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01-03-2009, 10:48 AM
Swarm Intelligence
Swarm Intelligence is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. The expression swarm intelligence was introduced by Beni & Wang in 1989, in the context of cellular robotic systems.
SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behavior. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling.
Application of swarm principles to large numbers of robots is called as swarm robotics
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Swarm Intelligence
Presented by:
Michael Jacob Mathew
S7,AEI
College Of Engineering, Trivandrum
2007-11 batch
[attachment=6976]
hi guys, If you like this presentation, please thank the author at: michaeljacob.mathew[at]gmail.com
Definition:
The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems
Swarm intelligence (SI) describes the collective behavior of decentralized, self-organized systems, natural or artificial, where collective behavior of simple agents causes coherent solutions or patterns to emerge. The concept is employed in work on artificial intelligence.
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Social Insects
Flexible
Robust
Decentralized
Self-Organized
Eg:Bees, Wasps, Termites, Ants
Ants
An In-depth Look at Real Ant Behaviour
Interrupt The Flow
The Path Thickens!
The New Shortest Path
Possible Solutions to Create Swarm Intelligence Systems
Create a catalog of the collective behaviors
Model how social insects collectively perform tasks
Use this model as a basis upon which artificial variations can be developed
Model parameters can be tuned within a biologically relevant range or by adding non-biological factors to the model
Four Ingredients of Self Organization
Positive Feedback
Negative Feedback
Amplification of Fluctuations - randomness
Reliance on multiple interactions
Properties of Self-Organization
Creation of structures
Nest, foraging trails, or social organization
Changes resulting from the existence of multiple paths of development
Non-coordinated & coordinated phases
Possible coexistence of multiple stable states
Two equal food sources
Types of Interactions For Social Insects
Direct Interactions
Food/liquid exchange, visual contact, chemical contact (pheromones)
Indirect Interactions (Stigmergy)
Individual behavior modifies the environment, which in turn modifies the behavior of other individuals
Stigmergy Example
Pillar construction in termites
Ants Agents
Stigmergy can be operational
Coordination by indirect interaction is more appealing than direct communication
Stigmergy reduces (or eliminates) communications between agents
From Ants to Algorithms
Swarm intelligence information allows us to address modeling via:
Problem solving
Algorithms
Real world applications
Modeling
Observe Phenomenon
Create a biologically motivated model
Explore model without constraints
Modeling...
Creates a simplified picture of reality
Observable relevant quantities become variables of the model
Other (hidden) variables build connections
A Good Model has...
Simplicity
Coherence
Refutability
Parameter values correspond to values of their natural counterparts
Robots
Collective task completion
No need for overly complex algorithms
Adaptable to changing environment
Communication Networks
Routing packets to destination in shortest time
Similar to Shortest Route (using DIP)
Statistics kept from prior routing (learning from experience)
Ant Net Algorithm:
Advantages:
Shortest Route
Adaptability
Flexibility
Scalability
Fault Tolerance
Modularity
Parralelism
Autonomy
Problems Regarding Swarm Intelligent Systems
Swarm Intelligent Systems are hard to ‘program’ since the problems are usually difficult to define
Solutions are emergent in the systems
Solutions result from behaviors and interactions among and between individual agents
Closing Arguments
Still very theoretical
No clear boundaries
Details about inner workings of insect swarms
The future……??
Pipe Inspection
Miniaturization
Telecommunications
Medical
Self-Assembling Robots
Engine Maintenance
Cleaning Ship Hulls
Satellite Maintenance
Pest Eradication
References
Multi-resolution Ant Colony - A New Approach to Use Swarm Intelligence in Continuous Problems
By: Rezaee, A. Jalali, M.J.P. Buinzahra Branch, Islamic Azad Univ., Buinzahra, Iran
This paper appears in: Information and Multimedia Technology, 2009. ICIMT '09. International Conference on Issue Date : 16-18 Dec. 2009 Date of Current Version : 15 January 2010
Different Types of Swarm Intelligence Algorithm for Routing
By: Sharvani, G.S. Cauvery, N.K. Rangaswamy, T.M. R.V. Coll. Of Eng., Bangalore, India
This paper appears in: Advances in Recent Technologies in Communication and Computing, 2009. ARTCom ‘09. International Conference on Issue Date : 27-28 Oct. 2009 Date of Current Version : 17 November 2009
Optimization of integrated circuits using an artificial intelligence algorithm
By:Vural, R.A. Yildirim, T. Commun. Eng. Dept., Yildiz Tech. Univ. Electron., Istanbul
This paper appears in: Research in Microelectronics and Electronics, 2008. PRIME 2008. Ph.D. Issue Date : June 22 2008-April 25 2008 Date of Current Version : 12 August 2008
Swarm Intelligence From Natural to Artificial Systems
By:Eric Bonabeau
Marco Dorigo
Guy Theraulaz
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