文档介绍：works and Decision Graphs
A 3-week course at Reykjavik University
Finn V. Jensen & Uffe Kjærulff({fvj,uk}***@)
Group of Machine Intelligence
Department puter Science, University
26 April – 13 May, 2005
Outline
1 Introduction
2 Contents of Course
3 works and Relevance Analysis
4 Bayesian Probability Theory
Reykjavik University, April/May 2005, BNs and DGs: Introduction 2
Introduction
1 Artificial Intelligence
2 Expert Systems
3 Normative Expert Systems
4 Sample Application Areas
Reykjavik University, April/May 2005, BNs and DGs: Introduction 3
Artificial Intelligence / Machine Intelligence
What is artificial intelligence?
Device or service that
reasons and makes decisions under uncertainty,
extracts knowledge from data/experience, and
solves problems efficiently and adapts to new situations.
Reykjavik University, April/May 2005, BNs and DGs: Introduction 4
Artificial Intelligence / Machine Intelligence
What is artificial intelligence?
Device or service that
reasons and makes decisions under uncertainty,
extracts knowledge from data/experience, and
solves problems efficiently and adapts to new situations.
Why use artificial intelligence?
Automate tasks.
Automate reasoning and decision making.
Extract knowledge and information from data.
Reykjavik University, April/May 2005, BNs and DGs: Introduction 5
Expert Systems
The first expert systems were constructed in the late 1960s.
Expert System = Knowledge Base + Inference Engine
Reykjavik University, April/May 2005, BNs and DGs: Introduction 6
Expert Systems
The first expert systems were constructed in the late 1960s.
Expert System = Knowledge Base + Inference Engine
The first expert systems were constructed puter models of
the expert, . production rules like:
if condition, then fact
if condition, then action
Reykjavik University, April/May 2005, BNs and DGs: Introduction 7
Expert Systems
The first expert systems were constructed in the la