artificial systems 2
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Adaptive Systems
Lecture 9: Artificial Adaptive Systems (2)
Dr Giovanna Di Marzo Serugendo
Department of Computer Science
and Information Systems
Birkbeck College, University of London
Email: [email protected]
Web Page: http://www.dcs.bbk.ac.uk/~dimarzo
http://www.dcs.bbk.ac.uk/~dimarzohttp://www.dcs.bbk.ac.uk/~dimarzohttp://www.dcs.bbk.ac.uk/~dimarzohttp://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 recruitnestmates
Size of group is adapted to size of prey Pheromone used to recruit nestmates
Coordination for transporting prey occurs throughindirect 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) ->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
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Region Detection
http://media.star-telegram.com/Multimedia/News/Photos/Bigweb.jpg
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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
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Region Detection
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Region Detection
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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 realisingthe 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, andmakes 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
time
Orders - Agent
Ra
Rb
Rc
Rd
t1
EA1
Ra
Rb
Rc
Rd
t2
EA2
Ra
Rb
Rc
Rd
t3
EA3
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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 ofantigen and antibody
Ab = (Ab1, ..., Ab
L) Ag = (Ag
1, ..., Ag
L)
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)
L
i iiAgAbD
1
2)(
L
i iiAgAbD
1)(
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 (tobe 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 detectedantigens
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 specificantigen
Proportional to degree of affinity (higheraffinity, 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 minimalimpact)
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 oneregenerated
(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 atrail 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 themselvesgivenhigh-level objectivesfrom
administrators [Kephart03]
35
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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 followhigh-level policies. Rest of System adjusts automatically and
seamlessly [Kephart03]
Self-Optimisation (parameters)
Components and systems continually seek opportunities toimprove their own performance and efficiency [Kephart03]
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Autonomic Computing
Self-Healing (error detection, diagnostic, repair)
System automatically detects, diagnoses, and repairs localizedsoftware 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 andprevent 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) handlingresources (servers S1, S2)
Resources are dynamically allocated on the basis ofpolicies
If application manager cannot apply its policy, it asks aResource Arbiter (RA) for additional resources
RA
AM1 AM2
S1 S2
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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 > 100 ms and CPU availability > 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]
),(iii DSU
i iii DSU ),(
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Unity
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Readings
[Bonabeau 99] E. Bonabeau, M. Dorigo, and G. Thraulaz. SwarmIntelligence: From Natural to Artificial Systems Santa Fe Institute Studies onthe 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 MobilityArchitecture 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 swarmmechanism based on social spiders colonies: from web weaving toregion detection. In Web Intelligence and Agent Systems: AnInternational 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] Mrk Jelasity and Ozalp Babaoglu: T-Man: Gossip-based overlay topology management. In Proceedings ofEngineering Self-Organising Applications (ESOA'05), July 2005.
[Kephart 03] J. Kephart, D. Chess: The Vision of AutonomicComputing, IEEE Computer, January 2003, 36(1):41-50, 2003.