Course syllabus - Artificial Intelligence 2

Objectives

The course aims at deepening the knowledge in artificial intelligence by looking into practical methods for problem definition and knowledge representation. Furthermore, to introduce logic programming and paradigms suitable for when fast execution and memory safety are desirable properties.

Learning outcomes

After completing the course, the student shall  be able to:

1. analyse and define propositional and predicate logic; and demonstrate how these theories can be used in logic programming for solving problems and representation of agent models, 
2. explain and apply population-based agent models, such as evolutionary algorithms and its variations, 
3. analyse and define the most representative methods in uncertainty, fuzzy logic, and reasoning,  
4. explain and apply expert systems for solving domain specific problems, 
5. explain and apply planning through population-based agent models, 
6. explain the means for knowledge representation with respect to performance, especially in the context of different representation paradigms, such as (i) logic, that is symbolic, (ii) numeric, and (iii) the combination of both and
7. analyse and define a given problem with the ambition of deciding if it can be addressed by the methods covered in this course.

Course content

- Introduction to propositional and predicate logic; and a profound review of applying these theories to logic programming and agent models in general, 

- Agent models, including interaction between agents within the context of population-based algorithms for optimisation and planning, 

- Uncertainty, fuzzy logic, and reasoning for agents, 

- Domain specific expert systems, 

- Planning for individual and multi-agent systems, 

- Analysis of representation of knowledge, through practical methods, and with respect to high performance and low memory and requirement.

Specific requirements

30 credits in coumputer science, of which includes Programming 7,5 credits, Data Structures, Algorithms and Programme Development 7,5 credits and Artificial Intelligence 7,5 credits. In addition vectoralgebra 7,5 credits is needed.

Examination

Laboratory work (LAB1), an assignment that is demonstrated to the teacher, 0.5 credits, examines the learning outcomes 1 and 7, marks Fail (U) or Pass (G).
Laboratory work (LAB2), an assignment that is presented with a report and a demonstration to the teacher, 2.5 credits, examines the learning outcomes 1, 3, 4, 6 and 7, marks Fail (U) or Pass (G).  
Laboratory work (LAB3), an assignment that is presented with a report and a demonstration to the teacher, 2 credits, examines the learning outcomes 2, 6 and 7, marks Fail (U) or Pass (G).  
Laboratory work (LAB4), an assignment that is presented with a report and a demonstration to the teacher, 2.5 credits, examines the learning outcomes 2, 5, 6 and 7, marks Fail (U) or Pass (G).

For final grade Pass (G) the mark Pass (G) is required in all four laborations.

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass, Fail

Interim Regulations and Other Regulations

The course completely overlaps towards CDT312/DVA340 Artificial Intelligence.