Artificial Immune Systems

1
Artificial Immune Systems

• Andrew Watkins

2
Why the Immune System?

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

3
Definition

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

4
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

5
How does it work?
6
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 different antibodies can
  recognize a single antigen

7
Immune Responses
8
Clonal Selection
9
Immune Network Theory

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

10
Shape Space Formalism

• Repertoire of the immune system is complete
  (Perelson, 1989)
• Extensive regions of complementarity
• Some threshold of recognition

V

V
e
e
V
e
e




V
e
e


11
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

12
General Framework for AIS
Solution
Immune Algorithms
Affinity Measures
Representation
Application Domain
13
Representation Shape Space

• Describe the general shape of a molecule

• Describe interactions between molecules
• Degree of binding between molecules
• Complement threshold

14
Define their Interaction

• Define the term Affinity
• Affinity is related to distance
• Euclidian

• Other distance measures such as Hamming,
  Manhattan etc. etc.
• Affinity Threshold

15
Basic Immune Models and Algorithms

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

16
Bone Marrow Models

• Gene libraries are used to create antibodies from
  the bone marrow
• Use this idea to generate attribute strings that
  represent receptors
• Antibody production through a random
  concatenation from gene libraries

17
Negative Selection 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

18
Clonal Selection Algorithm (de Castro von
Zuben, 2001)

• Randomly initialise a population (P)
• For each pattern in Ag
• Determine affinity to each Ab in P
• Select n highest affinity from P
• Clone and mutate prop. to affinity with Ag
• Add new mutants to P
• endFor
• Select highest affinity Ab in P to form part of M
• Replace n number of random new ones
• Until stopping criteria

19
Immune Network Models (Timmis Neal, 2001)
Initialise the immune network (P) For each
pattern in Ag Determine affinity to each Ab in
P Calculate network interaction Allocate
resources to the strongest members of P Remove
weakest Ab in P EndFor If termination condition
met exit else Clone and mutate each Ab in P
(based on a given probability) Integrate new
mutants into P based on affinity Repeat
20
Somatic Hypermutation

• Mutation rate in proportion to affinity
• Very controlled mutation in the natural immune
  system
• The greater the antibody affinity the smaller its
  mutation rate
• Classic trade-off between exploration and
  exploitation

21
How do AIS Compare?

• Basic Components
• AIS ? B-cell in shape space (e.g. attribute
  strings)
• Stimulation level
• ANN ? Neuron
• Activation function
• GA ? chromosome
• fitness

22
Comparing

• Structure (Architecture)
• AIS and GA? fixed or variable sized populations,
  not connected in population based AIS
• ANN and AIS
• Do have network based AIS
• ANN typically fixed structure (not always)
• Learning takes place in weights in ANN

23
Comparing

• Memory
• AIS ? in B-cells
• Network models in connections
• ANN ? In weights of connections
• GA ? individual chromosome

24
Comparing

• Adaptation
• Dynamics
• Metadynamics
• Interactions
• Generalisation capabilities
• Etc. many more.

25
Where are they used?

• Dependable systems
• Scheduling
• Robotics
• Security
• Anomaly detection
• Learning systems

26
Artificial Immune Recognition System (AIRS)

• An Immune-Inspired Supervised Learning Algorithm

27
AIRS Immune Principles Employed

• Clonal Selection
• Based initially on immune networks, though found
  this did not work
• Somatic hypermutation
• Eventually
• Recognition regions within shape space
• Antibody/antigen binding

28
AIRS Mapping from IS to AIS

• Antibody Feature Vector
• Recognition Combination of feature Ball
  (RB) vector and vector class
• Antigens Training Data
• Immune Memory Memory cellsset of mutated
  Artificial RBs

29
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

30
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

31
Memory Cell Identification
A
Memory Cell Pool
ARB Pool
32
MCmatch Found
A
1
Memory Cell Pool
MCmatch
ARB Pool
33
ARB Generation
A
1
Memory Cell Pool
MCmatch
Mutated Offspring
2
ARB Pool
34
Exposure of ARBs to Antigen
A
1
Memory Cell Pool
MCmatch
Mutated Offspring
3
2
ARB Pool
35
Development of a Candidate Memory Cell
A
1
Memory Cell Pool
MCmatch
Mutated Offspring
3
2
ARB Pool
36
Comparison of MCcandidate and MCmatch
A
1
Memory Cell Pool
MCmatch
A
4
Mutated Offspring
3
2
MC candidate
ARB Pool
37
Memory Cell Introduction
A
1
Memory Cell Pool
MCmatch
A
4
5
Mutated Offspring
3
2
MCcandidate
ARB Pool
38
Memory Cells and Antigens
39
Memory Cells and Antigens
40
AIRS Performance Evaluation
Fishers Iris Data Set
Pima Indians Diabetes Data Set
Ionosphere Data Set
Sonar Data Set
41
(No Transcript)
42
AIRS Observations

• ARB Pool formulation was over complicated
• Crude visualization
• Memory only needs to be maintained in the Memory
  Cell Pool
• Mutation Routine
• Difference in Quality
• Some redundancy

43
AIRS Revisions

• Memory Cell Evolution
• Only Memory Cell Pool has different classes
• ARB Pool only concerned with evolving memory
  cells
• Somatic Hypermutation
• Cells stimulation value indicates range of
  mutation possibilities
• No longer need to mutate class

44
Comparisons Classification Accuracy

• Important to maintain accuracy

• Why bother?

45
Comparisons Data Reduction

• Increase data reductionincreased efficiency

46
Features of AIRS

• 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

47
More Information

• http//www.cs.ukc.ac.uk/people/rpg/abw5
• http//www.cs.ukc.ac.uk/people/staff/jt6
• http//www.cs.ukc.ac.uk/aisbook