Technical Seminar Presentation - 2005 Presented By:- Prasanna Kumar Misra(EI200117233) Under the guidance of Ms. Suchilipi Nepak Presented By Prasanna.

Slides:



Advertisements
Similar presentations
Sync and Swarm Behavior for Sensor Networks
Advertisements

Swarm-Based Traffic Simulation
Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios Emily Shaeffer and Shena Cao 4/28/2011Shaeffer and Cao- ESE 313.
Security Issues in Ant Routing Weilin Zhong. Outline Swarm Intelligence AntNet Routing Algorithm Security Issues in AntNet Possible Solutions.
Swarm Intelligence (sarat chand) (naresh Kumar) (veeranjaneyulu) (kalyan raghu)‏
Swarm algorithms COMP308. Swarming – The Definition aggregation of similar animals, generally cruising in the same direction Termites swarm to build colonies.
Ant Colony Optimization. Brief introduction to ACO Ant colony optimization = ACO. Ants are capable of remarkably efficient discovery of short paths during.
Biologically Inspired Computation Lecture 10: Ant Colony Optimisation.
The Antnet Routing Algorithm - A Modified Version Firat Tekiner, Z. Ghassemlooy Optical Communications Research Group, The University of Northumbria, Newcastle.
Chapter 4 DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE
Mobile Agents for Adaptive Routing Presented by Hong-Jiun Chen & Manu Prasanna Gianni Di Caro & Marco Dorigo.
Natural Computation and Applications Xin Yao Natural Computation Group School of Computer Science The University of Birmingham.
CMPT 401 Summer 2007 Dr. Alexandra Fedorova Lecture XVII: Distributed Systems Algorithms Inspired by Biology.
Investigation of antnet routing algorithm by employing multiple ant colonies for packet switched networks to overcome the stagnation problem Firat Tekiner.
Ants-based Routing Marc Heissenbüttel University of Berne
• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •
By Stefan Rummel 05/05/2008 Prof. Rudowsky CIS 9.5 Brooklyn College.
DSS: Decision Support Systems and AI: Artificial Intelligence
CMPT Dr. Alexandra Fedorova Lecture XVII: Distributed Systems Algorithms Inspired by Biology.
D Nagesh Kumar, IIScOptimization Methods: M1L4 1 Introduction and Basic Concepts Classical and Advanced Techniques for Optimization.
Biologically Inspired Computation Ant Colony Optimisation.
Algorithms for Self-Organization and Adaptive Service Placement in Dynamic Distributed Systems Artur Andrzejak, Sven Graupner,Vadim Kotov, Holger Trinks.
L/O/G/O Ant Colony Optimization M1 : Cecile Chu.
Distributed Systems 15. Multiagent systems and swarms Simon Razniewski Faculty of Computer Science Free University of Bozen-Bolzano A.Y. 2014/2015.
SWARM INTELLIGENCE IN DATA MINING Written by Crina Grosan, Ajith Abraham & Monica Chis Presented by Megan Rose Bryant.
Lecture Module 24. Swarm describes a behaviour of an aggregate of animals of similar size and body orientation. Swarm intelligence is based on the collective.
By:- Omkar Thakoor Prakhar Jain Utkarsh Diwaker
Swarm Computing Applications in Software Engineering By Chaitanya.
Week 3a Mechanisms for Adaptation. POLS-GEOG-SOC 495 Spring Lecture Overview Review –CAS –Principles of chaos How do systems “learn”? –“Credit.
Swarm Intelligence 虞台文.
SWARM INTELLIGENCE Sumesh Kannan Roll No 18. Introduction  Swarm intelligence (SI) is an artificial intelligence technique based around the study of.
-Abhilash Nayak Regd. No. : CS1(B) “The Power of Simplicity”
Kavita Singh CS-A What is Swarm Intelligence (SI)? “The emergent collective intelligence of groups of simple agents.”
Chapter 40 Springer Handbook of Robotics, ©2008 Presented by:Shawn Kristek.
FRE 2672 TFG Self-Organization - 01/07/2004 Engineering Self-Organization in MAS Complex adaptive systems using situated MAS Salima Hassas LIRIS-CNRS Lyon.
Ant Colony Optimization. Summer 2010: Dr. M. Ameer Ali Ant Colony Optimization.
Multi-swarm Problem Solving in Networks Tony White
Biologically Inspired Computation Ant Colony Optimisation.
Modeling and Simulation. Warm-up Activity (1 of 3) You will be given a set of nine pennies. Let’s assume that one of the pennies is a counterfeit that.
Neural and Evolutionary Computing - Lecture 11 1 Nature inspired metaheuristics  Metaheuristics  Swarm Intelligence  Ant Colony Optimization  Particle.
Neural Networks and Machine Learning Applications CSC 563 Prof. Mohamed Batouche Computer Science Department CCIS – King Saud University Riyadh, Saudi.
From Swarm Intelligence to Swarm Robotics
Emergent Behavior in Biological Swarms Stephen Motter.
Ant Colony Optimization Quadratic Assignment Problem Hernan AGUIRRE, Adel BEN HAJ YEDDER, Andre DIAS and Pascalis RAPTIS Problem Leader: Marco Dorigo Team.
Distributed Models for Decision Support Jose Cuena & Sascha Ossowski Pesented by: Gal Moshitch & Rica Gonen.
Artificial Ants Book report on Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds (Complex Adaptive Systems), Ch 3 - Mitchel.
AntNet: A nature inspired routing algorithm
5 Fundamentals of Ant Colony Search Algorithms Yong-Hua Song, Haiyan Lu, Kwang Y. Lee, and I. K. Yu.
Swarms MONT 104Q – Mathematical Journeys, November 2015.
The Ant System Optimization by a colony of cooperating agents.
Biologically Inspired Computation Ant Colony Optimisation.
Path Planning Based on Ant Colony Algorithm and Distributed Local Navigation for Multi-Robot Systems International Conference on Mechatronics and Automation.
Swarm Intelligence. An Overview Real world insect examples Theory of Swarm Intelligence From Insects to Realistic A.I. Algorithms Examples of AI applications.
Topic1:Swarm Intelligence 李长河,计算机学院
Theme Guidance - Network Traffic Proposed NMLRG IETF 95, April 2016 Sheng Jiang (Speaker, Co-chair) Page 1/6.
Ant Colony Optimisation. Emergent Problem Solving in Lasius Niger ants, For Lasius Niger ants, [Franks, 89] observed: –regulation of nest temperature.
AUTONOMIC COMPUTING B.Akhila Priya 06211A0504. Present-day IT environments are complex, heterogeneous in terms of software and hardware from multiple.
A Sensitive Metaheuristic for Solving a Large Optimization Problem
Marco Mamei Franco Zambonelli Letizia Leonardi ESAW '02
BIOLOGICALLY MOTIVATED SYSTEMS
DRILL Answer the following in your notebook: What is a swarm?
Lecture XVII: Distributed Systems Algorithms Inspired by Biology
Firat Tekiner (Phd Student) Z. Ghassemlooy
James Hobson Andrew Forth Josh Griffin
Data and Computer Communications
Overview of SWARM INTELLIGENCE and ANT COLONY OPTIMIZATION
traveling salesman problem
CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano
Introduction to Artificial Intelligence Instructor: Dr. Eduardo Urbina
Speaker: Ao Weng Chon Advisor: Kwang-Cheng Chen
Presentation transcript:

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) Under the guidance of Ms. Suchilipi Nepak Presented By Prasanna Kumar Misra Roll no-EI NATIONAL INSTITUTE OF SCIENCE & TECHNOLOGY Palur Hills,Berhampur ,Orissa,India SWARM INTELLIGENCE

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) WHAT IS SWARM INTELLIGENCE Swarm Intelligence is a property of systems of non-intelligent robots exhibiting collectively intelligent behavior. Characteristics of a swarm: – Distributed, no central control or data source; – No explicit model of the environment; – Perception of environment, I.e. sensing; – Ability to change environment

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) Swarm systems are examples of behavior-based systems exhibiting: – –Multiple lower level competences; – –Situated in environment; – –Limited time to act; – –Autonomous with no explicit control provided; – –Problem solving is emergent behavior; – –Strong emphasis on reaction and adaptation; SWARM SYSTEMS

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) MOTIVATION Robust nature of animal problem-solving – –Simple creatures exhibit complex behavior; – –Behavior modified by dynamic environment. Emergent behavior observed in: – –Bacteria – –Ants – –Bees

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) EMERGENT PROBLEM SOLVING – –Raiding specific areas for food; – –Building and protecting nest; – –Sorting brood and food items; – –Cooperating in carrying large items; – –Emigration of a colony; – –Finding shortest route from nest to food source; – –Preferentially exploiting the richest food source available.

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) PHEROMONE EVAPORATION

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) REROUTING OF NETWORK TRAFFIC Network structure and dynamics dependent on the function and evolution of biological agents Network traffic can be rerouted on the fly with software agents Transmission through an alternative (green arrow) node to avoid traffic Software agents can perform this rerouting automatically

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) ANT COLONY & PHEROMONE TRAILS Ants are behaviorally unsophisticated; collectively perform complex tasks. Ants have highly developed sophisticated sign-based stigmergy –communicate using pheromones; –trails are laid that can be followed by other ants. Species lay pheromone trails travelling from nest, to nest or possibly in both directions. pheromones evaporate. pheromones accumulate with multiple ants using path.

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) VIRTUAL FORAGING Swarm intelligence investigated foraging behavior of ants. Pheromone a chemical substance that attracts other ants. Good strategy for finding the shortest path between a nest and a food source Optimal routes can be obtained by using artificial ants Ant like agents can also cope with dynamic environments

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) ROBOTICS APPLICATION A machine capable of processing energy. Automation capable of processing information. Robot capable of processing both energy & information. Intelligent swarm is a group of machines capable of forming ordered material patterns unpredictably Robots are intentionally programmed very crudely, the similarity between their behavior and that of a swarm of ants is striking.

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) BANKING APPLICATION By studying such brood sorting, it was developed that a method for exploring a large database. The problem is that many of the customers have never borrowed money from any financial institution. If the bank had a way to visualize clusters of people with similar characteristics, loan officers might be able to predict more accurately whether a particular person would repay a loan. The artificial ants make their sorting decisions by considering all the different customer characteristics simultaneously. The software could mathematically weigh some of the attributes more heavily than others.

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) SELF ADAPTATION Shortest paths emerged quickly Pheromone and cost sensitivities should vary during search: – –Avoid premature convergence; – –Speed up search considerably. Explorers encode sensitivity values: – –Fitness of encoding is cost of route; – –New agents are created with and use genetically-manipulated values for route finding.

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) PROMISED ADVANTAGES Simple and quasi-identical units Decentralized control action Lack of synchronicity Simple units could be Mass produced, Interchanged,Disposable Redundancy could result in reliability and adaptation. Massive computation

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI ) CONCLUSION Swarm Intelligence Intelligent set of machines capable of predicting ordered outcome to provide reliability and adaptation describes the intelligence property. Manage and design systems that lead to more efficient social organization by understanding the working of networks. The agents have the capability of predicting the unpredictable outcomes.

Technical Seminar Presentation Presented By:- Prasanna Kumar Misra(EI )