1. BIOLOGICALLY MOTIVATED SYSTEMS

2. Biologically Motivated Systems
INTRODUCTION
Algorithms inspired from Nature.
Problems of traditional Approach
Programming solutions increase in complexity day by day.
Error being generated is very high
Complex programs tends to crash more often
Change in code is may lead to bugs
A higher intelligence is required
Self organizing, highly tolerable, learning algorithms preferable-pointing towards nature
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MECHANISMS
Emergent behavior
Self-organization
Swarm intelligence
Mound building in ants
Bee Hiving
Predator-Prey model
Symbiosis
Parasitism
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Biologically Motivated Systems

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EMERGENT BEHAVIOR
The interaction of a group of agents lead to emergent behaviors at the group level.
Examples are given by social insects - ants, termites, bees and wasps and swarming, flocking phenomena in groups of vertebrates.
Advantages:
Scalability
Control architecture is kept exactly the same from a few units to thousands of units.
Flexibility
Units can be dynamically added or removed, reallocated and redistributed in a self organized way.
Robustness
Unit redundancy and minimalist design.
Biologically Motivated Systems
Biologically Motivated Systems

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METHOD
Dynamic interaction of individuals with
each other and
the environment
Give rise to emergent patterns on a macro level.
Not programmed into individual agents
Not programmed into the environment
Emerging as the result of interaction
Biologically Motivated Systems
Biologically Motivated Systems

6. EXAMPLES OF EMERGENT BEHAVIOR
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EXAMPLES OF EMERGENT BEHAVIOR
Example:
Foraging algorithm of ants:
Find food and take it to the nest but leave a chemical trail.
If you're wandering about and you come across a trail then follow it.
If you're not carrying food and you haven't come across a trail then wander randomly until you find one.
These rules together give a particular kind of emergent behavior.
Models based on it solve the ‘traveling salesman problem' - finding the shortest route to service a number of randomly scattered clients.
The shortest chemical trail routes to food will be used by more and more ants, because those ants will get back to the nest and get out again sooner than the others.
Biologically Motivated Systems
Biologically Motivated Systems

7. Biologically Motivated Systems

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STRATEGIES
Simple strategies for coordinating action
Parallelism
Parallelism plus interaction through the shared environment
Control by location in an inhomogeneous environment
Differential attraction recruitment and competition
Direct and indirect communication
Less internal state helps
Differential retention
Foraging for work
Stigmetry
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STIGMETRY
Stigmetry is a concept occasionally used in biology to describe the influence on behavior of the persisting environmental effects of previous behavior
The coordination of tasks and the regulation of constructions does not depend directly on the workers, but on the constructions themselves. The worker does not direct his work, but is guided by it. It is to this special form of stimulation that we give the name Stigmetry.
Systems can produce a wide range of apparently highly organized and coordinated behaviors and behavioral outcomes, simply by exploiting the influence of the environment.
Biologically Motivated Systems
Biologically Motivated Systems

10. EXAMPLES OF EMERGENT BEHAVIOR
Exhibited by
Social insects
Ants, termites, bees and wasps
Groups of vertebrates
Swarming,
Flocking
Herding
Shoaling phenomena
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FLOCKING
Birds, fish and some insects
The three rules that implement flocking are:
Cohesion:
Alignment
Separation
Advantages
Manages group movement very well
Rapid directed movement of the whole flock
Reactivity to predators (flash expansion, fountain effect)
Reactivity to obstacles
No collisions between flock members
The form of the flock may bring benefits in energy savings.
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SELF-ORGANIZATION
A self-organizing system is a system that tends to improve its performance in the course of time by making its elements better organized for achieving the goal.
This formulations includes the special case in which the goal is to achieve a high degree of organization of relevant entities from low degree of their organization.
Spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions
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Biologically Motivated Systems

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SWARM INTELLIGENCE
Swarm Intelligence
Swarm Intelligence (SI) is a computational and behavioral metaphor for solving distributed problems that takes its inspiration from biological examples mainly provided by social insects.
The abilities of such natural systems appear to transcend the abilities of the constituent individual agents
Robust and capable high-level group behavior is mediated by a small set of simple low-level interactions between individuals and between individuals and the environment
Biologically Motivated Systems
Biologically Motivated Systems

14. ADVANTAGES AND PROPERTIES
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ADVANTAGES AND PROPERTIES
Advantages Swarm Intelligence
Redundancy
Simplification
Time Efficiency
Resource Efficiency
Properties
Communication
Cooperation
Biologically Motivated Systems
Biologically Motivated Systems

15. EXAMPLES OF SWARM INTELLIGENCE
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EXAMPLES OF SWARM INTELLIGENCE
Army ant behavior
500,000 to 20,000,000 per colony
Feed on small arthropods
15 nomadic days: raid 200m in straight line, 20m raid front, 30,000 prey items brought back, bivouac with 50,000 larvae moved every night
20 static days: 14 raids, each at 123 degrees to previous raid
Fission: colony stages two raids in opposite directions,
Termite construction
Some of largest and longest lasting nonhuman constructions
Not just mounds of earth: cemented together, highly structured and differentiated, many different functional aspects
Biologically Motivated Systems
Biologically Motivated Systems

16. MODULAR RECONFIGURABLE ROBOTICS
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MODULAR RECONFIGURABLE ROBOTICS
Modular reconfigurable robotics
building robots for various complex tasks.
Robots built out of a number of identical simple modules. Each module contains a processing unit, a motor, sensors and the ability to attach to other modules.
One module can’t do much by itself, but when many modules are connected together, the result may be a system capable of complex behaviors.
Abilities
reconfigure itself—change its shape
meet the demands of different tasks or different working environments.
Biologically Motivated Systems
Biologically Motivated Systems

17. ADVANTAGES OF MODULAR RECONFIGURABLE ROBOTICS
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ADVANTAGES OF MODULAR RECONFIGURABLE ROBOTICS
Advantages
The modules can connect in many ways making it possible for a single robotic system to solve a range of tasks.
The robot may even decompose itself in several smaller ones.
Adapt to the environment and change shape as needed.
Robust to module failures. Defect modules can be ejected from the system
Modules can be mass-produced and therefore the cost may be kept low .
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Biologically Motivated Systems

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PREDATOR-PREY MODEL
Predator is focused on only one goal - to capture the prey.
On the capture of each prey unit, a reward will be awarded.
All the actions of the predator are focused on moving towards the ultimate goal
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Biologically Motivated Systems

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APPLICATIONS
Surveillance and transport-smart dust
Space exploration.
Foraging algorithm -packet transmission in network
Obstacle avoidance-efficient routing table construction
Termite mound building- for efficient task allocation
Incorporating complexity modeling
Automatic complex task handling -share negotiation,
Applications in maths and astronomy
Medical Applications
Nanobots.
Microimaging
Vacuuming and cleaning
Assembly
Biologically Motivated Systems
Biologically Motivated Systems

20. Conclusion and Future Scope
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Conclusion and Future Scope
Biologically Motivated Systems
Biologically Motivated Systems

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REFERENCES
Books
Prey - Science fiction by Micheal Crichton
Artificial Intelligence- Elaine Rich and Kevin Knight
Compact Discs
Britannica CD 2.0, Encyclopedia Britannica, 1995
Websites
- The Association of Artificial Intelligence
- The Caltech university homepage
- The Washington university homepage
Biologically Motivated Systems
Biologically Motivated Systems