Adaptive Systems Lecture 9: Artificial Adaptive Systems 2

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Adaptive SystemsLecture 9 Artificial Adaptive
Systems (2)

• Dr Giovanna Di Marzo Serugendo
• Department of Computer Science
• and Information Systems
• Birkbeck College, University of London
• Email dimarzo_at_dcs.bbk.ac.uk
• Web Page http//www.dcs.bbk.ac.uk/dimarzo

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Lecture 8 Review

• Taxonomy / Classification
• Static Optimisation Problems
• Ant-Colony Optimisation
• Particle Swarm Optimisation
• Dynamic Optimisation Problems
• Trust-based access control

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Lecture 9 Overview

• Swarms
• Robots
• Spiders-based systems
• Manufacturing Control
• Immune Computer
• P2P Systems
• Autonomic Computing

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Swarms of Robots

• Cooperative prey transport by social insects
• Ants recruit other ants for collaborative
  transport of preys too heavy to be carried by a
  single ant
• E.g. 100 ants transporting a worm (5000 times
  bigger than each single ant)
• Resistance to traction decides ants to recruit
  nestmates
• Size of group is adapted to size of prey
• Pheromone used to recruit nestmates
• Coordination for transporting prey occurs through
  indirect communication (stigmergy)
• Actual transport involves
• re-alignment and re-positioning

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Swarms of Robots

• Application
• Swarms of robots Bonabeau 99
• Collaborative box-pushing
• Indirect communication
• Decentralised control
• Goal
• Localise a box in a given space and push it
  towards an edge
• Subsumption architecture
• Every behaviour is subdivided into atomic
  sub-behaviours activated when necessary (reactive
  approach)
• Each sub-behaviour has its own sensors inputs and
  actuators outputs
• Hierarchy of behaviours with priority
• Arbitration module controls actual activation of
  sub-behaviour

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Swarms of Robots

• Sensors
• Left/Right infrared (obstacles) and photocells
  sensors (box)
• Steering actuator
• Left/Right wheel motors
• Behaviours definition
• Find (box) lowest priority
• Follow (other robot)
• Slow (neighbour collision)
• Goal (move towards box)
• Avoid (obstacle collision change direction)
  highest priority

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Swarms of Robots

• Scenario
• Goal activated
• Follow and Goal set motion
• Avoid stop current process (Goal deactivated) -gt
  re-alignment and re-positioning
• Goal of one robot is re-activated
• No direct communication (stigmergy)
• Robots implementation
• Model and simulation of ants prey transports

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Swarms of Robots

• Adaptation
• Different configurations
• Box positioning, robots placements

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Region Detection

• Metaphor Social Spiders
• Few species of spiders are social
• Sharing of web
• Collaboration (preys, web weaving)
• Stigmergy based on silk
• Spiders follow silk or move to points where silk
  is fixed

Adaptive Systems - Giovanna Di Marzo Serugendo
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Region Detection
http//media.star-telegram.com/Multimedia/News/Pho
tos/Bigweb.jpg
Adaptive Systems - Giovanna Di Marzo Serugendo
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Region detection

• Region detection (grey levels) Bourjot 03
• Partition of image into subsets of separate
  objects
• Determination of sets of connected pixels
  (regions)
• Idea
• Webs weaving determines the region
• Algorithm
• Spider has to detect a given region (grey level)
• Several spiders explore image and fix silk on
  relevant pixels
• Silk attraction
• Resulting web is fixed on interesting pixels

Adaptive Systems - Giovanna Di Marzo Serugendo
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Region Detection




Adaptive Systems - Giovanna Di Marzo Serugendo
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Region Detection
Adaptive Systems - Giovanna Di Marzo Serugendo
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Manufacturing Control

• Manufacturing control
• Management of internal logistic and of production
  system
• Routing of product instances
• Assignment of workers
• Assignment of raw material
• Operations begin and end
• Dynamic environment
• Failures, new products, equipment upgrades, etc.
• Idea
• Decentralised control with self-organising
  behaviour

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Manufacturing Control

• Metaphor Ant foraging
• PROSA Architecture Hadeli 03
• Agents
• Orders agents (logistics for managing products),
  products agents (processes tasks), resources
  agents (raw material, machines, etc)
• Mapping of control and production system into
  agents
• Actual production system is reflected into an
  agents structure
• Each resource/product/order has a corresponding
  resource/product/order agent (local information
  only)
• Links among agents (e.g. order agents know about
  location of resources agents to products agents
  necessary to complete order)
• Agents creates ant-agents (mobile agents) that
  explore the cyber production system and
  deposit/sense pheromone

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Manufacturing Control

• Ant-agents behaviour
• Feasibility information ants
• Information related to the resource locations
  (availability, speed, etc.)
• Exploring ants
• Order agents create several ants each exploring a
  way of realising the order (gives back a report
  with followed route)
• Intention propagation ants
• Order agents create ants that propagate
  information about the orders intentions (chosen
  best route). Ant has a fixed route, and makes
  bookings.
• Manufacturing control
• Obtained from the choices made by order agents
• On the basis of the above information
• Actually executed by resources agents

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Manufacturing Control

• Exploring Ants (EA)
• Tries to find solutions
• Searching for solutions is guided by local
  pheromones
• Reports result of solution to the corresponding
  Order Agent

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Manufacturing Control

• Adaptation
• Actual factory status
• Current orders

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Artificial Immune System

• Design of an artificial immune system
• Representation for the components of the system
• Mechanisms to evaluate the interaction of
  individuals with environment and with each other
• Procedures of adaptation dynamics of system
• Used for
• Modelling immune systems
• Solving problems using artificial immune systems

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Artificial Immune System

• Representation
• Abstract model of immune cells and molecules
• Recognition of antigen by cell (antibody)
  receptor
• Occurs through shape complementarity or shape
  similarity
• Model of shape recognition
• Data structure attribute string
• Real-valued vector / Integer vector / ...
• Shape recognition is based on
• Similarity/affinity measure between attribute
  strings of antigen and antibody
• Ab (Ab1, ..., AbL) Ag (Ag1, ..., AgL)?
• Affinity D SL x SL ? RL (degree of matching)?

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Artificial Immune System

• Evaluating interactions
• Affinity measure
• Affinity D SL x SL ? RL
• Complementary / Similarity
• A distance (Euclidian, Manhattan, Hamming)?

http//en.wikipedia.org/wiki/Immune_system
Ab 1 0 0 0 1 1 0 0 1
Ag 1 1 0 0 0 1 0 1 0 Match(Ab,
Ag) 0 1 0 0 1 0 0 1 1 Complementarity
affinity 4 (how different)? Similarity
affinity 5 (how similar)?
http//en.wikipedia.org/wiki/Immune_system
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Artificial Immune Systems

• Immune Algorithms
• Bone marrow
• Generate populations of immune cells and
  molecules (to be used in artificial immune
  system)?
• Negative selection
• Learning phase (avoid matching self)
• Define set of detectors (for anomaly detection)?
• Clonal selection
• Generate additional immune cells driven by
  detected antigens
• Immune networks
• Simulate immune networks

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Artificial Immune System

• Bone Marrow
• Generation of antibodies
• Model 1
• Generation of attribute string of length L with
  random values
• Model 2
• Generation of antibodies from gene library
• Concatenation of genes from gene library

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Artificial Immune System

• Negative Selection Algorithms
• T cells mature in Thymus
• Learn to distinguish self from non-self
• T cells that cannot distinguish self properly
  must be destroyed
• Model
• Create T cells bit strings of length L
• Test T cells against known set of self-patterns
  (S)?
• Discard the ones that match the self-patterns
  (affinity measure)?
• Otherwise allow T cell to enter set of detectors
• Monitoring/Protection
• test detectors against set of strings to protect
• If matching then an anomaly (non-self) has been
  detected
• Applications
• Computational security

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Artificial Immune Systems

• Clonal selection
• Proliferation of cells that recognise specific
  antigen
• Proportional to degree of affinity (higher
  affinity, higher proliferation)?

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Intrusion Detection

• Metaphor Mammalian Immune System (Lecture 2)
• Self/Non-Self recognition
• Each cell has a marker (self)
• Cells without marker (non-self) are considered
  antigen
• Immune system attacks antigen
• Agents of Immune System
• B cells - Detection
• Wait for antigen, replicate and release
  antibodies
• Antibodies mark antigen (intruders)
• T cells - Response
• Destroy marked cells
• B and T cells
• Transported by blood and lymphatic vessels across
  the whole body

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Intrusion Detection

• Characteristics of Immune System
• Robust
• Decentralised and distributed (no central
  control)
• Dynamic (new components created, destroyed,
  circulated)
• Tolerant to errors (failure of single components
  has a minimal impact)
• Adaptable
• Learn to recognise new infections
• Memory of past infections
• Autonomous
• No outside control

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Intrusion Detection

• Intrusion Detection - ARTIS Forrest 99
• ARTificial Immune System
• (Mobile) detectors circulating in the system
• Stand for the T, B cells and antibodies
• Detection
• Bit strings stand for proteins to detect
• Random generation of detectors (random string)
• Look for matching portions of strings (anomaly)
• Training
• If detector matches a self string then detector
  is destroyed and new one regenerated
• (Associative) Memory
• Mapping of identified non-self strings to
  responses

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Intrusion Detection

• Applied to
• Computer virus detection
• Host-based intrusion detection
• Network intrusion detection
• Proteins network traffic
• Strings
• (Source IP address, Destination IP address, TCP
  Port Service)
• Anomaly detection high frequency of connections
  
• Environment
• Network of computers
• Each computer runs a detector node
• Experiments
• Off-line with actual data, no mobile detectors
• Detection of attacks

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Intrusion Detection

• Adaptation
• Learning
• Memory
• Quick reaction for further identical intrusion
• Adaptation to changes in normal behaviour

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Network Intrusion Detection and Response

• Combination of Metaphors Foukia 05
• Intrusion Detection
• Metaphor Immune System
• Implementation mobile agents
• Anomaly bad sequence of events
• Alert triggers the diffusion of pheromone
• Intrusion Response
• Metaphor Ants foraging
• Implementation mobile agents
• Mobile agents trace the source of the attack
  (machine) as ants follow a trail for food
• Mobile Agents
• Software able to change its location (keeping its
  execution state)

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P2P / Networks

• T-Man Algorithm Jelasity 05.
• Generic protocol based on gossip communication
  model
• Goal network topology management problem
• Nodes randomly connected
• Re-organisation of connections to produce
  desirable topology
• Nodes become neighbours based on information such
  as geographic position, content, storage
  capacity
• Metaphor Gossip
• Periodic exchange and update of information among
  members of a group
• Allows aggregation of global information inside
  a population, social learning
• Parameters neighbourhood, level of precision of
  information

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P2P / Networks

• Principle
• Nodes maintain local list of of (logical)
  neighbours a fixed number of neighbours, say c
• For each neighbour a profile is stored
• Profile is relevant for the topology to achieve
  (type of data stored, ID, location, etc)
• Ranking function defines the target topology
  (e.g. distance)
• Serves for reorganising the set of neighbours
• Based on profile of the nodes (distance between
  profiles)
• Gossip message exchange
• Choice of  closest  neighbour based on ranking
  function
• Local exchange / combination of neighbours
  profile
• Merging of neighbours profile
• Keep c closest neighbours
• Drop the rest
• Nodes become closer and closer
• Allows adaptation of neighbours list
• Re-organisation of the network topology

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P2P / Networks

• Applications
• Overlay networks supporting P2P systems
• Maintenance or establishment of P2P topology
• Sorting, Clustering, Distributed Hash table

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Autonomic Computing
Autonomic computing computing systems
that can manage themselves given high-level
objectives from administrators Kephart03
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Autonomic Computing

• Metaphor human nervous system
• Regulation of vital functions
• Breath, blood pressure, heart beating,
• Seamlessly for human being
• Autonomic Computing
• Goal
• Machines that manages themselves
  (self-management)
• With highest performances 24/7
• Human Nervous System metaphor
• Not used for implementation!
• Artificial mechanisms employed

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Autonomic Computing

• Self-Configuration (installation, configuration,
  integration)
• Automated configuration of components and
  systems follow high-level policies. Rest of
  System adjusts automatically and seamlessly
  Kephart03
• Self-Optimisation (parameters)
• Components and systems continually seek
  opportunities to improve their own performance
  and efficiency Kephart03

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Autonomic Computing

• Self-Healing (error detection, diagnostic,
  repair)
• System automatically detects, diagnoses, and
  repairs localized software and hardware problems
  Kephart 03
• Self-protection (detection and response to
  attacks)
• System automatically defends against malicious
  attacks or cascading failures. It uses early
  warning to anticipate and prevent system wide
  failures Kephart 03

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Autonomic Element
Autonomic Manager
Managed Resource
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Autonomic Elements
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Example

• Two Applications Managers (AM1, AM2) handling
  resources (servers S1, S2)?
• Resources are dynamically allocated on the basis
  of policies
• If application manager cannot apply its policy,
  it asks a Resource Arbiter (RA) for additional
  resources

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Example

• Components
• Two application managers (AM1, AM2)
• Resource Arbiter (RA)
• Two Servers (S1, S2)
• Meta-data
• Servers Transaction Time
• Servers CPU availability

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Example

• Policies
• AM Policy1
• Increase CPU by 5 if response time is above
  100ms
• AM Policy 2
• If transaction time gt 100 ms and CPU availability
  gt 98, ask RA for more CPU
• RA Policy
• If request for CPU, grant and give priority to AM1

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Unity

• Autonomic Elements
• Application Environment Manager (AM)
• Management of environment resources and
  Communications
• Prediction about impact in increasing/decreasing
  resources
• Utility function
• Resource Arbiter (RA)
• Allocation of resources
• Computation of Optimum
• Resources (servers)
• Registry Location of autonomic elements
• Registry policy High-level policies (utility
  function)
• Sentinel Monitors elements for another element
• Chess 04

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Unity
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Readings

• Bonabeau 99 E. Bonabeau, M. Dorigo, and G.
  Théraulaz. Swarm Intelligence From Natural to
  Artificial Systems Santa Fe Institute Studies on
  the Sciences of Complexity. Oxford University
  Press, UK, 1999.
• Hadeli 03 Hadeli et al. Self-organising in
  Multi-Agent Coordination and Control using
  Stigmergy. LNAI 2977, Springer, pp. 105-123,
  2004.
• Foukia 05 N. Foukia IDReAM Intrusion
  Detection and Response executed with Agent
  Mobility Architecture and Implementation.
  AAMAS05, 2005.
• Hofmeyr 99 S. A. Hofmeyr, S. Forrest
  Architecture for an Artificial Immune System.
  Evolutionary Computation 7(1)45-68, 1999.

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Readings

• Bourjot 03 Bourjot, C. Chevrier, V. and
  Thomas, V.A new swarm mechanism based on social
  spiders colonies from web weaving to region
  detection. In Web Intelligence and Agent
  Systems An International Journal -  Vol 1, N.1,
  pp 47-64 WIAS. 2003.
• Chess 04 Chess et al. Unity Experiences with a
  prototype Autonomic Computing System. ICAC'04.
  2004.
• Jelasity 05 Márk Jelasity and Ozalp Babaoglu
  T-Man Gossip-based overlay topology management.
  In Proceedings of Engineering Self-Organising
  Applications (ESOA'05), July 2005.
• Kephart 03 J. Kephart, D. Chess The Vision of
  Autonomic Computing, IEEE Computer, January
  2003, 36(1)41-50, 2003.