Artificial Immune Systems: A New Computational Intelligence Approach

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Artificial Immune Systems A New Computational
Intelligence Approach
New Trends in Intelligent Information Processing
and Web Mining. Zakopane, Poland,June 2-5, 2003

• Jonathan Timmis
• Computing Laboratory
• University of Kent
• CT2 7NF. UK.
• J.Timmis_at_kent.ac.uk
• http/www.cs.kent.ac.uk/jt6

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• Novel paradigms are proposed and
• accepted not necessarily for being faithful
• to their sources of inspiration, but for
• being useful and feasible

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What do I want to achieve?

• Give you a taster of what AIS is all about
• Define an AIS
• Why do we find the immune system useful?
• Explain what AIS are
• Show you where they are being used
• Some high level case studies
• Comments for the future
• I wont
• Talk about all areas of AIS and applications
• Talk too much about how AIS relate to other
  bioinspired ideas (although I will mention it)
• Go into too much detail this is an introduction

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Outline

• What are AIS?
• Useful immunology
• Thinking about AIS
• Application Areas and Case Studies
• The Future

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Why the Immune System?

• Recognition
• Anomaly detection
• Noise tolerance
• Robustness
• Feature extraction
• Diversity
• Reinforcement learning
• Memory Dynamically changing coverage
• Distributed
• Multi-layered
• Adaptive

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A Definition

• AIS are adaptive systems inspired by theoretical
  immunology and observed immune functions,
  principles and models, which are applied to
  complex problem domains

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Some History

• Developed from the field of theoretical
  immunology in the mid 1980s.
• Suggested we might look at the IS
• 1990 Bersini first use of immune algos to solve
  problems
• Forrest et al Computer Security mid 1990s
• Hunt et al, mid 1990s Machine learning

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Scope of AIS

• Computer Security(Forrest949698, Kephart94,
  Lamont9801,02, Dasgupta9901,
  Bentley0001,02)
• Anomaly Detection (Dasgupta960102)
• Fault Diagnosis (Ishida9293, Ishiguro94)
• Data Mining Retrieval (Hunt9596,
  Timmis9901, 02)
• Pattern Recognition (Forrest93, Gibert94, de
  Castro 02)
• Adaptive Control (Bersini91)

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Scope of AIS (Cont)

• Job shop Scheduling (Hart98, 01, 02)
• Chemical Pattern Recognition (Dasgupta99)
• Robotics (Ishiguro9697,Singh01)
• Optimization (DeCastro99,Endo98, de Castro 02)
• Web Mining (Nasaroui02)
• Fault Tolerance (Tyrrell, 01, 02, Timmis 02)
• Autonomous Systems (Varela92,Ishiguro96)
• Engineering Design Optimization (Hajela96 98,
  Nunes00)
• And so on

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Outline

• What are AIS?
• Useful immunology
• Thinking about AIS
• Application Areas and Case Studies
• The Future

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Role of the Immune System

• Protect our bodies from pathogen and viruses
• Primary immune response
• Launch a response to invading pathogens
• Secondary immune response
• Remember past encounters
• Faster response the second time around

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How does it work A simplistic view
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Immune cells

• There are two primarily types of lymphocytes
• B-lymphocytes (B cells)
• T-lymphocytes (T cells)
• Others types include macrophages, phagocytic
  cells, cytokines, etc.

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Self/Non-Self Recognition

• Immune system needs to be able to differentiate
  between self and non-self cells
• Antigenic encounters may result in cell death,
  therefore
• Some kind of positive selection
• Some element of negative selection

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Antigen

• Substances capable of starting a specific immune
  response commonly are referred to as antigens
• This includes some pathogens such as viruses,
  bacteria, fungi etc .

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Immune Pattern Recognition

• The immune recognition is based on the
  complementarity between the binding region of the
  receptor and a portion of the antigen called
  epitope.
• Antibodies present a single type of receptor,
  antigens might present several epitopes.
• This means that each antibody can recognize a
  single antigen

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Clonal Selection
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Main Properties of Clonal Selection (Burnet, 1978)

• Elimination of self antigens
• Proliferation and differentiation on contact of
  mature lymphocytes with antigen
• Restriction of one pattern to one differentiated
  cell and retention of that pattern by clonal
  descendants
• Generation of new random genetic changes,
  subsequently expressed as diverse antibody
  patterns by a form of accelerated somatic
  mutation

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Immune Network Theory

• Idiotypic network (Jerne, 1974)
• B cells co-stimulate each other
• Treat each other a bit like antigens
• Creates an immunological memory

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Reinforcement Learning and Immune Memory

• Repeated exposure to an antigen throughout a
  lifetime
• Primary, secondary immune responses
• Remembers encounters
• No need to start from scratch
• Memory cells
• Continuous learning

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Learning (2)
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Immune System Summary

• Define host (body cells) from external entities.
• When an entity is recognized as foreign (or
  dangerous)- activate several defense mechanisms
  leading to its destruction (or neutralization).
• Subsequent exposure to similar entity results in
  rapid immune response.
• Overall behavior of the immune system is an
  emergent property of many local interactions.
• So it is useful?

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Outline

• What are AIS?
• Useful immunology
• Thinking about AIS
• Application Areas and Case Studies
• The Future

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Artificial Immune Systems

• AIS are adaptive systems inspired by theoretical
  immunology and observed immune functions,
  principles and models, which are applied to
  complex problem domains

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This Section

• General Framework for describing and constructing
  AIS models
• A short review of where AIS are used today
• Can not cover them all, far too many
• Also we are not experts in all application areas
  !
• Where are AIS headed?

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What do want from a Framework?

• In a computational world we work with
  representations and processes. Therefore, we
  need
• To be able to describe immune system components
• Be able to describe their interactions
• Quite high level abstractions
• Capture general purpose processes that can be
  applied to various areas

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General Framework for AIS
Immune Algorithms
Affinity Measures
Representation
Application Domain
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Representation Shape Space

• Describe the general shape of a molecule

• Describe interactions between molecules
• Degree of binding between molecules

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Representation

• Vectors
• Ab  ?Ab1, Ab2, ..., AbL?
• Ag  ?Ag1, Ag2, ..., AgL?
• Real-valued shape-space
• Integer shape-space
• Binary shape-space
• Symbolic shape-space

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Define their Interaction

• Define the term Affinity
• Distance measures such as Hamming, Manhattan etc.
  etc.
• Affinity Threshold

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Basic Immune Models and Algorithms

• Negative Selection Algorithms
• Clonal Selection Algorithm
• Immune Network Models
• Somatic Hypermutation

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Negative Selection (NS) Algorithms

• Forrest 1994 Idea taken from the negative
  selection of T-cells in the thymus
• Applied initially to computer security
• Split into two parts
• Censoring
• Monitoring

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Clonal Selection Algorithm (de Castro von
Zuben, 2001)

• 1. Initialisation Randomly initialise a
  population (P)
• 2. Antigenic Presentation for each pattern in
  Ag, do
• 2.1 Antigenic binding determine affinity to
  each P
• 2.2 Affinity maturation select n highest
  affinity from P and clone and mutate prop. to
  affinity with Ag, then add new mutants to P
• 3. Metadynamics
• 3.1 select highest affinity P to form part of M
• 3.2 replace n number of random new ones
• 4. Cycle repeat 2 and 3 until stopping criteria

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Discrete Immune Network Models (Timmis Neal,
2001)

• Initialisation create an initial network from a
  sub-section of the antigens
• Antigenic presentation for each antigenic
  pattern, do
• 2.1 Clonal selection and network interactions
  for each network cell,
• determine its stimulation level (based on
  antigenic and network interaction)
• 2.2 Metadynamics eliminate network cells with a
  low stimulation
• 2.3 Clonal Expansion select the most stimulated
  network cells and
• reproduce them proportionally to their
  stimulation
• 2.4 Somatic hypermutation mutate each clone
• 2.5 Network construction select mutated clones
  and integrate
• 3. Cycle Repeat step 2 until termination
  condition is met

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Somatic Hypermutation

• Mutation rate in proportion to affinity
• Very controlled mutation in the natural immune
  system
• Trade-off between the normalized antibody
  affinity D and its mutation rate ?,

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Case Study Data Mining
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Data mining Problem description

• More benchmark problem in this case
• Assume a set of labelled vectors
• Classification

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AIRS (Artificial Immune Recognition System)
Watkins 2003

• Clonal Selection
• Based initially on immune networks, though found
  this did not work
• Resource allocation
• Somatic hypermutation
• Eventually
• Antibody/antigen binding

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AIRS Mapping from IS to AIS

• Antibody Feature Vector
• Recognition Combination of feature Ball vector
  and vector class
• Antigens Training Data
• Immune Memory Memory cellsset of mutated
  ARBs

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Classification

• Stimulation of an ARB is based not only on its
  affinity to an antigen but also on its class when
  compared to the class of an antigen
• Allocation of resources to the ARBs also takes
  into account the ARBs classifications when
  compared to the class of the antigen
• Memory cell hyper-mutation and replacement is
  based primarily on classification and secondarily
  on affinity

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AIRS Algorithm

• Data normalization and initialization
• Memory cell identification and ARB generation
• Competition for resources in the development of a
  candidate memory cell
• Potential introduction of the candidate memory
  cell into the set of established memory cells

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AIRS Performance Evaluation
Fishers Iris Data Set
Pima Indians Diabetes Data Set
Ionosphere Data Set
Sonar Data Set
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Classification Accuracy

• Important to maintain accuracy

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Features

• No need to know best architecture to get good
  results
• Default settings within a few percent of the best
  it can get
• User-adjustable parameters optimize performance
  for a given problem set
• Generalization and data reduction

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aiNET Artificial Immune Network for Data Mining
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Problem description

• More benchmark problem in this case
• Assume a set of unlabelled vectors
• We can ask the questions
• Is there a large amount of redundancy?
• Are there any groups or subgroups intrinsic to
  the data?
• What is the structural or spatial distribution?

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aiNET Immune principles employed

• B-cells (antibodies)
• Antigens
• Antibody/antigen binding
• Clonal selection process
• Immune network theory
• Combined with statistical analysis tools

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Data mining Immune Network Algorithm

• 1. Initialization create an initial random
  population of network antibodies
• 2. Antigenic presentation for each antigenic
  pattern, do
• 2.1 Clonal selection and expansion
• 2.2 Affinity maturation
• 2.3 Clonal interactions
• 2.4 Clonal suppression
• 2.5 Metadynamics
• 2.6 Network construction
• 3. Network interactions
• 4. Network suppression
• 5. Diversity
• 6. Cycle repeat Steps 2 to 4 until a
  pre-specified number of iterations is reached.

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Data mining Mapping from IS to aiNET
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Data mining Clustering (aiNet)

• Limited visualisation
• Interpret via MST or dendrogram
• Compression rate of 81
• Successfully identifies the clusters

Training Pattern
Result immune network
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Data miningHierarchical Clustering (aiNET)
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Other Interesting Applications

• Immune Network for continuous learning (Neal
  2002)
• Track moving data over time
• Maintains clusters in absence of patterns
• Useful for dynamic environments
• Continuous Classification
• Email classification of interesting/non-interestin
  g emails
• Changing profile of the user
• Maintain classification accuracy
• Comparable to Naïve Bayes

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New Trends

• Danger Theory
• Not self/non-self but Danger/Non-Danger
• Immune response is initiated in the tissues.
  Danger Zone.
• This makes it context dependant
• Could this be useful for Web Mining?

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Summary

• Covered much, but there is much work not covered
  (so apologies to anyone for missing theirs)
• Immune metaphors
• Antibodies and their interactions
• Immune learning and memory
• Self/non-self
• Negative selection
• Application of immune metaphors

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The Future

• Rapidly emerging field
• Much work is very diverse
• Framework helps a little
• More formal approach required?
• Wide possible application domains
• What is it that makes the immune system unique?
• More work with immunologists
• Theories such as Danger theory, Self-Assertion
  may have something to say to AIS

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The Future (2)

• ARTIST A Network for Artificial Immune Systems
  (EPSRC funded network)
• Work towards
• A theoretical foundation for AIS as a new CI
• Extraction of accurate metaphors
• Immune System Modelling
• Application of AIS
• Train PhD students
• Fund workshops/meetings
• Coordinate and Disseminate UK based AIS research
  (links to Europe)

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The Future (hopefully)

• IT IS Information Technology Inspired by the
  Immune System
• FP 6 IP
• 16 institutions across Europe
• Create a European Library of immune algorithms
• Theoretical analysis of AIS
• Application of AIS
• Autonomous boat
• Immunoinformatics
• Web Mining
• Modelling of Immune System

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AIS Resources Books

• Artificial Immune Systems and Their Applications
  by Dipankar Dasgupta (Editor) Springer Verlag,
  January 1999.
• Artificial Immune Systems A New Computational
  Intelligence Approach by Leandro N. de Castro,
  Jonathan Timmis, Springer Verlag, November 2002.
• Immunocomputing Principles and Applications by
  Alexander O. Tarakanov, Victor A. Skormin,
  Svetlana P. Sokolova, Springer Verlag, April
  2003.

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AIS Related Events in 2003

• Special Session on Artificial Immune Systems at
  the Congress on Evolutionary Computation (CEC),
  December 8-12, 2003, Canberra, Australia.
• Special Session on Immunity-Based Systems at
  Seventh International Conference
  on Knowledge-Based Intelligent Information 
  Engineering Systems (KES), September 3-5, 2003,
  University of Oxford, UK.  
• Second International Conference on Artificial
  Immune Systems (ICARIS), September 1-3, 2003,
  Napier University, Edinburgh, UK.
•  Tutorial on Artificial Immune Systems at 1st
  Multidisciplinary International Conference on
  Scheduling Theory and Applications (MISTA), 12
  August 2003, The University of Nottingham, UK.
•  Tutorial on Immunological Computation at
  International Joint Conference on Artificial
  Intelligence (IJCAI), August 10, 2003, Acapulco,
  Mexico.
•  Special Track on Artificial Immune Systems at
  Genetic and Evolutionary Computation Conference
  (GECCO), Chicago, USA, July 12-16, 2003