F29AI Artificial Intelligence and Intelligent Agents

Dr Ron PetrickDr Arash EshghiTalal Shaikh

Course co-ordinator(s): Dr Ron Petrick (Edinburgh), Dr Arash Eshghi (Edinburgh), Talal Shaikh (Dubai).

Aims:

  • To introduce the fundamental concepts and techniques of AI, including planning, search and knowledge representation
  • To introduce the scope, subfields and applications of AI, topics to be taken from a list including natural language processing, expert systems, robots and autonomous agents, machine learning and neural networks, and vision.
  • To develop skills in AI programming in an appropriate language

Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: Elementary knowledge of logic at the level of undergraduate Computer Science. Knowledge of high-level programming language concepts..

Location: ALP, Dubai, Edinburgh.

Semester: 1.

Syllabus:

  • Search algorithms (depth first search, breadth first search, uniform cost search, A* search)
  • constraint satisfaction problems;
  • games (min-max, alpha-beta pruning);
  • logic, resolution, introductory logic programming
  • knowledge representation – logic, rules, frames
  • goal and data-driven reasoning
  • practical rule-based programming
  • Overview of main fields of AI (Vision, Learning, Knowledge Engineering)
  • In depth view of one field of AI (e.g. Planning, Natural language)
  • Autonomous agents
  • Applications of AI
  • AI programming

Learning Outcomes: Subject Mastery

Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)

  • Critical understanding of traditional AI problem solving and knowledge representation methods
  • Use of knowledge representation techniques (such as predicate logic and frames).
  • Critical understanding of different systematic and heuristic search techniques
  • Practice in expressing problems in terms of state-space search
  • Broad knowledge and understanding of the subfields and applications of AI, such as computer vision, machine learning and expert systems.
  • Detailed knowledge of one subfield of AI (e.g. natural language processing, planning) and ability to apply its formalisms and representations to small problems
  • Detailed understanding of different approaches to autonomous agent and robot architectures, and the ability to critically evaluate their advantages and disadvantages in different contexts.
  • Practice in the implementation of simple AI systems using a suitable language.

Learning Outcomes: Personal Abilities

ndustrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT

  • Identification, representation and solution of problems
  • Time management and resource organisation
  • Research skills and report writing
  • Practice in the use of ICT, numeracy and presentation skills.

Assessment Methods:

Assessment: Examination: (weighting – 70%) Coursework: (weighting – 30%)
Re-assessment: Examination: (weighting – 100%)

SCQF Level: 9.