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  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    INTRODUCTION TOCOMPUTATIONAL INTELLIGENCE

    Lin ShangDept. of Computer Science and Technology

    Nanjing University

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Introduction to Computational Intelligence

    n From AI to CIn What is intelligence ?n What is AI ?n What is CI ?

    n Different views of Computational Intelligence n History of Computational Intelligence n Computational Intelligence Paradigmsn Conferences and Journals

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    From AI to CI

    n What is intelligence?

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    n Characteristics of Intelligence

    n Perception

    n Action

    n Reasoning

    n Problem-solving

    n LearningandAdaptation

    n Sociality

    n Creativity

    From AI to CI

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    From AI to CIn What is AI ?

    n Philosophy Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality

    n Mathematics Formal representation and proof algorithms, computation,(un)decidability (in)tractability, probability

    n Economics utility, decision theory

    n Neuroscience physical substrate for mental activity

    n Psychology phenomena of perception and motor control, experimental techniques

    n Computer building fast computers Science

    n Control theory design systems that maximize an objective function over time

    n Linguistics knowledge representation, grammar

    AI prehistory

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 201616/2/29 AI:Introduction 6

    From AI to CIAbridged history of AI

    n 1943 McCulloch & Pitts: Boolean circuit model of brainn 1950 Turing's "Computing Machinery and Intelligence"n 1956 Dartmouth meeting: "Artificial Intelligence" adoptedn 1952-69 Great expectations n 1950s Early AI programs, including Samuel's checkers program, Newell & Simon's

    Logic Theorist, Gelernter's Geometry Enginen 1965 Robinson's complete algorithm for logical reasoningn 1966-73 AI discovers computational complexity, Neural network research almost

    disappearsn 1969-79 Early development of knowledge-based systemsn 1980- AI becomes an industry n 1986- Neural networks return to popularityn 1990- Novelle AI and intelligent agents n 1995- Evolutionary Computation, Swarm Intelligence, and New Generation

    Computers

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    1943:McCulloch & Pitts: Boolean circuit model of brain

    MP

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    1950: Turing test

    n When does a system behave intelligently?

    n Turing (1950) Computing Machinery and Intelligence

    n Operational test of intelligence: imitation game

    n Requires the collaboration of major components of AI: knowledge, reasoning, language understanding, learning,

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    2011:Cleverbot

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    1956:Dartmouth meeting: "Artificial Intelligence" adopted

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Ideas for AIn Learning

    n child machine

    n Symbolic AI

    n Connectionism

    n Nouvelle AI

    n Evolutionary Computation

    n artificial life

    n Computational Swarm Intelligence

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Symbolic AIn Physical Symbol System Hypothesis of Newell and Simon

    n the processing of structures of symbols by a digital computer is

    sufficient to produce artificial intelligence

    n the processing of structures of symbols by the human brain is

    the basis of human intelligence

    n it remains an open question whether the Physical Symbol System

    Hypothesis is true or false

    n Top-down strategy

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    n Problem-sloving Expert System Knowledge Engineering- Search, Representation, Reasoning

    n Problems- Frame problem (CYC, Go..)- Substituting large amounts of computation for understanding

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Connectionismn The mechanisms of brains are very different in

    detail from those in computersn how brains work? Bottom-up strategy

    Natural Neural Network

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    n A brief historyM-P neuron (McCulloch & Pitts) Perceptron

    (Rosenblatt) Hopfield Model, B-P Learning Method (Rumelhart & McClelland) Deep Learning

    n ApplicationsRecognition, Vision, Business, Medical, .

    Connectionism

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Evolutionary Computation

    n Biological evolutionTo produce an enormous variety of living organisms closely suited to different sets of needs in different environments.

    n Simulated evolutionBy modeling those processes of biological evolution on computers, it turns out that we can sometimes get the computers to evolve solutions to problems.

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    n Genetic AlgorithmUse strings of symbols to encode solutions to problems,like strings of molecules in DNA. Transforming andrecombining portions of strings enables an evolutionarycomputation to search for good solutions, partly analogousto biological evolution.

    n Genetic ProgrammingExtends these ideas to automatic programming by usingstructures which are better suited to the problem thanstrings are.

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    n Evolutionary StrategyUse natural problem-dependent representations, and primarily mutationand selection as search operators. Mutation is normally performed byadding a normally distributed random value to each vector component.The step size or mutation strength is often governed by self-adaptation. The selection in evolution strategies is deterministic andonly based on the fitness rankings, not on the actual fitness values.

    n Evolutionary ProgrammingHarder to distinguish from evolutionary strategies. Its main variation

    operator is mutation; members of the population are viewed as partof a specific species rather than members of the same speciestherefore each parent generates an offspring

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Artificial Life (Alife)n Artificial Life is the study of man-made systems that

    exhibit behaviors characteristic of natural living systems. It complements the traditional biological sciences concerned with the analysis of living organisms by attempting to synthesize life-like behaviors within computers and other artificial media. By extending the empirical foundation upon which biology is based beyond the carbon-chain life that has evolved on Earth, Artificial Life can contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be."

    Chris Langton (in Proc. of first Alife conference)

    Ref:http://www.cogs.susx.ac.uk/users/inmanh/easy/alife09/lectures.html

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Artificial Life and Evolutionary

    Origin of Life

    Today

    Life, and might have beenas it is

    FromVirgilGriffith,GoogleTechTalk- 2007

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Example: Forming body plans with evolution

    n Node specifies part type, joint, and range of movement

    n Edges specify the joints between parts

    n Population?

    n Graphs of nodes and edges

    n Selection?

    n Ability to perform some task (walking, jumping, etc.)

    n Mutation?

    n Node types change/new nodes grafted on

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Computational Swarm Intelligence n Intelligence is often considered a property of individuals.n Are we social because we are intelligent or are we

    intelligent because we are social?- Intelligence can emerge from social interaction.

    n Emergent behaviour when a group behaves in ways that were not programmed into its members.

    n Swarm intelligence- simulated social interaction- emergent collective intelligence of groups of simple agents

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

  • INTRODUCTION TO COMPUTATIONAL INTELLIGENCE, Nanjing University Spring 2016

    Computational Tools

    n Multi-Agent Systems- a system composed of multiple interacting intelligent agents. - application including computer games, networks, transportation, logistics, and etc.

    n Ant C