David Lamb Introductory Presentation

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David Lamb Introductory Presentation Powered By Docstoc
					       David Lamb
 Introductory Presentation

         Room 608, ext 2280
   Overview of research to date
       Cognitive Immunity Subsystems
       Detection – Machine Learning techniques
          Danger Theory
          Novelty Detection

          Chance Discovery

       Anticipatory Learning Classifier Systems
   The “Research Problem”
   Planned Future Research
                Cognitive Immunity

   A Cognitive Immune system should:
       Identify problems:
            both vulnerabilities and newly-introduced problems
       Diagnose the cause and severity of
       Then, ideally, restore the system to correct
    Cognitive Immunity: Subsystems
   A proposed layered model of CI subsystems:

       Detection – detects events in environment
       Diagnosis – maps/combines events to form
       Planning – plans actions to resolve a situation
       Enactment – assesses impact of actions, and
        performs them
       Learning/Evolution – evolves the system based on
        feedback from environment
       Self-Organisation – re-organises the system to
        avoid vulnerabilities
               Current Research
   Research thus far has been in the area of
    Cognitive Immunity:
       Detection and Diagnosis subsystems
   Tried to concentrate on mechanisms that can
    provide services suitable for Detection
   Also looked at existing cognitive software /
    system models to gain insight into the design of
    this type of software

The following slides aim to present an overview of
  this research…
Novelty Detection: Introduction
   Novelty Detection systems are concerned
       Detecting the data in a given set of inputs that
        may be considered abnormal – or novel.
       Effectively generalising the known type – i.e.
        not just a pattern match!
   Useful to Immune Systems as a detector –
    a known vs. unknown discriminator,
    allowing a response to unknown data or
     Novelty Detection: Statistics
   Traditional Mathematical / Statistical approaches
    can determine novelty by:
       Plotting all known data, according to its defining
        attributes, in an n-dimensional space.
       Identifying clusters of plots as known types or classes
       Identifying plots outside of these clusters as novel,
        abnormal, or unrecognised
   This approach is complicated by:
       High-dimensional data
       Outliers (standalone plots outside a cluster) – both
        legitimate and those as a result of noisy data
       Quality of known data samples
    Novelty Detection: Neural Networks 1
   Trained Neural Networks can be used as Novelty
   Training a Neural Network – an overview:
        Standard Multi-Layer Perceptron Neural Networks can
         be trained to produce certain outputs for certain
        This is achieved by repeatedly presenting the network
         with sample input data, and appropriate target output
        After many training cycles, the network will reproduce
         the target output data for the specified inputs
        If the network inputs described the data well and the
         training data varied sufficiently, the network should
         perform well on data similar to the training data
    Novelty Detection: Neural Networks 2
   This allows MLP networks to behave as data classifiers:
        Training data is comprised of samples of known types and
         suitable output class indication
        A high signal on a particular network output indicates a
         particular class/type has been presented as input
        Confidence scores can be added to quantify the confidence in
         any given classification
   However, using classification networks for novelty
    detection poses the following problems:
        Accurate classification clearly depends on good quality training
        Expensive to retrain to recognise additional classes - typically a
         full retraining from scratch is required
        May result in confusion at outputs when presented with truly
         novel data
     Novelty Detection: SO(F)Ms
   Self Organising (Feature) Maps:
       A special type of neural network that
        undergoes unsupervised training
            i.e. the network is trained solely on input data, and
             doesn’t require additional target output data
       Can easily derive classes (clusters) of data
        based on the variety in the input set
       SOM visualisations are particularly appropriate
        for presenting high dimensional data in a 2D
        map output
Novelty Detection: Other Approaches
   Detectors / Selection approaches
       Known data is coded, typically as binary strings
       A set of random detectors are created as strings
       The random detectors are tested on the known/self
       Those that match (against self) are eliminated
   Evolutionary approaches (genetic algorithms)
       Rules to match known/unknown are coded in strings
       Generations are evolved based on fitness of previous
        “parents” and modification via evolution operators:
            Mutation – one or more bits are changed
            Combination/Crossover – x bits from one parent, y bits from
             other parent
Novelty Detection and Classification
   In addition to differentiating between known
    and unknown, several of the proposed novelty
    detectors can also serve as advanced classifiers
   Classification can also prove useful to the
    Detection layer of an Immune System

   An ideal data classifier should:
       Generalise classes (or types) of inputs
       Operate at a higher, more abstract level than a simple
        pattern match:
            (i.e. not just X = Y, but X is similar to Y)
    The Danger Theory: Introduction
   Originated in biological immunology
   Changes emphasis of response to a
    specified Danger Signal, rather than
    reacting purely to non-self
   Can provide a localised response (within the
    Danger Zone)
   Simple, (mostly) independent interactions
    repeated on a large scale produce the
    desired immune response
The Danger Theory: Biological Model
   Response to Danger Signal (in the illustrated
    case, cell damage) triggers antibody reaction
    within the Danger Zone
    Matching antibodies are then duplicated to
    facilitate more antigen matching
The Danger Theory in Software

   Using the Danger Theory model in
    software presents some problems:
       Representation of Danger Signal(s)
       Representation of spatial Danger Zone
       How to implement antibody / antigen
       How to implement antibody suppression of
Chance Discovery: Introduction

   New-ish field, some disagreement on
    definition and application:
       Some argue it is simply a variation on existing
        data mining themes
   Broad Definition, “Discovers valuable
    chance events – those that are rare, but
    Chance Discovery: Overview
A Chance Discovery system must be able
to perform two main tasks:
   Identification/Prediction of Chance Events
   Identification/Prediction of Consequences:
       Identifying consequences – e.g. associate
        cause with effect, based on system history.
        Find the value (or cost) of the cause.
       Prediction of consequences – where history is
        not available or inappropriate, prediction with
        bounded accuracy
Chance Discovery: Current Systems
Despite the fact that the field is quite new, some
  prototype/research CD systems exist:
 Key Graphs
     A method initially created to index documents
     Clusters co-occurrences of terms in a document
     These clusters should indicate topics
     Index terms are then chosen based on their
      relationship to other clusters
     Chance events (i.e. index terms) are chosen based on
      their links to significant high-frequency events (term
Chance Discovery: Current Systems
   Knowledge Base – (Change-based CD)
       World knowledge is modelled as rules in a KB
       Chance discoveries are made as this knowledge is
        changed, based on the fact that changes may:
            Enable/disable some goal(s)
            Alter the cost/reward of achieving some goal(s)
   Dialogue approach
       Dialogue facilitates communication between separate
        knowledge bases
       Can be viewed as a distributed extension of the KB
        approach, deals with separate (and possibly differing)
  (Anticipatory) Learning Classifier
ALCS – Currently research in progress!
 ALCS are cognitive systems that form
  anticipations about future events based on
  current behaviour and observations
 They are of interest, as they may
  represent a significant building block
  towards a CI system model…
              ALCS: Components
   Two essential ALCS components:
       ALP – Anticipatory Learning Process –
        compares anticipations with actual results,
        resulting in specialised rules that describe the
        observed behaviour.
       Genetic Generalisation Mechanism –
        Generalises accurate rules from the over-
        specified ALP output, making the model more
       The “Research Problem”
   How to create a reusable model for
    Cognitive Immunity, and to find
    components suitable for use in that model
   How to apply that model to a real-world
   How to implement a complete system,
    using the proposed subsystems for a real-
    world problem
              Planned Research
   How do I get from where I am now to
    where I want to be?
   Which significant areas of research must
    be covered?
       A continuation of ALCS research, plus…
       Research into other types of Cognitive
        Systems and Artificial Immune Systems to
        understand the various ways of modelling
        these systems
       Research into more components or services
        suitable for the identified CI subsystems
    The End

Thanks for listening!!

   Any questions?

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