ARTIFICIAL IMMUNE SYSTEM

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ARTIFICIAL IMMUNE SYSTEM

• FERAT SAHIN
• R.S. Srividhya
• KGCOE Colloquium Series II
• 01/12/2000

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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

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Are Humans Perfect Machines?

• Learning
• Continuous
• Selective
• Memory
• Short term, Long term
• Selective
• Recall
• Processing
• Recognition (hearing, sight, various senses)

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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.

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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.

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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!!!?

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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.

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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

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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.

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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)

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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

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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

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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

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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

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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

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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

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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

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The Human Immune System

• B-lymphocytes
• are produced by the bone marrows
• roughly there are 10 million B-lymphocytes in the
  human body.

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The Human Immune System

• Distinct chemical structures and produces many Y
  shaped antibodies from its surfaces

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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.

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Spreading influence of the antigen
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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

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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.

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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.

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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

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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

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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

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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.

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Jernes Idiotropics Network
Ag
B Cell 2
Id2
B Cell 1
Ab2
P2
Id1
B Cell 3
Ab1
P1
Id3
Ab3
P3
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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

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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

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An overview of immune system
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An overview of Immune system
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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).

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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.

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Dog and Sheep problem
For Simplicity
Sheep
Dog
Dog task Force the sheep to return to the
pen Sheep task Avoid the dog
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Dog and Sheep problem
Distance ( Sheep, Pen )
Distance ( Dog , Sheep)
Distance ( Dog , Sheep)
Distance ( Dog , Pen )
Sheep
Dog
Adaptation
Adaptation
Direction
Direction
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Dog and Sheep simulation

• Two Dogs one sheep

S
D2
D1
Area of focus
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Dog and Sheep Identical behavior
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Dog and sheepD2 solely different behavior
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Board Game and AIS
Antigen
B cells
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Board Game and AIS
Antigen
B cells
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Board Game and AIS
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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

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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

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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

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Thank you