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

InteractionsThe Framework to Study a System

Bottom-Up

Top-Down

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

Low Complexity Solutions forHigh Complexity Problems

• Convexity • Stability (Metastability)• Sampling• Ergodicity• Convergence• Regularization• Software and Hardware

InteractionsThe Framework to Study a System

Bottom-Up

Top-Down

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

Data Representation

Mathematical Foundation

MathematicalRepresentation

TypicalAlgorithm

AI-RelatedQuestion

Graph Graph Theory and Variable Reduction

OptimizationLiner Programming

Network Modularity and Organization

Logic Algebraic Logic Random Boolean Network, Automata

Network Structureand Attractors

Circuit Complex Number and Control Theory

LinearizationStability and control

Network Stability and Control

Reasoning Game Theory Evolutionary GameNash Equilibrium Markov Games

Inference Bayes Theorem Believe PropagationModel Searching Causality Inference

Discrete Stochastic

Markov-based Updating

ConvergenceMeta-stability

Evolution and Dynamics

Continuous Stochastic

Stochastic Differentials

Brownian integralsFokker-Planck

Network Dynamics and Control

Review of Lecture One• Overview of AI

– Knowledge-based rules in logics (expert system, automata, …) : Symbolism in logics– Kernel-based heuristics (neural network, SVM, …) : Connection for nonlinearity– Learning 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 learning and inference– 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