1. Neural Networks and Machine Learning Applications CSC 563
Prof. Mohamed Batouche
Computer Science Department
CCIS – King Saud University
Riyadh, Saudi Arabia

2. Complex Systems
Emergent Behaviors and Patterns from Local Interactions

3. What is 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

4. 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

5. Complex vs. Simple Systems
Have many parts
• Parts are interdependent in behaviour
• Difficult to understand because:
– behaviour of whole understood from behaviour of parts
– behaviour of parts depends on behaviour of whole

6. Complex Systems perspectives
Complex Systems as a Science to Understand Nature
Complex Systems as a New Form of Engineering

7. Complex Systems A new form of Engineering
Engineering which faces complex problems using simple dynamic systems.
« The whole is too much greater than the sum of the parts »

8. Examples of Complex Systems
Brain
Government
Family
Wold Ecosystem
Local Ecosystem (desert, ocean, rainforest)
Weather
University
Ant colony …

9. Anything to be Learnt from Ant Colonies?
Fairly simple units generate complicated global behaviour.
An ant colony expresses a complex collective behavior providing intelligent solutions to problems such as:
carrying large items
forming bridges
finding the shortest routes from the nest to a food source, prioritizing food sources based on their distance and ease of access.
“If we knew how an ant colony works, we might understand more about how all such systems work, from brains to ecosystems.”
(Gordon, 1999)

10. Shortest path discovery

11. Adaptation to Environmental Changes

12. Interactions among Social Insects
• Direct Interactions
• Food or liquid exchange
• Visual or tactile contact
• Indirect Interactions: Stigmergy
• Pheromones
• Individual behaviour modifies the environment (e.g., by putting up signs = stigma), which in turn modifies the behaviour of other individuals.

13. Demo NetLogo
Massively Parallel MicroWolds

14. Universal properties shared by complex systems
Emergence: The appearance of macroscopic patterns, properties, or behaviors that are not simply the “sum” of the microscopic properties or behaviors of the components
Self-organization: In biological systems, the emergent order often has some adaptive purpose
– e.g., efficient operation of ant colony

15. Emergence: Attempt of a Definition
• From the book:
Steven Johnson, Emergence—The Connected Lives of
Ants, Brains, Cities, and Software
– Emergence is what happens when an interconnected system of relatively simple elements self-organizes to form more intelligent, more adaptive higher-level behaviour.
– It’s a bottom-up model; rather than being engineered by a general or a master planner, emergence begins at the ground level.
– Systems that at first glance seem vastly different […] all turn out to follow the rules of emergence.

16. Emergence in complex systems
• How do neurons respond to each other in a way that produces thoughts (minds)?
• How do cells respond to each other in a way that produces the distinct tissues of a growing embryo?
• How do species interact to produce predictable changes, over time, in ecological communities?
• ...

17. Emergence in complex systems
Boids of Craig Reynolds

18. Boids of Reynolds Bird Flocking “Boids” model was proposed by Reynolds
Boids = Bird-oids (bird like)
Only three simple rules

19. Boids of Reynolds
simple rules give rise to complex behavior

20. Collision Avoidance
Rule 1: Avoid Collision with neighboring birds

21. Velocity Matching: Alignment
Rule 2: Match the velocity of neighboring birds

22. Flock Centering: Cohesion
Rule 3: Stay near neighboring birds

23. Characteristics Simple rules for each individual No central control
Decentralized and hence robust
Emergent
Performs complex functions

24. Boids of Reynolds
Boids of Craig Reynolds

25. Emergence in complex systems
Boids of Craig Reynolds

26. Emergence in complex systems
Boids of Craig Reynolds

27. Demo NetLogo
Bird Flocking

28. Why Are Complex Systems Important for CS?
• Fundamental to theory & implementation of massively parallel, distributed computation systems
• How can millions of independent computational (or robotic) agents cooperate to process information & achieve goals, in a way that is:
– efficient
– self-optimizing
– adaptive
– robust in the face of damage or attack

29. Some of Natural Systems
adaptive path minimization by ants
fish schooling and bird flocking
evolution by natural selection
information processing in the brain
wasp and termite nest building
pattern formation in animal coats
game theory and the evolution of cooperation
computation at the edge of chaos

30. Some of Artificial Complex Systems
artificial neural networks
simulated annealing
cellular automata
ant colony optimization
artificial immune systems
particle swarm optimization
genetic algorithms
other evolutionary computation systems

31. Reverse Emergence • simple rules give complex behaviour
–but which simple rules give the desired complex behaviour?
Reverse emergence

32. Reverse Emergence Searching simple rules by hand (trials).
Taking inspiration from nature (bees, ants…)
Optimization and Learning (GA, RL …)
Reverse emergence

33. Reverse Emergence Molecular NanoTechnology
• assembled artefact is emergent property
– of actions of vast number of nanites
• design requires “reverse emergence”
– from desired emergent artefact
– to behaviour of naniteassemblers
• extreme example of “non-classical refinement”
• simple rules give complex behaviour
– but whichsimple rules give the
desiredcomplex behaviour?
Reverse emergence
Adapted from Susan Stepney Slides (April 2006) University of York – Journeys in non-classical computation

34. Artificial Complex Systems
Genetic Algorithms
Genetic programming
Particle Swarm optimization

35. Genetic Algorithms

36. Stochastic Search: Genetic Algorithms
Formally introduced in the US in the 70s by John Holland.
GAs emulate ideas from genetics and natural selection and can search potentially large spaces.
Before we can apply Genetic Algorithm to a problem, we need to answer:
- How is an individual represented?
- What is the fitness function?
- How are individuals selected?
- How do individuals reproduce?

37. Stochastic Search: Genetic Algorithms Representation of states (solutions)
Each state or individual is represented as a string over a finite alphabet. It is also called chromosome which Contains genes.
genes
Solution: 607
Encoding
Chromosome:
Binary String

38. Stochastic Search: Genetic Algorithms Fitness Function
Each state is rated by the evaluation function called fitness function. Fitness function should return higher values for better states:
Fitness(X) should be greater than Fitness(Y) !!
[Fitness(x) = 1/Cost(x)]
Cost
States X Y

39. Stochastic Search: Genetic Algorithms Selection
How are individuals selected ?
Roulette Wheel Selection 1 2 3 5 18 4 6 7 8
Rnd[0..18] = 7
Chromosome4
Rnd[0..18] = 12
Chromosome6

40. Stochastic Search: Genetic Algorithms Cross-Over and Mutation
How do individuals reproduce ?

41. Stochastic Search: Genetic Algorithms Crossover - Recombination
Crossover single point - random
Parent1
Parent2
Offspring1
Offspring2
With some high probability (crossover rate) apply crossover to the parents. (typical values are 0.8 to 0.95)

42. Stochastic Search: Genetic Algorithms Mutation
mutate
Offspring1
Offspring1
Offspring2
Offspring2
Original offspring
Mutated offspring
With some small probability (the mutation rate) flip each bit in the offspring (typical values between 0.1 and 0.001)

43. Genetic Algorithms
GA is an iterative process and can be described as follows:
Iterative process
Start with an initial population of “solutions” (think: chromosomes)
Evaluate fitness of solutions
Allow for evolution of new (and potentially better) solution populations
E.g., via “crossover,” “mutation”
Stop when “optimality” criteria are satisfied

44. Genetic Algorithms Algorithm:
1. Initialize population with p Individuals at random
2. For each Individual h compute its fitness
3. While max fitness < threshold do
Create a new generation Ps
4. Return the Individual with highest fitness

45. Genetic Algorithms Create a new generation Ps:
Select (1-r)p members of P and add them to Ps. The probability of selecting a member is as follows:
P(hi) = Fitness (hi) / Σj Fitness (hj)
Crossover: select rp/2 pairs of hypotheses from P according to P(hi).
For each pair (h1,h2) produce two offspring by applying the Crossover operator. Add all offspring to Ps.
Mutate: Choose mp members of Ps with uniform probability. Invert one bit in the representation randomly.
Update P with Ps
Evaluate: for each h compute its fitness.

46. Genetic Algorithms
Cost
States

47. Genetic Algorithms
Mutation
Cross-Over

48. Genetic Algorithms

49. Genetic Algorithms

50. Genetic Algorithms

51. Genetic Algorithms

52. Genetic Algorithms

53. Genetic Algorithms

54. Genetic Algorithms

55. Genetic Algorithms

56. Genetic Algorithms

57. Genetic Algorithms

58. Genetic Algorithms

59. Genetic Algorithms

60. Genetic Algorithms

61. Genetic Algorithms

62. Genetic Algorithms

63. Genetic Algorithms

64. Genetic Algorithms

65. Genetic Programming

66. Genetic Programming Genetic programming (GP) Programming of Computers
by Means of Simulated Evolution
How to Program a Computer
Without Explicitly Telling It What to Do?
Genetic Programming is Genetic Algorithms where solutions are programs …

67. Genetic programming
When the chromosome encodes an entire program or function itself this is called genetic programming (GP)
In order to make this work, encoding is often done in the form of a tree representation
Crossover entails swapping subtrees between parents

68. Genetic programming
It is possible to evolve whole programs like this but only small ones. Large programs with complex functions present big problems

69. Genetic programming Inter-twined Spirals: Classification Problem
Red Spiral
Blue Spiral

70. Genetic programming
Inter-twined Spirals: Classification Problem

71. Ant Colony Optimization

72. Shortest path discovery
Ants get to find the shortest path after few minutes …

73. Ant Colony Optimization
Each artificial ant is a probabilistic mechanism that constructs a solution to the problem, using:
Artificial pheromone deposition
Heuristic information: pheromone trails, already visited cities memory …

74. TSP Solved using ACO

75. Particle Swarm Optimization

76. 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.

77. 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.

78. 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).

79. 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)

80. Algorithm of PSO

84. 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 (Swarm Intelligence) are interesting as they are cheap, robust, and simple.
They have lots of different applications.
Swarm intelligence is an active field in Artificial Intelligence, many studies are going on.