
labs: 3 hours per week
Text

Russell, S. J. and Norvig, P. Artificial Intelligence: a Modern Approach, 4th edn: Prentice Hall May 2020.

  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Visit the Essential Textbooks website to see if any textbooks are required for this Unit. The website is updated regularly so content may change. Students are recommended to purchase Essential Textbooks, but a limited number of copies of all Essential Textbooks are held in the Library in print, and as an ebook where possible. Recommended readings for the unit can be accessed in Unit Readings directly through the Learning Management System (LMS).
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.