UbiCom Book Slides

1
UbiCom Book Slides

• Chapter 10
• Autonomous Systems Artificial Life
• (All Parts, Short Version)

Stefan Poslad http//www.eecs.qmul.ac.uk/people/st
efan/ubicom
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Chapter 10 Overview

• Chapter 10 focuses on
• Internal system properties autonomous
• External interaction with any type of environment
• Focussing more on physical environment
• A lesser extent focussing on Human ICT
  environments

3
Introduction
4
Related Chapter Links

• Sometimes autonomy is seen as a property of
  intelligence (Chapter 8)
• Designing UbiCom systems to be autonomous enables
  them to be self-managed (chapter 12)

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Chapter 10 Overview

• The slides for this chapter are split into
  several parts
• Part A Autonomous Systems Basics ?
• Part B Reflective Self-Aware Systems
• Part C Self-Management Autonomic Computing
• Part D Complex Systems
• Part E Artificial Life

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(No Transcript)
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Part A Outline

• Basics ?
• Types of Autonomous System
• Autonomous Intelligent Systems
• Limitation of Autonomous Systems
• Self- Properties of Intra-Action

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Introduction

• Term autonomous originates from the Greek terms
  autos meaning self and nomos meaning rule or law.
  
• Autonomy is considered to be a core property of
  UbiCom systems
• enabling systems to operate independently
• without external intervention.
• Autonomous systems operate at the opposite end of
  the spectrum to completely manual, interactive,
  HCI systems.
• Without autonomous systems, sheer number
  variety of tasks in an advanced technological
  society that require human interaction would
  overwhelm us and make system operation
  unmanageable.

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Automatic System

• An automatic system is specific type of
  autonomous system designed to
• Act without human intervention
• Execute specific preset processes
• Work in deterministic dynamic environments
• Incorporate simple models of environment
  behaviour
• Incorporate algorithms to control the environment
  (closed-loop control systems).

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Autonomous Systems

• More general types of systems than automatic
  systems are needed. Why?

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Autonomous Systems Design

• 4 major designs for general autonomous systems
• Dynamically reusable and extensible components
• EDA and context-aware system
• Hybrid goal-based (Section 8.3.4) and environment
  model based IS systems (section 8.3.3).
• Autonomic Systems

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Autonomous Systems Design Component-based

• Autonomous systems can be designed to consist of
  dynamically reusable and extensible components
• There are different types of component autonomy /
  cohesion
• Design autonomy
• Interface Autonomy
• Components use of SOA (Section 3.2.4).
• Component functions could be reprogrammable
  e.g., using mobile code (Section 4.2.2).
• These types of systems tend to have a strong
  notion of ICT environment autonomy
• but not of their physical human environment
  autonomy.

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Autonomous Systems Design EDA, Context-Aware

• Autonomous systems which operate in dynamic
  environments can be designed to ?
• These systems tend to focus on
• Physical world awareness and user awareness
• How the system decides how to adapt to such
  context changes how they affect current,
  active, user goals (Section 7.2).

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Autonomous Systems Design IS

• Autonomous systems can be designed to be hybrid
  goal-based (Section 8.3.4) and environment model
  based IS systems (section 8.3.3).
• Generally, focus of such systems is on
  supporting a notion of social autonomy or
  self-interested behaviour rather than on
  supporting ICT environment autonomy or physical
  environment autonomy.

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Autonomous Systems Design Autonomic

• Autonomous systems can be designed to be
  autonomic. How?
• Key challenges?

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Autonomous Intelligent Systems

• Autonomy is a key property of an IS
• An IS may have a physical embodiment or software
  embodiment which may be free to be executed in
  any part of a internetworked virtual computer.

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Autonomous Types Self Governance

• There are two main kinds of autonomy
• self-governance
• independence.

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Social / Organisational Autonomy

• Often an IS will delegate actions to another one,
  making it dependent on actions of another IS
• But ? risk of failure for the delegated actions
  because of misunderstandings, disagreements and
  conflicts, error, unknown private utilities and
  self-interests may operate.
• Designs for IS to cope with risk of delegation of
  actions?

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Autonomous versus Automatic

• Automatic refers to a system being self-steering
• To be self-steering requires a system to sense
  its environment act upon it based upon what it
  has sensed
• Autonomous are generally first automatic systems
  but which are extended so that they become
  self-governed.

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Limitation of Autonomous Systems

• Systems designed to be autonomous over parts of
  its life-cycle
• Challenges in supporting full autonomy?

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Self- Properties of Intra-Action

• Application processes are not influenced by
  external behaviour or control but are dependent
  on, or driven by, their internal behaviour
• Attempts to externally pertubate the system will
  be resisted and moderated in autonomous systems.
• Some system behaviour is decentralised and local,
  
• However, other system behaviour is
  community-wide, global
• Set of so called self- properties is a way to
  characterise autonomous systems
• These are also called the self-self-x or auto-
  properties
• Set of self- properties that need to be
  supported depends upon the application domain and
  system design.

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Self-Star Properties

• Need to model complex systems whose components
  have some autonomy and propensity to maintain and
  improve their own operation in the face of
  external environment perturbations, e.g.,
• Autonomic computing
• Organic Computing
• IS

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Self-Star Properties

• Self-star model focuses on doing things
  self-contained or doing things internally
• List of types of self-star system could be
  expanded
• to incorporate self-referencing, self-interaction
  (intranets) etc.

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Self-star Properties Examples

• Self-Configuring
• Self-Regulating
• Self-Optimising, Self-Tuning
• Self-Learning
• Self-Healing, Self-Recovery
• Self-Protecting
• Self-Aware
• Self-Inspection Self-Decision

• Self-Interested
• Self-Organising
• Self-Creating, Self-Assembly, Self-Replicating
• Self-Evolution, Emergence
• Self-Managing or self-governing
• Self-Describing Self-Explaining
• Self-representing

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Part B Outline

• Self-Awareness ?
• Self-Describing and Self-Explaining Systems
• Self-Modifying Systems based upon Reflective
  Computation

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Context-Awareness versus Self-Awareness

• Context-awareness is complementary to
  self-awareness.
• Context-awareness (Section 7) focuses on an
  awareness of a systems external environment
  (user, ICT, Physical ) context, periodically
  sensing this, automatically detecting significant
  change, and using this to adapt the internal
  systems behaviour to external environment
  behaviour.
• An associated design issue is what degree if any,
  an awareness the system needs of its own internal
  behaviour.
• Self-awareness focuses on monitoring its internal
  behaviour in order to optimise its behaviour

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Self-Awareness

• Basic design is that an ICT system does not
  process itself or is not aware of its own
  actions. Why not?
• Is basic designs for Intelligent Systems
  inherently self-aware?
• Useful that systems know its internal state how
  it acts?

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Self-Awareness Applications

• Optimising internal resource use
• Robots, e.g., robot arms
• Self-explaining systems

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External Descriptions Explanations

• Common reasons why systems are less usable?
• Intelligibility to user etc
• Disadvantages to using an external operators
  manual?
• How to support self-descriptions?
• Using internal (to the device) resources
• Hybrid device can be queried for the address,
  e.g., URL, of its description, but which resides
  elsewhere in its environment, e.g., the Cooltown
  and Semacode, Tagging (Chapter 6)

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Self-Describing Self-Explaining Systems

• A self-describing system is able to describe
  itself from the perspective of what
• A self-explaining system describes itself from
  the perspective of how and why.
• Self-descriptions self-explanations can be
  supported at multi-levels
• Systems can also provide mechanisms and
  interfaces to output their external for other
  meta-level processes or diagnostics
• e.g., the JTAG interface (Section 6.6.1).
• Devices know their state in relation to their
  plans and goals
• etc

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Self-Description Limitations Design Issues

• Limitations?
• Design issues?
• How rich are descriptions?
• How structured are they?
• How full versus short?
• Internal versus external and on-line versus
  off-line,
• Co-located versus external not co-located
  (Section 6.2).

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Reflection

• Reflection is the process by which a system can
  observe its own structure and behaviour, reason
  about these and possibly modify these
• Reflection has several benefits for UbiCom?
• Reflection is considered in more detail in
  Section 10.3.3.

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Reflective System Architecture
To support reflection, reflective computation is
done by a system at a meta-level about its own
operation at the application or base level of the
system
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Reflection

• There are three elements to the reflection
  process
• Instrumentation
• introspection
• adaptation

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Reflection Design

• Combined system reflection operation
  representation
• Separate system reflection operation
  representation
• Design issues?
• How to support enable this meta-level
  processing
• How the reflection is represented,
• what triggers the reflection
• what parts of the system can be reflected upon
• performance security aspects of using
  reflection.

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Reflection Design

• Reflection as an extension to middleware model?
• Reflective model is often applied as a design for
  context-aware systems. How?

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Part C Outline

• Basics ?
• Self- Management Control
• Autonomic Computing Design
• Autonomic Computing Applications
• Modelling and Management Self-Star Systems

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Autonomic Computing

• Motivation for autonomic systems was to deal with
  the obstacle of IT system complexity
• The growing complexity of the IT infrastructure
  threatens to undermine the very benefits
  information technology aims to provide (Horn,
  2001).
• Autonomic computing is one of the most well-known
  types of self- system
• Autonomic computing is also referred to as
  self-managing systems or self-governing systems.
• Autonomic computing was inspired through analogy
  with the human bodys nervous system.
• Autonomic systems can be constructed as group of
  locally interacting autonomous entities that
  cooperate to maintain system wide behaviour
  without any external control.

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Types of Self- Control Compared
Global
Global
Policies
Policies
Policies
Policies
Control Loop
Local
Local
Policies
Local
Policies
Local
Local
Local
Local
Local
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Key Autonomic Computing Properties

• Different definitions of key properties
• Keplar
• self-configuration
• self-optimisation
• self-healing
• self-protection.
• Ganek
• self-awareness
• context-aware adaptation
• planning to control behaviours constrained by
  system policies.

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Taxonomy for Basic Self-star Properties

• General Classification for basic self-star
  properties of systems can be based upon?

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Taxonomy for Basic Self-star Properties

• Hence, agent designs oriented to these
  environments can form the basis of designs of
  autonomic systems
• Specific properties relate to the type of
  organisation
• Spatially dependent,
• Role-based,
• Group-based,
• Resource access control
• Self protecting.

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Taxonomy for Basic Self-star Properties

• Methods for coordination of autonomous systems
  are dependent on the type of organisation
• Tuning servers individually (also called locally
  or on a microscopic scale) may be beneficial
• However in other applications, tuning servers
  individually, may not be optimal

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Autonomic Computing Design
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Autonomic Computing Architectural Models

• 5 basic components
• a user interface (task manager)
• an autonomic manager with an autonomic control
  loop
• a knowledge base about the managed resources
  including management policies
• a standardised interface to access managed
  resources (TouchPoint)
• service-based communications network, the ESB
  (Section 3.3.3.8).

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Designs to Support Self- System Components

• SNMP and the MIB can be used to implement
  TouchPoint part of knowledgebase (Chapter 12)
• Semantic and Syntactical metadata wrappers can be
  used to describe the structures of resources in
  richer ways.
• Sensors can poll resources or receive
  notifications about events in resources (Chapters
  3,6)
• Designs for autonomic control loop can be based
  on
• feedback control algorithms (Section 6.6)
• action selection loop design of intelligent
  systems (Section 8.3).

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Designs for Self- System
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Different Levels of Support for Self- Properties

• Self-star systems can be designed to support
  different maturity levels of self-star
  properties
• Basic
• Managed
• Predictive
• Adaptive
• Autonomic

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Autonomic Computing Applications

• Channels allocation to meet peak demand for calls
  in different mobile phone cells
• Load-balancing in servers to meet a designated
  QoS under variable external processor loads
• Intrusion detection
• Protecting Critical Infrastructures (SCADA)

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Modelling Managing Self- Systems

• How to model and mange systems which are dynamic
  decentralised and to an extent,
  non-deterministic?
• Unit testing and formal proofs?
• Statistical methods?
• Equation based computation methods ?
• Equation free computation methods ?
• Time series chaos theory analysis?

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Modelling Self- Systems

• Modelling behaviour interaction in simple
  life-forms forms an important contribution to
  improve models of complexity.
• Mathematical equations nay not capture many of
  natures most essential mechanisms.
• Modelling systems purely based upon physics
  interaction may lead to useful self-organising
  system models, models
• They must take into account higher level social
  science interaction models

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Part D Outline

• Basics of Complex Systems ?
• Self-Organisation and Interaction
• Self-Creation
• Self-Replication

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Complex Systems

• Complex system is defined to be a system whose
  properties are not fully explained by an
  understanding of its component parts
• In computation, a complex system often represents
  a hard-problem that cannot be solved within
  polynomial time.
• Complex ubiquitous systems?

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Complex Systems

• Complex systems can arise when?
• Complexity can also arise through, or can be
  designed using, simple interactions and repeated
  applications of simple rules

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Modelling Complex Systems

• It is not clear how accurate or equivalent the
  complexity models of one phenomenon,
• Models of relative complexity?
• Modus also discusses the growth in complexity

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Modelling Complex Systems

• Conventional technique to modelling complex
  systems is the divide-and-conquer or reductionist
  approach
• Some systems however, have macro properties which
  are almost impossible to predict from knowledge
  of the micro level properties of the individual
  parts of the system

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Modelling Complex Systems

• Key design challenges is whether or not complex
  system that are designed by
• specifying local interactions
• can be controlled, constrained
• can be coherent
• Can they enable the separate lower level
  components to act at a higher level in some
  unified way?

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Self-Organisation and Interaction

• Self-Organisation is a set of dynamical processes
  whereby stable or transient structures or order
  appears at a higher or global level of a system
  from the interactions between the lower-level or
  local entities
• Self-organised behaviour can be characterised by
  key properties such as
• Spatiotemporal structures
• Multiple equilibria
• Bifurcations

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Emergent versus Self-Organising Systems

• Emergent or process of macro behaviour emerging
  from micro or local behaviour is also called
  micro-macro effect.
• Self-organisation is an adaptable behaviour that
  autonomously acquires and maintains an increased
  order, statistical complexity, structure, etc.
• Emergence can have a micro-macro effect, but may
  not be self-organising, hence
• System can have emergence without
  self-organising.
• System can also be self-organising without
  emergence.
• Self-organising Emergent are often combined

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Self-Organising Systems Interaction Mechanisms

• Interaction mechanisms organising and
  coordinating autonomic system components?
• Digital Stigmergy
• Co-field coordination (or Gradient-based
  Coordination)
• Tag-based,
• Token-based

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Digital Stigmergy Design Principles

• Proximity principle
• Quality principle
• Diverse response
• Stability principle
• Adaptability principle
• UbiCom Applications
• Network routing
• Power distribution energy regulation system etc

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Co-field Coordination

• Inspired by physics forces in nature
• Also called Gradient-based Coordination and Wave
  Propagation coordination
• Autonomous entities can spread out a computation
  field or co-field, throughout the local
  environment
• UbiCom Applications
• to provide context information to other entities
  to follow the field.
• to signal that someone wants priority use of
  specific resources.
• to repel (separation) or attract individuals
  (cohesion and alignment).
• to support tourists in planning their activities

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Tag based Coordination

• Observable labels are attached to things.
• Can be used to make control decisions, to allow
  self-organisation of things according to tag
  types.
• UbiCom Applications
• Resource allocation in networks to enable them to
  adapt to continuous node failure and to the
  addition of new nodes and resources and changes
  in traffic conditions (Section 11.8.1).

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Token based Coordination

• Token based coordination resource access control
  token can be circulated amongst resource users
• Section 11. 8.1
• Token based vs. Tag based coordination?
• Applications
• to control access to shared things such as
  devices or people in social spaces, e.g., patient
  appointment system

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IS Models as Self-Organising Systems

• MAS can support relatively fixed organisational
  models based upon quite complex agents and
  relatively complex cooperative and competitive
  interaction
• But these often lack the flexibility to
  dynamically reorganise and to self-organise?
• Cellular computing
• Amorphous computers

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Self-Creation and Self-Replication

• Self-organising models discussed so far focus on
  how existing peers and resources are optimised
  through self-organisation, not clear how
• Self-replication is considered to be a hallmark
  of living systems.
• Evolution of self-producing systems require
  strategies that lead to cumulative selection of
  traits occurs over multiple cycles of
  reproduction

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Self-Replication Computer Viruses

• Computer virus is a well-known examples of
  artificial self-replicating mechanism.
• Computer virus is also referred to as a
  self-reproducing or self-replicating program
  (SRP).
• Tends to be software virus rather than a hardware
  virus. Why?
• Are nano devices more dangerous than Micro size
  MEMS as hardware viruses?

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Self-Replication Computer Viruses

• Virus is often introduced into host system
  hidden
• Virus contains a trigger that activates the
  self-replication mechanism
• Computer viruses versus Worms?
• Anti-virus systems?

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Part E Outline

• Basics of Artificial Life ?
• Finite State Automata Models
• Evolutionary Computing

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Artificial Life

• Are systems which mimic natural life and are
  characterised as follows.
• Have a finite lifetime from birth to death.
• Use selective reproduction.
• Their offspring inherit some of traits of the
  parents.
• They use survival of the fittest (evolution).
• They support the ability to expand in numbers to
  command a space or habitat.
• They can respond to stimuli in a habitat, acting
  to maintain it and adapting to it.

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Artificial Life

• Living organisms are consummate problem solvers
• Hence the motivation for use of artificial life
  models?
• UbiCom Applications?

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Finite State Automata (FSA) Models

• There are different types of FSA
• FSM can be represented in several ways,
  mathematically
• FSMs can be used to model devices with a finite
  set of states such as off, on and standby.

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Finite State Automata Models
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(Multi) Cellular Automata Models

• Are models of self-reproducing organisms
• Can be designed in the form of a grid, where each
  cell is represented as an FSM
• Cell exists in 1 of 2 states dead or alive
  which use basic transformation rules to transform
  a cell into dead or alive.
• E.g., rules of Conways Game of Life

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Conways game of life
This example illustrates the principle that
reactive type behaviours combined with a set of
simple transformation rules, when repeatedly
applied, can model more complex behaviours e.g.,
the gliding pattern
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Multi) Cellular Automata Models

• Other more complex rules of life can also be
  formulated.
• Other examples of simple rules governing
  collective behaviour?
• Rule model for flocks of animals (Section 10.4).
• Multiple individual FSMs can also be
  interconnected to form device networks.

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Evolutionary Computing

• Evolutionary computing are computer algorithms
  which involve cumulative selections from a
  population of entities to solve a problem.
• Behaviour of entities in the system is governed
  by implicit behaviours and goals,
• Cumulative mean that in each generation or step
  of evolution, existing entities reproduce to form
  a new generation of entities.

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Evolutionary Computing

• Evolutionary computing originated from research
  in cell automata, the ideas also changed
  somewhat.
• New generations are based upon the best traits of
  the previous generation rather than on rules
  which define how the existing generation
  transforms into the next one.
• There is a set of possible outcomes for
  reproduction determined by natural selection.

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Evolutionary Computing

• Main types of evolutionary computing techniques
  include
• Genetic Algorithms (GA) and Genetic Programming
  (GP),
• Evolutionary Strategies (ES) and Evolutionary
  Programming (EP).

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Digital Ecosystems

• Digital ecosystem is analogous biological
  ecosystem which consists of a community
• Set of organisms from different species
  interacting together
• Organisms interact with and lives in harmony with
  their habitat.
• Distributed Evolutionary Computing (DEC) as an
  extension of SOA
• Evolutionary computing is used locally within an
  ecosystem to find individual members of ecosystem
  to satisfy locally relevant constraints.

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Summary
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Revision

• For each chapter
• See book web-site for chapter summaries,
  references, resources etc.
• Identify new terms concepts
• Apply new terms and concepts define, use in old
  and new situations problems
• Debate problems, challenges and solutions
• See Chapter exercises on web-site

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Exercises Define New Concepts

• Annotation

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Exercise Applying New Concepts