Artificial IntelligenceDF22200, 2011VT
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Course plan
Lectures
24 hours
Recommended for
Doctoral students in computer science.
The course was last given
2007, but with another course plan
Goals
The student should learn to understand and to use basic formal and practical techniques in artificial intelligence
Prerequisites
Solid competence in computer programming Introductory course in artificial intelligence Previous knowledge of first-order logic is recommended, but can also be obtained using additional study within the course
Contents
The contents are structured as core contents and additional contents.
Core Contents
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Basic concepts and formal languages in knowledge representation
Decision trees and causal nets
Description logic
Defeasible inheritance
Reasoning about actions and action planning:
Characterizing actions in terms of preconditions and effects
Use of preconditions in autonomous agents
Explicit-time logics and the situation calculus
Prediction; the frame problem and related issues
Action planning methods
Nonmonotonic reasoning. Entailment using preferred models, default logic,
circumscription
Search techniques, the A* algorithm, SAT methods
Computational paradigms in artificial intelligence: logic programming and
logic-programming-based approaches, answer-set programming, production systems,
rule-based systems
Software architectures for intelligent autonomous agents: SOAR, BDI
architectures, Hierarchical Task Networks, three-level architectures
Additional Contents
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Some asepcts of computer learning: Reinforcement learning, inductive learning,
learning of decision trees, classification, PAC learning
Qualitative reasoning
Software issues in robotics
Model-based diagnosis and execution monitoring
Organization
Lectures
The course is organized as three course weeks, with several lectures during
each of those weeks, and several weeks of independent study between the course
weeks. It is assumed that the students can acquire major parts of the course
contents by studying the lecture notes.
Literature
Lecture notes as specified on the course webpage
Lecturers
Erik Sandewall
Examiner
Erik Sandewall
Examination
Completion of exercises ("labs")
A written exam
Credit
6 hp
Comments
Course webpage
Page responsible: Director of Graduate Studies