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ARTIFICIAL IMMUNE SYSTEM

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Dog and Sheep. Identical behavior. 8/14/09. KGCOE Colloquium Series II: Artificial Immune System. Dog and sheep. D2 solely different behavior. 8/14/09 ... – PowerPoint PPT presentation

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Title: ARTIFICIAL IMMUNE SYSTEM


1
ARTIFICIAL IMMUNE SYSTEM
  • FERAT SAHIN
  • R.S. Srividhya
  • KGCOE Colloquium Series II
  • 01/12/2000

2
Outline
  • Are humans perfect machines?
  • How the brain works? Human vs. Computer.
  • Are computers designed with the correct logic!?
  • Why biological based methods?
  • Some history
  • Artificial Immune Systems

3
Are Humans Perfect Machines?
  • Learning
  • Continuous
  • Selective
  • Memory
  • Short term, Long term
  • Selective
  • Recall
  • Processing
  • Recognition (hearing, sight, various senses)

4
How the brain works?
  • One of the great mysteries of science How the
    brain enables thought?
  • Man has the largest brain in proportion to his
    size, Aristotle.
  • The seat of consciousness, until the middle of
    18th century.
  • The functional regions of the brain began to be
    mapped out, late 19th century.
  • Elements of the brain
  • The neuron / nerve cell the fundamental
    functional unit of all nervous system tissue,
    including the brain.
  • Each neuron consists of a cell body, or soma,
    that contains a cell nucleus.
  • There are number of fibers, called dendrites, and
    a single long fiber called axon branching out
    from the cell body.
  • The axon also branches into strands and
    substrands that connect to the dendrites and cell
    bodies of other neurons.
  • The connecting junction is called synapse.

5
How the brain works?
  • Signaling
  • Complicated electromagnetic reaction from neuron
    to neuron.
  • The synapses releases chemical transmitter
    substances
  • The chemical substances enter the dendrite,
    raising or lowering the electrical potential of
    the cell body.
  • When the potential reaches a threshold, an
    electric pulse or action potential is sent down
    to the axon.
  • Plasticity long-term changes in the strength of
    connections in response to the pattern of
    stimulation.
  • Migration sometimes entire collections of
    neurons can migrate from one place to another.
  • Most of the information goes on in the cerebral
    cortex, the outer layer.

6
How the brain works?
  • Certain areas of the brain have specific
    functions
  • The third left frontal convolution of the
    cerebral cortex is important for speech and
    language - aphasia
  • The mapping between areas of the brain and the
    parts of the body they control , or from which
    they receive sensory input,
  • Radical changes of the mapping and multiple
    mappings.
  • How other areas can take over functions when one
    area is damaged is not fully known. Migration?
    No known intelligent system can perform this.
  • There is almost no theory about how an individual
    memory is stored.
  • Article about the face recognition of the brain.
  • Neurobiology is a long way from a complete theory
    of consciousness.
  • The only real alternative theory is mysticism
  • There is some mystical realm in which minds
    operate that is beyond physical science. THE
    SOUL!!!?

7
How the brain works?
  • A general comparison of the raw computational
    resources available to computers and brains.
  • Computer Human Brain
  • Computational units 1 CPU, 105 gates 1011
    neurons
  • Storage units 109 bits RAM, 1010 bit disk
    1011 neurons, 1014 synapses
  • Cycle time 10-9 sec 10-3 sec
  • Bandwidth 109 bits/sec 1014 bits/sec
  • Neuron updates 105 1014
  • Even though the computer is a million faster in
    raw switching speed, the brains ends up being a
    million times faster at what it does.
  • Face recognition
  • The brain requires less than a second - a few
    cycles.
  • A serial computer requires billions of cycles.

8
Why biological based methods?
  • Biological systems outperform the advanced
    machines
  • They are slower but effective
  • Face recognition
  • 4-5 cycles versus billions of cycles
  • 1 cycle of the brain is extremely slower than a
    cycle of a ?C
  • Storing a face requires Mega Bytes in a computer
  • Examples
  • Genetic Algorithms
  • Neural Networks
  • Artificial Immune Systems

9
Learning Computational and Biological Viewpoints
  • Computational viewpoint
  • Learning is about a method of representing
    functions using network of simple arithmetic
    elements, and about methods for learning such
    representations from examples
  • Biological viewpoint
  • The simple arithmetic computing elements
    correspond to neurons-the cells that perform
    information processing in the brain.
  • The network as a whole corresponds to a
    collection of interconnected neurons.
  • Besides computational properties, neural networks
    may offer the best chance of understanding many
    psychological phenomena that arise from the
    specific structure and operation of the brain.

10
Some History The Foundations of AI
  • Philosophy (428 B.C.- present)
  • Mathematics (c. 800 - present)
  • Psychology (1879 - present)
  • Computer Engineering (1940 - present)
  • Linguistic (1957 - present)

11
Some History AI from past to now
  • The gestation of artificial intelligence (1943 -
    1956)
  • knowledge of the basic psychology and function of
    neurons in the brain
  • Turings theory of Computation.
  • Early Enthusiasm, great expectations (1952-1969)
  • Lisp
  • Adalines by Bernie Widrow, 1960 ( Enhanced
    version of Hebbs learning)
  • Perceptrons by Frank Rosenblatt, 1960 (Perceptron
    Convergence Theorem)
  • A dose of reality ( 1966-1974)
  • Principle versus practice
  • Machine evolution (now called Genetic Algorithm)
  • Very large computational time, Combinatorial
    Explosion
  • Some fundamental limitations

12
Some History AI from past to now
  • Knowledge-based systems The key to the power?
    (1969-1979)
  • Expert systems Medical diagnosis
  • Frames (Minsky, 1975) Collecting together facts
    about particular object and event types, and
    arranging the types into a large taxonomic
    hierarchy analogous to a biological taxonomy.
  • AI becomes an industry (1980-1988)
  • R1 the first commercial expert system.
  • The Fifth Generation project, by Japanese, to
    build intelligent computers
  • The Microelectronics and Computer Technology
    Corporation
  • Chip design and human-interface research
  • The booming AI industry
  • Software Carnegie Group, Inference, Intellicorp,
    and Teknowledge
  • Hardware Lisp machines Inc., Texas Instruments,
    Symbolics, and Xerox
  • The industry vent from a few million in sales in
    1980 to 2 billion in 1988

13
Some History AI from past to now
  • The return of neural networks (1986-present)
  • Large collection of neurons large collection of
    atoms in Physics.
  • Hopfield (1982) statistical mechanics to analyze
    the storage and optimization properties of
    networks.
  • David Rumelhart and Geoff Hinton the study of
    neural net models of memory.
  • Reinvention of Back-propagation algorithm ( mid
    1980s)
  • AI versus neural networks AI Winter
  • The fear
  • Historical factors

14
Some History AI from past to now
  • Recent Events (1987-present)
  • Hidden Markov Models (HHMs) successfully applied
    in Speech
  • Judea Pearls (1980) Probabilistic Reasoning in
    Intelligent Systems
  • The belief networks formalism was invented to
    allow efficient reasoning about the combination
    of uncertain evidence.
  • They are claimed to be the best representation of
    the human belief and reaoning.
  • Normative expert systems by Judea Pearl , Eric
    Horvitz and David Heckerman
  • Ones that act rationally according to the laws
    of decision theory and do not try to imitate
    human experts. - Think rationally and act
    rationally.
  • Distributed intelligent systems
  • Internet computing, mobile robots, autonomous and
    collaborative systems

15
Artificial Immune System
  • Introduction to human Immune system
  • The human immune system
  • Types of immunity
  • Type of immune system
  • Features of vertebrate immune system
  • Artificial immune system and properties
  • Application of immune system

16
Introduction to Immune systems
  • The human immune system
  • is a natural defense mechanism
  • maintains the system against dynamically changing
    environments
  • sophisticated information processors
  • learns to recognize patterns
  • cells do the job of encoding, controlling the
    system in parallel
  • immune system is a distributed system with no
    central controller

17
The Human Immune System
  • The main function of the human immune system
  • is to protect our body from infectious agents
    such as viruses, bacteria, fungi, and other
    parasites.
  • The basic components of the immune system
  • are the lymphocytes or the white blood cells
  • Two types of lymphocytes
  • B- lymphocytes
  • T- lymphocytes

18
The Human Immune System
  • B-lymphocytes
  • are produced by the bone marrows
  • roughly there are 10 million B-lymphocytes in the
    human body.

19
The Human Immune System
  • Distinct chemical structures and produces many Y
    shaped antibodies from its surfaces

20
Types of Immunity
  • Innate Immunity - Invertebrate immune system
  • its the natural resistance of the body to the
    foreign antigens.
  • Non-specific towards invaders into the body
  • Acquired Immunity - Vertebrate Immune system
  • Directed towards specific invaders
  • Immunological memory is modified by exposure to
    such foreign antigens.

21
Spreading influence of the antigen
22
Spreading influence of the antigen
  • When an antigen enters the body the B cells binds
    it
  • B cell analyzes the antigen and also creates new
    B cells
  • Each B cell passes the antigen onto other B cell
    objects within its neighborhood
  • The number of neighbors which are presented with
    the antigen depends on how many cells have
    already possessed the antigen
  • Antigen spreads through the network gradually
    decreasing in concentration as it goes

23
Types of Immune systems
  • Two types of immune systems are
  • 1. Vertebrate lymphocytes
  • Involves lymphocytes, which are antigen specific
  • Different receptors for difference antigens
  • 2. Invertebrate immune systems phagocytes
  • Involves Phagocytes, which is non-specific immune
    response
  • No distinct receptors for specific antigens
  • Tries to kill any antigen
  • Malfunction of this system Leukemia.
  • B-cells attacks the blood cells as if they are
    foreign.

24
Features of vertebrate Immune system
  • Feature extraction to determine the unique
    nature of the antigen.
  • Learn to recognize new patterns/antigens.
  • Work as distributed pattern recognizer.
  • Use content addressable memory to retrieve known
    patterns/antigens. Learning!!!
  • Use of specific proliferation and
    self-replication for quick recognition and
    response. Reproduction!!!
  • Eliminate/neutralize the effect of antigens in a
    systematic pattern.

25
Artificial Immune system
  • Inspiration to engineering sciences
  • Performing complex tasks such as
  • learning, memory of large number of components,
  • immunity development over time.
  • Artificial Immune system is essentially for the
    imitation of the immune system properties to
    computers and application to various fields

26
Properties of Immune system applicable to AIS
  • Clonal selection Principle
  • Reinforcement learning
  • Immune memory
  • Jernes idiotropic network theory
  • Positive and negative selection
  • Affinity maturation
  • Self organization

27
Clonal Selection Principle
  • Clonal selection principle
  • Only those cells that recognize the antigens
    reproduce
  • New cells are copies of their parents (clone)
    cells
  • Elimination of newly differentiated lymphocytes
  • Proliferation and differentiation on contact of
    mature cells with antigens

28
Jernes Idiotropics Network
  • Jernes hypothesis states that
  • antibody does not exist independently in living
    organisms
  • communicate with each other through idiotope and
    paratope
  • The portion of the antigen and the antigen
    recognized by the antibody is called the epitope
  • The one on the antibody that recognizes the
    corresponding epitope is called paratope.
  • Antibodies also have antigenic characteristic
    called idiotope.

29
Jernes Idiotropics Network
Ag
B Cell 2
Id2
B Cell 1
Ab2
P2
Id1
B Cell 3
Ab1
P1
Id3
Ab3
P3
30
Immune memory
  • Immune memory
  • Immune system remembers the already entered or
    attacked antigen
  • Primary response system evokes the antibodies
  • Secondary response remembers the attacked
    antigen
  • More rapid
  • shorter lag phase
  • higher rate
  • longer persistence of antibody synthesis

31
Immune memory
  • Cross reactive response
  • Uses the property of associative memory
  • For two similar antigens , immune system
    responds faster to the second by associating the
    response with the first antigen
  • It is found useful in artificial intelligence and
    neural networks

32
An overview of immune system
33
An overview of Immune system
34
Autonomous Multi-Agent Systems
  • Distributed Artificial Intelligence (DAI)
  • As a sub field of AI, it has existed for less
    than two decades. DAI is concerned with systems
    that consists of multiple independent entities
    that interact in a domain.
  • Two sub disciplines of DAI
  • Distributed Problem Solving (DPS),
  • Multi-Agent Systems (MAS).

35
Autonomous Multi-Agent Systems
  • Deals with behavior management in collections of
    several independent entities, or agents.
  • There are many definitions for an Agent
  • An agent is an entity with goals, actions, and
    domain knowledge all situated in an environment
  • The way the agent acts is its behavior, and
    there should be an interaction between this
    behavior and the environment that surrounds him.

36
Dog and Sheep problem
For Simplicity
Sheep
Dog
Dog task Force the sheep to return to the
pen Sheep task Avoid the dog
37
Dog and Sheep problem
Distance ( Sheep, Pen )
Distance ( Dog , Sheep)
Distance ( Dog , Sheep)
Distance ( Dog , Pen )
Sheep
Dog
Adaptation
Adaptation
Direction
Direction
38
Dog and Sheep simulation
  • Two Dogs one sheep

S
D2
D1
Area of focus
39
Dog and Sheep Identical behavior
40
Dog and sheepD2 solely different behavior
41
Board Game and AIS
Antigen
B cells
42
Board Game and AIS
Antigen
B cells
43
Board Game and AIS
44
Application of AIS
  • Negative selection,self/non self learning
  • Immune network dynamics/negative selection
    principle
  • Negative selection principle
  • Distribution, self organization
  • Anomaly detection-computer security in terms of
    viruses, unauthorized user detection and
    elimination
  • Image inspection image segmentation
  • Novelty detection algorithm for time series data
    to exhibit the normal behavior of the system
  • Agent based approach,Network intrusion detection

45
Application of AIS
  • Application to artificial immune system
  • Robots, Mutual interaction between
    modules,interaction between robot environments,
  • autonomous agents
  • Adaptive control, identification and synthesis,
    sequential control
  • Optimization
  • Neural network approaches
  • Immune System Property
  • Dynamic decentralized consensus making mechanism,
    Jernes network, clonal selection algorithm and
    network dynamics
  • Petri net concepts
  • Immune diversity, network theory and clonal
    selection principle
  • Cross reactive memory, recruitment mechanism

46
Application of AIS
  • Genetic mechanisms, clonal selection principle,
    affinity maturation, content addressable memory,
    matching mechanisms, and network self organizing
    properties.
  • Immune networks
  • Pattern recognition-classification, prediction,
    diagnosis and data mining
  • Sensor based diagnosis

47
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