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Prof. Dr. Mohamed Batouche
Department of Software EngineeringCCIS – King Saud University
Riyadh, Kingdom of Saudi Arabia
Outline
•Introduction•Natural Complex Systems•Complexity Science•Artificial Complex Systems•Applications•Conclusion
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Introduction
New Software Engineering Paradigm based on Complexity Science
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What is Wrong with Today’s Software Engineering Paradigm?
• The foundation of today’s software engineering paradigm is wrong (software is a nonlinear complex system)
• The process models are wrong (Waterfall, incremental …)
• The software development methodologies are outdated
• The software testing paradigm is outdated• The quality assurance paradigm is outdated
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Jay Xiong
What Is the Root Cause for Those Critical Issues Existing withToday’s Software Engineering?• The root cause for those critical issues
comes from the wrong foundation of the software engineering paradigm that software and the software engineering paradigm are complex nonlinear systems, and should be handled with complexity science to comply with the essential principles of complexity science, particularly the Nonlinearity principle and the Holism principle to make all tasks and activities being performed holistically and globally rather than partially and locally.
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Jay Xiong
The Challenge
• Creation of common principles, unified theories, and methods to design, operate, and protect complex engineered systems.
• Sources of complexity:• Too much information, too many components, too many
constraints, too many parameters for consideration to accomplish a particular task
• Not enough information about essential elements or components of a system or about their interfaces
• Not enough information about how elements or components will behave under known or unknown conditions that may lead to unintended consequences
NATURAL COMPLEX SYSTEMS
What are
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Natural Complex Systems
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A termite "cathedral" mound produced by a termite colony: a classic example of emergence in nature.
Bottom-up behavior: Simple agents following simple rules generate complex structures/behaviors.
Fish SchoolFish SchoolBirds FlockingBirds Flocking
Natural Complex Systems
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Nature Inspired Computing
• Draws on the principles of emergence, self-organization, and complex systems
• Aims to develop new techniques and algorithms by getting ideas from nature.
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Complexity ScienceNew Kind of Science
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Systems• System Definition
• A combination of interrelated elements, parts, methods, or procedures forming a complex unitary whole working together toward a common objective (Blanchard & Fabrycky, 2006).
• Unity, Functional relationship, Useful purpose.
• System Elements• Components - Input, Process, Output.• Attributes - Properties of the components.• Relationships - Link between the first two.
• System Function• Purposeful action performed by the system.• Alter material, energy, information.
What is A System?A A System System is a is a collection of entities, elements, or collection of entities, elements, or components, e.g., people or machines, that act and components, e.g., people or machines, that act and interact together toward the accomplishment of some interact together toward the accomplishment of some logical end. logical end. • A system performs one or more functions.• A system is composed of interacting components.• A system performs functions not performable by its components.• A system has a boundary, separating it from everything else.
-- These are systems --
Small-scale Hardware
Large-scale Hardware People & Organizations
©New England Complex Systems Institute, Cambridge, Massachusetts
Systems
Engineered Systems
System
InputsMaterialsInformationEnergyOrganizational Structure
OutputsProductInformationResourcesWaste
ControlsTechnicalPoliticalEconomicEnvironmental
MechanismsHuman ResourcesComputer ResourcesFacilities/UtilitiesMaintenance/Support
Environment
• A combination of interacting elements organized to achieve one or more stated purposes.
• Systems Engineering is an interdisciplinary approach and means to enable the realization of successful systems. - INCOSE
Traditional Systems Engineering
• SE design process is developed to produce efficient and reliable systems.• Respect pre-specified constraints.• Meet pre-specified standards of performance.• Work in pre-specified situations.
• Distinction between the design/production phase and the operational phase.• Does not allow any type of adaptation and self-organization
in the operational phase.• Adaptive controllers do follow this two-phase.
• Adaptation only in the superficial sense of parameter adjustment.
Complexity and Complex Systems
• “I think the next century will be the century of complexity.” • Stephen Hawking for the San Jose Mercury News on
January 23, 2000.
• “Complex systems science emerged from the interplay of physics, mathematics, biology, economy, engineering, and computer science, with the mission to overcome the simplifications and idealizations that lead to unrealistic models in these sciences” (Chu et al., 2003).
• “...(Complex systems) landscape is bubbling with activity, and now is the time to get involved. Engineering should be at the center of these developments...” (Ottino, J. M. 2004).
Complex Systems• System with a large number of components and
interconnections, interactions or interdependencies that are difficult to describe, understand, predict, manage, design, and/or change (C.L Magee, O.L de Weck, MIT)
• System whose behavior is not deducible, nor may it be inferred from the structure and behavior of its component parts (Bar-Yam, 2003).
• Characteristics:• Property of self-organization or emergence of structure from
the interaction between the constituent parts of the system.• Self-organization: spontaneous appearance of large-scale
organization through limited interactions among parts of the system.
• Intrinsically related to notions such as complex adaptive systems, emergence, and complexity.
Complicated vs. Complex• Complicated systems:
• Systems whose behavior can be completely understood through functional decomposition.
• Ex: Microprocessor• Carefully designed and tested: works as documented.• Efficiency dependent on all components.• Increase in performance needs engineering redesign.
• Complex systems:• Systems that exhibit emergent behavior.• Ex: U.S. economy
• No one designed U. S. economy; yet it works.• Efficiency is robust to perturbations and failures.• Grows (shrinks) on its own, without any explicit control.
Complex Systems Complicated vs. Complex
Complicated Systems Complex Systems
©New England Complex Systems Institute, Cambridge, Massachusetts
Complex Systems
Complex Systems
• Complex systems are systems that exhibit emergent behavior:
• Anthills• Human societies• Financial Markets• Climate• Nervous systems• Immune systems• Cities• Galaxies• Modern telecommunication infrastructures
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Complex Engineered Systems
• Man-made complex systems that exhibit emergent behavior while retaining important systems engineering characteristics such as robustness, reliability, and fault-tolerance.• Examples: air and ground transportation networks,
distributed manufacturing environments, distributed supply networks, financial markets, healthcare delivery.
• In the large-scale systems domain, complex systems are the rule not the exception.
• Complex systems can only be engineered by intervention, not by specification and development.
→The system is no longer designed, it is engineered to evolve, adapt, and reconfigure as needed.
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Characteristics of a complex system?
• A complex system displays some or all of the following characteristics:
• Agent-based• Basic building blocks are the characteristics and activities of individual
agents
• Heterogeneous• The agents differ in important characteristics
• Dynamic• Characteristics change over time, usually in a nonlinear way;
adaptation
• Feedback• Changes are often the result of feedback from the environment
• Organization• Agents are organized into groups or hierarchies
• Emergence• Macro-level behaviors that emerge from agent actions and interactions
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Attributes for complex system?
• Interdependent
• Independent
• Distributed
• Cooperative
• Competitive
• Adaptive
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Complex systems
The fundamental characteristic of a complex system is that it exhibits emergent properties: Local interaction rules between simple agents give rise to complex pattern and global behavior
The concept of Emergent Behavior• the notion of emergence appeared with
the general systems theory and is linked to complexity.
• “The global behavior is greater than sum of the behaviors of the individual parts”
• The global emergence characterizes the properties of a system that are new ones.
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Complex Systems perspectives
Complex Systems as a Science to Understand Nature
Complex Systems as a New Form of Engineering
Engineering Complex Systems – Key idea• The idea of nature inspired computing
arose from the observations and questions regarding natural emergent phenomena:
• How do ants build their colonies, without any central control?
• How do termites build their nests,
without an architect that draws a plan? • How do species evolve without any
direction or imposed objective function?
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Complex Systems Engineering
• How can we understand and make use of these emergent phenomena to develop new ways of generating computer programs?
• Can we build self-adapting, self-organizing, and evolving computer systems and programs?
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Complex Systems A new form of Engineering• Engineering which faces complex problems using
simple dynamic systems (vast parallelism, robustness, locality).
• « The whole is too much greater than the sum of the parts »
What is A System of Systems?A A System of Systems (SoS) System of Systems (SoS) is a “super-system” made is a “super-system” made up of elements – each of which is itself a complex, up of elements – each of which is itself a complex, independent system -- that interact to achieve a independent system -- that interact to achieve a common goal.common goal.
-- Are These SoS? --
Major League Baseball
• SoS elements (i.e., the systems) can and do operate independently.• An SoS evolves – functions are added/removed/changed with experience.• An SoS exhibits emergent behavior not attributable to any element (system).• An SoS is geographically distributed – elements exchange information only.
International Air TravelJoint Theater Ops
Hardware
From INCOSE
Areas of Application• Integrated Deepwater System – Optimize and
rejuvenate the force structure of the U. S. Coast Guard
• Future Combat Systems – Define an optimal Brigade Force structure
• P8A – Integrating a new aircraft into the fleet• SBINet- Determine optimal coverage and
coordination between sensor, communications, C2.• General Navy Applications – formation of a carrier
task force• Air Force Applications – formation of an air wing
From INCOSE
Source: Monica Farah-Stapleton, IEEE SOS conference, 2006
Army SOS Perception
Artificial Complex Systems
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Artificial Complex Systems
• Artificial Neural Networks• Cellular Automata• Swarm Intelligence• Evolutionary Computing• Quantum Computing• DNA/Molecular Computing• Artificial Life• Artificial Immune System
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Artificial Neural Networks
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Developing Intelligent Program Systems
Neural Nets
• Artificial Neural Networks: Artificial Neural Networks are Artificial Neural Networks are crude attempts to model the highly massive parallel and crude attempts to model the highly massive parallel and distributed processing we believe takes place in the brain.distributed processing we believe takes place in the brain.
• Two main areas of activity:• Biological: Try to model biological neural systems.
• Computational: develop powerful applications. 38
Developing Intelligent Program Systems
Neural Nets
Neural nets can be used to answer the following:
• Pattern recognition: Does that image contain a face?
• Classification problems: Is this cell defective?
• Prediction: Given these symptoms, the patient has disease X
• Forecasting: predicting behavior of stock market
• Handwriting: is character recognized?
• Optimization: Find the shortest path for the TSP. 39
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Cellular Automata
The CA space is a lattice of cells (usually 1D, 2D, 3D) with a particular geometry.
• Each cell contains a variable from a limited range of values (e.g., 0 and 1).
• All cells update synchronously.
• All cells use the same updating rule, depending only on local relations.
• Time advances in discrete steps.
CellState = empty/off/0State = filled/on/1
First Generation
neighboring values
Second Generation
Game of Life: 2D Cellular Automata using simple rules
Emergent pattern: Blinker
Conway’s Life: Rules
A living cell with 0-1 8-neighbors dies of isolation
A living cell with 4+ 8-neighbors dies from
overcrowding
A cell with 3 living neighbors becomes a living cell
All other cells are unaffected
Game of Life: emergent patterns
43gliders: patterns that moves constantly across the grid
Conway’s Rules: Game of Life
Survive with 2 – 3 living neighbors
Generate with 3 living neighbors
Gosper’s glider gun : emits glider stream
Emergent Patterns
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Emergent Patterns: A Clock
45See Demo: Game of Life
Artificial Life
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Artificial Life
• Artificial Life: An attempt to better understand “real” life by in-silico modeling of the entities we are aware of.
• Motivations:
• The Emergent properties in life motivate scientists to explore the possibility of artificially creating life and expecting the unexpected.
• An Emergent property is created when something becomes more than sum of its parts.
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Boids of Craig Reynolds
• Bird Flocking
• “Boids” model was proposed by Reynolds• Boids = Bird-oids
(bird like)
• Only three simple rules
Flocking Model
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The model consists in three simple forces (steering behaviors): (a) Separation: steer to avoid crowding local flockmates
(a) Alignment: steer towards the average heading of local flockmates
(b) Cohesion: steer to move toward the average position of local flockmates
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Boids of Reynolds• simple rules give rise to complex
behavior
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Collision Avoidance
• Rule 1: Avoid Collision with neighboring birds (separation)
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Velocity Matching: Alignment
• Rule 2: Match the velocity of neighboring birds (Alignment)
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Flock Centering: Cohesion
• Rule 3: Stay near neighboring birds (Cohesion)
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Characteristics
• Simple rules for each individual
• No central control• Decentralized and hence robust
• Emergence: a system made up of discrete birds yet the overall motion is coordinated.
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Boids of Reynolds
• Boids of Craig Reynolds
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Emergence in complex systems
• Boids of Craig Reynolds
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Emergence in complex systems
• Boids of Craig Reynolds
Swarm IntelligenceAnt colony optimizationParticle Swarm Optimization
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Ant Colony Optimization
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Shortest path discovery Ants get to find the shortest path after few minutes …Ants get to find the shortest path after few minutes …
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Ant Colony Optimization Each artificial ant is a probabilistic mechanism that Each artificial ant is a probabilistic mechanism that constructs a solution to the problem, using:constructs a solution to the problem, using:
• Artificial pheromone depositionArtificial pheromone deposition• Heuristic information: pheromone trails, already Heuristic information: pheromone trails, already visited cities memory …visited cities memory …
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TSP Solved using ACO
Particle Swarm Optimization
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Particle Swarm Optimization
• Particle Swarm Optimization (PSO) mimics the collective intelligent behavior of “ unintelligent ” creatures.
• It was developed in 1995 by James Kennedy and Russell Eberhart
• Individuals interact with one another while learning from their own experience, and gradually move towards the goal.
• It is easily implemented and has proven both very effective and quick when applied to a diverse set of optimization problems.
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• Bird flocking is one of the best example of PSO in nature.
• One motive of the development of PSO was to model human social behavior.
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Algorithm of PSO
• Each particle (or agent) evaluates the function to maximize at each point it visits in spaces.
• Each agent remembers the best value of the function found so far by it (pbest) and its co-ordinates.
• Secondly, each agent know the globally best position that one member of the flock had found, and its value (gbest).
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Algorithm of PSO• Using the co-ordinates of pbest and gbest,
each agent calculates its new velocity as:
vi = vi + c1 x rand() x (pbestxi – presentxi)
+ c2 x rand() x (gbestx – presentxi)
where 0 < rand() <1
presentxi = presentxi + (vi x Δt)
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Algorithm of PSO
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Applications
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New Sorting Algorithms based on Natural Computing
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Bead-sort• Bead-Sort is a method of ordering a set of
positive integers by mimicking the natural process of objects falling to the ground, as beads on an abacus slide down vertical rods. The number of beads on each horizontal row represents one of the numbers of the set to be sorted, and it is clear that the final state will represent the sorted set.
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[10 8 6 2 4 12] [2 4 6 8 10 12]
Bead-sort Extended
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The "extended" (anti-gravity) mode allows the inclusion of all integers, with "negative beads" rising while "positive beads" fall.
Image processing using Quantum Computing and
Reverse Emergence
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The proposed method
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The objective is to solve image processing tasks by emergence using cellular automata.
Noisy image filtered imageCellular automata
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The proposed approach
• The problem at hand is which simple rules provide the desired complex behavior?
Solution: Reverse emergence
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Reverse Emergence• To find simple rules that give rise to
the desired complex behavior:
• Searching simple rules by hand (trials).
• Taking inspiration from nature (bees, ants, termites, spiders …)
• Optimization and Learning (GA, PSO, QGA, QPSO …)
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Conclusions
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Conclusions• We can learn from nature and take advantage of the
problems that she has already solved.
• Many simple individuals interacting with each other can make a global behavior emerge.
• Techniques based on natural collective behavior are interesting as they are cheap, robust, and simple.
• They have lots of different applications.
• Simple individuals which interact locally give rise to very complex behavior.
References
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References• Jay Xiong, New Software Engineering Paradigm Based on
Complexity Science, Springer 2011.
• Claudios Gros : Complex and Adaptive Dynamical Systems. Second Edition, Springer, 2011 .
• Blanchard, B. S., Fabrycky, W. J., Systems Engineering and Analysis, Fourth Edition, Pearson Education, Inc., 2006.
• Braha D., Minai A. A., Bar-Yam, Y. (Editors), Complex Engineered Systems, Springer, 2006
• Gibson, J. E., Scherer, W. T., How to Do Systems Analysis, John Wiley & Sons, Inc., 2007.
• International Council on Systems Engineering (INCOSE) website (www.incose.org).
• New England Complex Systems Institute (NECSI) website (www.necsi.org).
• Rouse, W. B., Complex Engineered, Organizational and Natural Systems, Issues Underlying the Complexity of Systems and Fundamental Research Needed To Address These Issues, Systems Engineering, Vol. 10, No. 3, 2007.
References• Wilner, M., Bio-inspired and nanoscale integrated computing,
Wiley, 2009.
• Yoshida, Z., Nonlinear Science: the Challenge of Complex Systems, Springer 2010.
• Stephen Wolfram, A New Kind of Science, Art-Quality Printing, 2002 (available online: http://www.wolframscience.com)
• Gardner M., The Fantastic Combinations of John Conway’s New Solitaire Game “Life”, Scientific American 223 120–123 (1970).
• Nielsen, M. A. & Chuang, I. L. ,Quantum Computation and
Quantum Information, 3rd ed., Cambridge Press, UK, 2000.
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