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This course introduces important concepts in computational intelligence and demonstrates how they are used to solve problems that are normally difficult or intractable by conventional means. Topics covered include algorithms, techniques, and languages commonly used in artificial intelligence, knowledge representations and reasoning, and bio-inspired computing.
In this course you will learn about a set of techniques and methodologies that aim to produce computer programs that show aspects of intelligence present in humans and other animals. The collection of these techniques and methodologies that come from a variety of fields (such as mathematics, biology, neuroscience, philosophy, algorithms, operational research, statistics, control, ...) form a field that is called, for historical reasons, Artificial Intelligence (AI).The course covers core topics in AI including:- uninformed and informed graph search algorithms,- propositional logic and forward and backward chaining algorithms,- declarative programming with Prolog,- the min-max and alpha-beta pruning algorithms,- Bayesian networks and probabilistic inference algorithms,- classification learning algorithms,- consistency algorithms,- local search and heuristic algorithms such as simulated annealing, and population-based algorithms such as genetic search and swarm optimisation.
Having successfully completed the course, you will be able to: identify problems that are amenable to transformation into state-space graphs, create these graphs, and use known search techniques and algorithms to solve them; understand how logical inference can be achieved by computation and implement the related algorithms; create knowledge bases consisting of facts and rules about a domain and use appropriate algorithms to reason about objects in the domain; understand the declarative paradigm in problem solving and write programs in one of the languages in this paradigm; implement optimal algorithms to play games or solve any real-world problem where the process of decision making can be modelled as a turn-based zero-sum game; design and implement probabilistic networks and related algorithms in order to solve problems involving uncertain inputs, knowledge, or outcomes; apply basic machine learning algorithms to real datasets to build predictive models for tasks such as spam filters; create constraint networks for various problems that involve constraints and implement different algorithms to solve these problems; and recognise situations where due to the size or structure of the search space of an optimisation problem, classical approaches are not feasible and choose and apply an appropriate heuristic algorithm to attempt to solve the problem.
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Students must attend one activity from each section.
Depending on final student numbers, some of the advertised lab/tutorial streams may not run. Final lab/tutorial options will be available for self-allocation closer to the start of the semester through My Timetable.
Poole, David L.1958- , Mackworth, Alan K;
Artificial intelligence : foundations of computational agents;
Cambridge University Press, 2010.
Russell, Stuart J. , Norvig, Peter;
Artificial intelligence : a modern approach;
Prentice Hall, 2010.
Course Information on Learn
There are several important documents available online about departmental regulations, policies and guidelines at the following site. We expect all students to be familiar with these. Notices about this class will be posted to the class forum in the Learn system.COSC students will also be made members of a class called “CSSE Notices”, where general notices will be posted that apply to all classes (such as information about building access or job opportunities).
The Computer Science department's grading policy states that in order to pass a course you must meet two requirements:1. You must achieve an average grade of at least 50% over all assessment items.2. You must achieve an average mark of at least 45% on invigilated assessment items.If you satisfy both these criteria, your grade will be determined by the following University- wide scale for converting marks to grades: an average mark of 50% is sufficient for a C- grade, an average mark of 55% earns a C grade, 60% earns a B- grade and so forth. However if you do not satisfy both the passing criteria you will be given either a D or E grade depending on marks. Marks are sometimes scaled to achieve consistency between courses from year to year.Students may apply for special consideration if their performance in an assessment is affected by extenuating circumstances beyond their control.Applications for special consideration should be submitted via the Examinations Office website within five days of the assessment. Where an extension may be granted for an assessment, this will be decided by direct application to the Department and an application to the Examinations Office may not be required. Special consideration is not available for items worth less than 10% of the course.Students prevented by extenuating circumstances from completing the course after the final date for withdrawing, may apply for special consideration for late discontinuation of the course. Applications must be submitted to the Examinations Office within five days of the end of the main examination period for the semester.
Domestic fee $834.00
International fee $3,788.00
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
This course will not be offered if fewer than 10 people apply to enrol.
For further information see
Computer Science and Software Engineering.