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Artificial Immune Systems: A New Computational Intelligence Approach


Artificial Immune Systems: A New Computational Intelligence Approach New Trends in Intelligent Information Processing and Web Mining. Zakopane, Poland, – PowerPoint PPT presentation

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Title: Artificial Immune Systems: A New Computational Intelligence Approach

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.
  • http/

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

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

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

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

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

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
  • Forrest et al Computer Security mid 1990s
  • Hunt et al, mid 1990s Machine learning

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

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,
  • And so on

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

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

How does it work A simplistic view
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.

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,
  • Some kind of positive selection
  • Some element of negative selection

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

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
  • Antibodies present a single type of receptor,
    antigens might present several epitopes.
  • This means that each antibody can recognize a
    single antigen

Clonal Selection
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
  • Generation of new random genetic changes,
    subsequently expressed as diverse antibody
    patterns by a form of accelerated somatic

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

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

Learning (2)
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?

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

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

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?

What do want from a Framework?
  • In a computational world we work with
    representations and processes. Therefore, we
  • 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

General Framework for AIS
Immune Algorithms
Affinity Measures
Application Domain
Representation Shape Space
  • Describe the general shape of a molecule
  • Describe interactions between molecules
  • Degree of binding between molecules

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

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

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

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

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

Discrete Immune Network Models (Timmis Neal,
  • 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
  • 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

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

Case Study Data Mining
Data mining Problem description
  • More benchmark problem in this case
  • Assume a set of labelled vectors
  • Classification

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

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

  • 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

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

AIRS Performance Evaluation
Fishers Iris Data Set
Pima Indians Diabetes Data Set
Ionosphere Data Set
Sonar Data Set
Classification Accuracy
  • Important to maintain accuracy

  • No need to know best architecture to get good
  • 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

aiNET Artificial Immune Network for Data Mining
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?

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

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.

Data mining Mapping from IS to aiNET
Data mining Clustering (aiNet)
  • Limited visualisation
  • Interpret via MST or dendrogram
  • Compression rate of 81
  • Successfully identifies the clusters

Training Pattern
Result immune network
Data miningHierarchical Clustering (aiNET)
Other Interesting Applications
  • Immune Network for continuous learning (Neal
  • 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

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?

  • 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

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

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)

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

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

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,
  •  Special Track on Artificial Immune Systems at
    Genetic and Evolutionary Computation Conference
    (GECCO), Chicago, USA, July 12-16, 2003
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