Artificial Intelligence is a constantly growing field of study. Today, there is an emerging interest to bind concepts natural systems to computing to develop self-organized machines

1. Bio-inspired Artificial
Intelligence for Collective
Systems
Name :Achini Adikari
Index No : 104002P
Supervisor : Dr. H. Thilak Chaminda
Faculty of Information Technology
University of Moratuwa

2. Introduction
• Nature is the most organized dynamic system
• Behaviors of these systems have optimal adaptations to any kind of
critical situation.
• Systems developed in AI needs to do the correct thing at the correct
time
• Collective systems in AI needs to have a balance between
components and adapt to complex scenarios
• These could be influenced by Natural Collective Systems
• Thus, Swarm AI concept was introduced.

3. In collective AI systems there is a need to,
• Take decisions which have beneficial effects for all the components
• Use local information among sub components and systems
• Adapt to catastrophic and complex situations
Background and Motivation
Collective systems in nature are,
• Self organized
• Naturally adaptable to complex situations
• Have non linear interactions between each other
• Chooses the best option over many

4. Overview – Swarm Intelligence
• Swarm Intelligence is the study of collective behaviors of systems of
nature, mainly insects and birds
• Swarm AI is based on two main concepts which are self-
organization and Stigmergy.
There are four main swarm models,
• Ant Colony Optimization
• Ant Clustering Model
• Particle Swarm Optimization model
• Bird Flocking Model
Basic Structure of a Swarm Technique

5. Ant Colony Optimization
• The ant agent keeps a record of visited nodes and the
time elapsed for arrival.
• It will return following the same path and updates
the digital pheromone value on the links that
it passes by.
• The pheromone level decides the speed of
the transmission.
Ant Clustering Model
• Agent (ant) action rule is that the agent moves randomly in the
grid.
• They only recognize objects which are immediately in front of
them.
• Picking up or dropping item is based on pickup probability and
drop probability

6. Particle Swarm Optimization
• Particles move through the solution space, and are
evaluated according to some fitness criterion after
each timestamp

7. Bird Flocking Model
• Basic models of flocking behavior are controlled by three simple
rules:
– Separation - avoid crowding neighbors (short range repulsion)
– Alignment - steer towards average heading of neighbors
– Cohesion - steer towards average position of neighbors (long range
attraction)

8. Researches related to Swarm AI
Swarm Intelligence for Networking Principles and applications of swarm
intelligence for adaptive routing in telecommunications networks
• Study about the concepts of Wireless and telecommunication networks
using swarm intelligent agents
• They have studied many applications of the Swarm Intelligence paradigm,
considering routing algorithms for wired and wireless networks, best-
effort and quality-of-service networks.
Multicast Routing for Mobile Ad-Hoc Networks using Swarm Intelligence
• The study is done regarding group communication applications which
demand a large degree of coordination and have highly dynamic group
membership changes
• Presented an alternate approach to solve the multicast
routing problem in mobile ad hoc networks

9. Swarm Intelligence for Data Mining
• Two broad categories of Swarm AI, Effective Search and Data
organizing were studied.
• The benchmarking experiments done in this research showed that
ant-based clustering performs better than other techniques:

10. Applications of Swarm AI
• Concept of Ant colony Optimization is used in Southwest Airlines . They
are implementing and studying more about this technique and has got
impressive feedbacks
• The US Military uses swarm techniques to control unmanned vehicles. The
need to find the optimal path and best alternatives this foundation is
being used.
• Particle Swarm Optimization is used in the theory of social interaction to
problem solving. Particles can be regarded as simple agents that fly
through the search space and communicate the best solution that they
have reached.
• NASA has developed systems to investigate planetary mapping and
controlling micro satellites with the use of swarm technologies

11. • Using the concept of Ant based Routing, routing packets, reinforcement of routing
forward, backward and both directions have been researched in
telecommunication networks
• Location of transmission infrastructure for wireless communication networks is
also addressed using these techniques.
• Birds flocking model is heavily used in film industry, animations and as well in
controlling unmanned air vehicles
• In film production, swarm techniques are used in rendering and to generate
Complex interactive virtual environments, Break the Ice, Lord of the Rings
• Data Mining, data sensoring in router networks are also inspired by the collective
behaviors of natural systems.
• Swarm techniques are used in cargo arrangement in airline companies, route
scheduling in delivery companies and in power grid optimization control.
• Research state that swarm techniques could be used to control
nanobots within the body to kill cancer tumors.

12. Discussion
Advantages:
• The natural simplicity of Swarm AI agents and their communication
makes it easier to understand and results in a fast design process of a
Swarm AI system
• Agents in Swarm AI systems are necessarily fast hence they are very
efficient
• memory requirements are limited since these systems have simple
reactive and utility based agents which do not store previous
information
• Systems are robust and have adaptive nature with good
performance.

13. Drawbacks
• Swarm AI systems are not applicable in instances where exact
results are required since they provide approximate solutions.
• Expensive system methodologies
• Increasing the number of processing units in an agent will have
complexity issues when communicating and coordinating with
other sub systems

14. Algorithm Special Features
Ant Colony Optimization  It allows dynamic rerouting through shortest path if one
node is broken whereas other algorithms consider the path
to be static
 Inherent parallelism
 Positive Feedback leads to rapid discovery of good
Solutions
Particle Swarm Optimization  This does not have any overlapping and mutation
calculations
 Based on theories and easy to calculate
 PSO does not have genetic operators such as crossover
and mutation
 Can be applied into both scientific research and engineering
use
 Cannot work out the problems of scattering and optimization
Bird Flocking Model  Collision avoidance mechanisms
 Centralization and coordination between components

15. Future work
• One of the core focus areas is Data Mining and data clustering
where those can be inspired by swarm techniques.
• Prospects of having complex routing and telecommunication
systems.
• Research is being done regarding astronomy for satellites which are
auto mated.
• Robotics robots can be modeled to imitate the behavior of natural
organisms.
• Binding Swarm AI techniques with other artificial intelligence
models and algorithms, combination of many models will
compensate loop holes of some algorithms and will make an
efficient practice

16. Reference
Binitha S, S Silva Sathya, "A survey of Bio Inspires Optimization Algorithms"
ISSN: 2231-2307, Volume-2, Issue-2, May 2012
Dr. Xiaohui Cui
Applied Software Engineering Research Group
Oak Ridge National Laboratory,
Swarm Intelligence, Bio-inspired Emergent Intelligence
Mano Jean-Pierre, Bourjot Christine, Lopardo Gabriel, Glize Pierre,
Bio Inspired Mechanisms for Artificial Self-organized systems
Falko Dressler and Ozgur B. Akan
Computer Networks and Communication Systems, Dept. of Computer Sciences, University of Erlangen,
Germany, Bio Inspired networking
Mrs. B.D. Shirodkar, Dr. S.S.Manvi, A.J.Umbarkar,
Multicast Routing for Mobile Ad-Hoc Networks using Swarm Intelligence
David Martens, Bart Baesens · Tom Fawcett
Swarm Intelligence for Data Mining

17. Thank You…