Lecture 01

guineanhillΤεχνίτη Νοημοσύνη και Ρομποτική

20 Οκτ 2013 (πριν από 3 χρόνια και 7 μήνες)

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Artificial Intelligence

Bo Yuan, Ph.D.

Professor

Shanghai Jiaotong University

Overview of Machine Intelligence



Knowledge
-
based rules (expert system, automata, …)


Symbolic representation in logics (Deep Blue)



Kernel
-
based heuristics (MDA, PCA, SVM, …)


Nonlinear connection for more representation (Neural Network)



Inference (Bayesian, Markovian, …)


To sparsely sample for convergence (GM)



Interactive and stochastic computing (uncertainty,
heterogeneity)


To possibly overcome the limit of Turin Machine

Interactions

The Framework to Study a System

Bottom
-
Up

Top
-
Down

How much can we represent and model a
complex and evolving network ?

Low Complexity Solutions for

High Complexity Problems


Convexity


Stability (
Metastability
)


Sampling


Ergodicity


Convergence


Regularization


Software and Hardware

Interactions

The Framework to Study a System

Bottom
-
Up

Top
-
Down

How much can we represent and model a
complex and evolving network ?

Data

Representation

Mathematic
al
Foundation

Mathematical

Representation

Typical

Algorithm

AI
-
Related

Question

Graph

Graph Theory and
Variable Reduction

Optimization

Liner Programming

Network Modularity

and Organization

Logic

Algebraic Logic

Random

Boolean
Network, Automata

Network Structure

and Attractors

Circuit


Complex Number

and Control Theory

Linearization

Stability and control

Network Stability
and Control

Reasoning

Game Theory

Evolutionary

Game

Nash

Equilibrium

Markov

Games

Inference

Bayes

Theorem

Believe Propagation

Model

Searching

Causality Inference

Discrete

Stochastic

Markov
-
based

Updating

Convergence

Meta
-
stability

Evolution and


Dynamics

Continuous
Stochastic

Stochastic

Differentials

Brownian

integrals

Fokker
-
Planck

Network Dynamics
and Control

Review of Lecture One


O
verview of AI


Knowledge
-
based rules in logics (expert system, automata, …) :
Symbolism in logics


Kernel
-
based heuristics (
n
eural network, SVM, …) :
Connection for nonlinearity


L
earning and inference (Bayesian, Markovian, …) :
To sparsely sample for convergence


Interactive and stochastic computing (Uncertainty, heterogeneity) :
To overcome the
limit of Turin Machine



Course Content


Focus mainly on
l
earning and
i
nference


Discuss current problems and research efforts


Perception and behavior (vision, robotic, NLP, bionics …) not included



Exam


Papers (Nature, Science, Nature Review, Modern Review of Physics, PNAS, TICS)


Course materials

Outline


Knowledge Representation


Searching and Logics


Perceiving and Acting


Learning


Uncertainty and Inference