Course - Introduction to Artificial Intelligence - TDT4136
Introduction to Artificial Intelligence
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About the course
Course content
The subject starts with a description of problem solving methods by means of heuristic search. Therafter, various knowledge representation languages and inference methods for automatic problem solving. Representation in form of predicate logic, frames and semantic nets are treated, and connected to the main forms of reasoning - especially rule based reasoning. Furthermore, architectures that integrates various resoning methods, agent based architectures and architectures for interactive problem solving. Numerous applicaton examples are given to demonstrate the methods.
Learning outcome
Knowledge: The candidate will gain knowledge of:
- historical perspective of AI and its foundations
- basic principles of AI toward problem solving, inference, and knowledge representation
- representation and reasoning with propositional and predicate logic
- uninformed and heuristics search methods,
- adversarial search
- constraint satisfaction problems and methods
- representation of planning problems and solution methods
- multiagent environments and game theory principles and some problem solving methods
- ethics related problems in AI
Skills:
- decide which types of intelligence and agents are needed in a certain type of environment and design the agent accordingly
- design knowledge-based systems using the suitable type of representation, inference and problem solving method
- be able to identify possible ethical problems for a given a problem
General competence:
- Know AI's basis taken from logic and cognitive sciences.
Learning methods and activities
Lectures, self study, exercises and a project. A number of mandatory exercises must be approved in order to take the exam.
Compulsory assignments
- Exercises
Further on evaluation
If there is a re-sit examination, the examination form may be changed from written to oral.
In the written exam, the questions will only be in English since the lectures, slides, book and the other material are all in English. Students can answer in Norwegian.
Recommended previous knowledge
TDT4120 Algorithms and Data Structures and MA0301 Elementary Discrete Mathematics or TDT4120 Algorithms and Data Structures and TMA4140 Discrete Mathematics, or similar.
Course materials
To be announced.
Credit reductions
Course code | Reduction | From |
---|---|---|
IT2702 | 3.7 sp | Autumn 2007 |
IT272 | 3.7 sp | Autumn 2007 |
MNFIT272 | 3.7 sp | Autumn 2007 |
TDT4135 | 3.7 sp | Autumn 2007 |
SIF8015 | 3.7 sp | Autumn 2007 |
TDT4170 | 3.7 sp | Autumn 2007 |
SIF8031 | 3.7 sp | Autumn 2007 |
IMT3103 | 7.5 sp | Autumn 2018 |
Subject areas
- Computer Systems
- Informatics
Contact information
Course coordinator
Lecturers
- Ahmed Abouzeid
- Keith Linn Downing
- Ole Christian Eidheim
- Pinar Øztürk
- Xavier Fernando Cuauhtémoc Sánchez Diaz