Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

Objectives:

Concepts of artificial intelligence (AI) are central to the design and development of contemporary systems, e.g., database search and management, handheld devices (e.g., smartphones and tablets), games (e.g., chess), various adapting or learning systems, and so on. The objective of this course is to give students exposure to and an understanding of the fundamentals of AI programming, including: rational agents and their environment, knowledge representation, formal languages and logic, reasoning, basic graph theory, pathfinding algorithms, finite state automata, steering behaviors, and decision making. Students will develop practical skills in AI programming useful for the development and deployment of intelligent systems.

Learning objectives

Knowledge

Students who complete the module will obtain the following qualifications:

• Understand different levels of intelligent agent architectures, environments, and their application domains
• Understand basic graph theory
• Understand finite state machines, decision trees, and behaviour trees, and their implementation
• Understand different search strategies, and their implementation and underlying data-structures
• Understand different pathfinding algorithms and their implementation
• Understand steering algorithms and their implementation
• Understand classical planning approaches
• Understand knowledge representation, formal logic, and reasoning
• Understand basic fuzzy logic

Skills

Students who complete the module will obtain the following qualifications:

• Apply the above knowledge to construct an intelligent system using available technologies
• Choose appropriate methods and technologies for a given problem (analysis)
• Interpret and evaluate AI systems and their behaviour
• Use agent simulation systems for prototyping system behaviour (apply)

Competences

Students who complete the module will obtain the following qualifications:

• Ability to synthesise knowledge, methodology or techniques concerning a problem centred around intelligent systems
• Ability to integrate AI-based libraries into larger projects (apply)
• Ability to learn the use of AI tools like agent-based simulators, planning systems, network simulators, etc. (apply)

Type of instruction

Refer to the overview of instruction types listed in the start of chapter 3. The types of instruction for this course are decided in accordance with the current Framework Provisions and directions are decided and given by the Study Board for Media Technology.

Exam

Exams