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 1980’s. –Suggested we ‘might look’ at the IS 1990 – Bersini first use of immune algos to solve problems Forrest et al – Computer Security mid 1990’s Hunt et al, mid 1990’s – 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   V  V       

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 Application Domain Representation Affinity Measures Immune Algorithms Solution

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 AntibodyFeature Vector RecognitionCombination of feature Ball (RB)vector and vector class AntigensTraining Data Immune MemoryMemory cells—set 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 Memory Cell Pool A ARB Pool

32. MCmatch Found Memory Cell Pool A 1 ARB Pool MCmatch

33. ARB Generation Memory Cell Pool A 1 ARB Pool 2 MCmatch Mutated Offspring

34. Exposure of ARBs to Antigen Memory Cell Pool A 1 ARB Pool 2 MCmatch Mutated Offspring 3

35. Development of a Candidate Memory Cell Memory Cell Pool A 1 ARB Pool 2 MCmatch Mutated Offspring 3

36. Comparison of MCcandidate and MCmatch Memory Cell Pool A 1 ARB Pool 2 MCmatch Mutated Offspring 3 MC candidate 4 A

37. Memory Cell Introduction Memory Cell Pool A 1 ARB Pool 2 MCmatch Mutated Offspring 3 MCcandidate 4 5 A

38. Memory Cells and Antigens

40. Fisher’s Iris Data Set Pima Indians Diabetes Data Set Ionosphere Data SetSonar Data Set AIRS: Performance Evaluation

42. AIRS: Observations ARB Pool formulation was over complicated –Crude visualizationvisualization –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 –Cell’s stimulation value indicates range of mutation possibilities –No longer need to mutate class

44. Comparisons: Classification Accuracy Important to maintain accuracy AIRS1: AccuracyAIRS2: Accuracy Iris96.796.0 Ionosphere94.995.6 Diabetes74.174.2 Sonar84.084.9 Why bother?

45. Comparisons: Data Reduction Increase data reduction—increased efficiency Training Set SizeAIRS1: Memory CellsAIRS2: Memory Cells Iris120 42.1 / 65% 30.9 / 74% Ionosphere200 140.7 / 30% 96.3 / 52% Diabetes691 470.4 / 32% 273.4 / 60% Sonar192 144.6 / 25% 177.7 / 7%

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