Advanced Artificial Intelligence
CSci 543
Fall 2013
1
Course Overview
Intelligent Agents
Problem Solving by Searching and Game-Playing
Logical Systems: Knowledge and Reasoning
Planning Systems
Uncertainty
Probabilistic Reasoning
Decision Making
Learning
2
Innovative Systems Design and Engineering
ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online)
Vol 2, No 7, 2011
www.iiste.org
P-q Theory Based Shunt Active Power Conditioner for
Mitigation of Power Quality Problems
Mostafa A. Merazy (Corresponding author)
Elec
Presentation of the ieee
Document
p-q Theory for Active
Compensation Applied to
Supergrids and Microgrids
Overview:
Power System/Electrical Grid
Distributed Energy Sources (D.E.R)
Harmonics/Distortions
VSC HVDC link Connected Wind Farm
Super Grids & Micro
IDAPS and Multi-Agent
Systems
1.
What Is IDAPS?
It is a concept which includes some Power
Generating sources providing electrical
power to some designated loads, critical
loads at the time of any power outage at the
main grid. Thus, ensuring constant supp
Chap. 14
Bayesian Networks:
How to build network models to reason under uncertainty
according to the laws of probability theory?
Outline
l
Syntax
l
Semantics
l
Parameterized Distributions
l
Exact inference by enumeration
l
Exact inference by variable elim
Dempster-Shafer Theory
(U&C Chap.5 & AIMA 14.7)
CSci 543: Advanced Articial Intelligence
Fall, 2013
Introduction
Based on the original work by Dempster who attempted to model uncertainty by an interval of probabilities, rather than as a single probabilit
CSci 543: Advanced Artificial Intelligence
Due: 6 pm, October 17th (Thr.) 2013
October 10th, 2013
Instructor: Dr. E. Kim
Assignment 3: Temporal Probabilistic Reasoning DBN & HMM
NOTE: For each question, you may compute its probability either manually or w
Dempster-Shafer Theory
(U&C Chap.5 & AIMA 14.7)
CSci 543: Advanced Articial Intelligence
Fall, 2013
Introduction
Based on the original work by Dempster who attempted to model uncertainty by an interval of probabilities, rather than as a single probabilit
A Generalization of Bayesian Inference
Author(s): A. P. Dempster
Source: Journal of the Royal Statistical Society. Series B (Methodological), Vol. 30, No. 2
(1968), pp. 205-247
Published by: Wiley for the Royal Statistical Society
Stable URL: http:/www.js
Chapter 13
Quantifying
Uncertainty
1
Outline
Information and Uncertainty
Probability
Syntax and Semantics
Inference
Independence and Bayes' Rule
2
Theory of Uncertainty
To develop a fully operational theory for dealing with
uncertainty of some conceived t
i
m Equations
Chap. 6
. an approximate solution
7
P OSSIBILITY T HEORY
tion obtained. Also try
d compare them by the
7.1
FUZZY MEASURES
The fuzzy set provides us with an intuitively pleasing method of representing one form o f
uncertainty. Consider, howev