1. Immunocomputing and Artificial Immune SystemsBy
Daniel Reeves

2. Copyright 2007 Daniel Reeves
Overview
Introduction
Immune System
Immune system characteristics
Artificial Immune Systems (AIS)
AIS algorithms and applications
Questions
Copyright 2007 Daniel Reeves

3. Copyright 2007 Daniel Reeves
Introduction
Immune System
Immunocomputing
Generation of new software and algorithms based in principles of immune system components (proteins, immune networks) leading to concept of immunocomputer.
Immunocomputer
Artificial Immune Systems
Copyright 2007 Daniel Reeves

4. Noteworthy AI related Immune System Characteristics
Pattern recognition
Replication
Genetic Alteration
Multi-level response capabilities
Swarm Behavior
Networks
Copyright 2007 Daniel Reeves

5. Immune System Physiology
Primary (creation)
Bone Marrow
Thymus
Secondary (interaction)
Spleen
Appendix
Tonsils
Lymphatic Nodes and Vessels
Peyer’s Patches
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6. Immune System Physiology
Immune System Cells
B-Cells
T-Cells
Foreign Cells
Pathogens
Antigens
Epitopes
Copyright 2007 Daniel Reeves

7. Immune System – Response Systems
Innate Immune System
1st line of defense
General recognition
Holds Pathogens at bay
Adaptive Immune System
Recognition
Memory
Response
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8. Primary Immunological Principles
Pattern Recognition
Adaptive immune responses
Self/non-self discrimination
Immune Network theory
Danger Theory
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9. Recognition and Binding
Antigens and Epitopes
Antibodies and Immunoglobulin
Affinity
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10. Adaptive Immune Responses
Clonal Selection Theory
Affinity maturation
Mutation
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11. Self/Non-self Discrimination
Self and non-self
Negative selection
Co-stimulatory signals
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12. Copyright 2007 Daniel Reeves
Immune Network Theory
Learning
Structure
Dynamics
Metadynamics
Self-tolerance
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13. Copyright 2007 Daniel Reeves
Danger Theory
Many types of cells (self and non-self)
Dangers to immune system from both
Difficult to tell what is and is not dangerous
Suggests: “Immune system is more concerned with damage (preventing destruction) than foreignness”
And: “antigen presenting cells are activated by danger signals given off by injured or damaged cells.”
Copyright 2007 Daniel Reeves

14. Artificial Immune Systems
Definition
“adaptive systems inspired by theoretical and experimental immunology with the goal of solving problems”
Immune engineering
Representation
Evaluation Mechanisms
Adaptation Procedures
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15. Immune Engineering - Representation
Immune Response by Shape
Model Shape Space
Real-valued
Integer-valued
Hamming
Symbolic
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16. Immune Engineering – Evaluation Mechanisms
Affinity Measures
Real-valued vectors
Euclidian & Manhattan Distances
Integer Strings
Hamming Strings
Hamming Distances
Binary-Hamming shape space
Affinity Threshold
Cross-reactivity threshold
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17. Copyright 2007 Daniel Reeves
AIS Algorithms
Bone Marrow
Negative Selection
Clonal Selection
Continuous immune network
Discrete immune network
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18. Copyright 2007 Daniel Reeves
Bone Marrow Models
Generate populations of cells
Genetic Libraries
Genotype and Phenotype
Knowledge Storage
Applications
Evolution of Genetic encoding of antibodies
Antibodies for Job scheduling
Copyright 2007 Daniel Reeves

19. Bone Marrow Pseudocode
Pseudo-Code (antibody generation)
procedure[Ab] = gene_library(N)
initialize L
for i from 1 to N
for j from 1 to n
geneIndex = rand(L[1])
Ab[i][j] = L(j,geneIndex)
end for
end procedure
Copyright 2007 Daniel Reeves

20. Negative Selection Algorithms
Define set of anomaly detectors
Applications
Network Intrusion Detection
Breast Cancer Diagnosis
Copyright 2007 Daniel Reeves

21. Negative Selection Algorithms
Pseudo-code (non-self generation)
Generate random detectors
Generate random set of possible detectors
Evaluate Affinity
Compare Random detectors to set of self patterns
Accept or Reject
If there is a match, reject the detector
If there is not a match, add detector to set of non-self detectors
Copyright 2007 Daniel Reeves

22. Clonal Selection and Affinity Maturation
Features
System responds to subset of cells
Affinity level to offspring generation relation
Affinity level to offspring mutation relation
Algorithms
Forrest’s Algorithm
ClonalG
Applications
Network Security
Job-Shop scheduling
Copyright 2007 Daniel Reeves

23. Copyright 2007 Daniel Reeves
Forrest’s Algorithm
Pseudo-code
Initialize population
Match antibodies & antigens for affinity
Score affinity of antibodies
Evolve antibody repertoire (GA)
Fitness measure
Choose antigen and sample of antibodies
Match each antibody to antigen
Add match score to antibody
Repeat for many antigens
Copyright 2007 Daniel Reeves

24. Copyright 2007 Daniel Reeves
ClonalG
Similar to Forrest’s algorithm but
Does have: affinity proportional selection and mutation
Doesn’t have: locality of immune response
Pseudocode
Initialize
Antigenic Presentation
Affinity Evaluation
Clonal Selection and expansion
Affinity Maturation
Metadynamics
Cycle
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25. Artificial Immune Networks
Two models
Continuous
Discrete
Immune system dynamic
Applications
Recommender System
Data Compression and Clustering
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26. Continuous vs. Discrete
Ordinary Differential Equations
Concentrations of antibodies
Used for simulation of immune networks
Iterative Process or difference equation
Variations in antibody number and structure
Used for solving problems
Copyright 2007 Daniel Reeves

27. Natural World AIS Mapping
Cells and Molecules
Affinity
Fitness
Bone-marrow models
Affinity Function
Hyper mutation
Affinity maturation
Clonal selection
Negative Selection
Immune network
Attribute Strings
Degree of match
Data structure quality
Generate structures
Quantify affinities
Maintain population
Promotes learning
Interaction with antigens
Self/Non-self detectors
Performs dynamics
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28. Copyright 2007 Daniel Reeves
AIS Example - TSP
Problem: Traveling Salesman Problem
Distribution of points in 2D plane
Visit each point once and make one circuit
Euclidian distance for path length
Bone Marrow Algorithm (BM) for initial candidate solutions (genes)
ClonalG Algorithm (CG) for generating better solutions (Affinity Maturation)
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29. Copyright 2007 Daniel Reeves
TSP Example
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Representation
Genes:
Strings of four x/y pairs
Gene Libraries:
Collections of genes
Antibodies:
Groups of five genes
Affinity Equation:
Repair Function:
Remove duplicate points and add in missing
Copyright 2007 Daniel Reeves

31. Copyright 2007 Daniel Reeves
Bone Marrow
Initialize gene libraries
Randomly chose point & 3 closest neighbors
Build 5 different libraries of genes
Build antibody population from gene libraries
Grab one gene from each library
Repair bad antibodies
Use repair function to repair antibodies
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32. Copyright 2007 Daniel Reeves
ClonalG
Initial Population:
Candidate solutions from bone marrow algorithm
Affinity Evaluation, Clonal Selection and Expansion:
Choose best candidate solutions from BM
Affinity Maturation:
Create clones to mutate based on affinity level
Mutate created clones based on affinity level
Metadynamics:
Kill off poor cells and get new cells from BM
Repeat Process until stopping criterion
Copyright 2007 Daniel Reeves

33. Questions?

34. Copyright 2007 Daniel Reeves
TO DO
Find Pictures of Immune system components
Copy table translation from natural to artificial
Copyright 2007 Daniel Reeves