Transcript Ant Colony Optimization in Collaborative Learning

Ant Colony Optimization in Collaborative
Learning Environment Using NetLogo
By,
A.SAFIA #1, T.MALA*2
#1 Teaching Fellow, Department of Information Technology,
MIT Campus, Anna University, Chennai, India.
*2 Assistant Professor (Sr.G), Department of Information Science
and Technology,
CEG Campus, Anna University, Chennai, India.
1 [email protected]
2 [email protected]
Abstract
A Collaborative Learning Environment emanates from multiple
individuals’ consent to share knowledge and experiences for the collective
goodwill of all. It proves effective as the scope for a deep sense of
understanding increases multi-fold in a collaborative environment. This
however, can be optimised by identifying the Point Of Interest (POI) of
each member and thus, grouping individuals with similar POI. Considering
multiple samples from across multiple regions would prove effective in
forming highly efficient groups. With the Swarm Intelligence of Ant
Colony Optimization, the various experiences and learning of each member
of a group can prove helpful for other members. And with the cumulative
analysis of everyone’s learning, the group as a whole can be benefitted.
This paper shows how Ant Colony Optimization can be effectively used in
a Collaborative Learning Environment for improving the knowledge level
of individual implemented and this is proved using NetLogo simulation.
Agenda
• Existing Work
• Proposed Work
• Implementation
• Performance Evaluation
• Conclusions and Future work
Collaborative Learning
Collaborative Learning is a method of instruction in which multiple
students are grouped together to achieve a common academic goal. The
students may come from different locations and backgrounds. The very
purpose of Collaborative Learning is that different students master different
aspects of learning or knowledge-content and thus, when grouped, each
individual may provide his/her knowledge and learning abilities for the best
use of the rest of the members. This way, each member gets to utilise all the
resources available in the group thereby, enriching his/her knowledge
multi-fold, which wouldn’t have been possible individually.
Features of Collaborative Learning
Environment
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Heterogeneous Grouping
Peers Interaction
Individual Accountability
Positive Interdependence
Cooperative Skills
Ant Colony Optimization
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Swarm Intelligence techniques.
Solving computational problems using probabilistic approach
Ants -search of food.
Found- bring food almost
other ants - source of food.
Way - all ants find -source of food.
NetLogo
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NetLogo is a multi-agent programming language
Simulating natural and social phenomenon.
Developing large and complex systems.
NetLogo is Logo dialect -support agents.
Existing Work
• Collaborative Learning in E-Learning - (ELMS) [10].
• M2Learn [7]
• Scalable Framework for Large-Scale Distributed Collaboration (CSCW) [8]
• Web-based Framework for On-line Collaborative Learning and browsing
[3] [5].
• Social Learning (Omega Network) - User-based nearest neighbour
algorithms [6]
Proposed Work
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Ant Colony Optimization algorithm - leaner’s interest and maturity level.
Point Of Interest (POI), synchronisation with his/her vested interests.
Resembles a colony of ants which is in search of food
Path - Efficient learning Attract s
Dynamic mechanism - solutions to learning.
Algorithm
procedure ACO_in_CLE
initialise intensity matrix and feasibility matrix
while (sufficient knowledge of subject not gained)
do
Let path be initialised to current path
while ( other paths remain to be considered )
do
apply Probabilistic decision rule to an available path
if (switching to available path is more desirable )
then
path is equal to available path
else
path remains unmodified
done
end
end
Implementation
• Micro-level & Macro-level patterns - Interaction [1][2]
• Ant Colony Optimization - Natural phenomenon – Many agents
Simulated using NetLogo.
• Level of maturity.
• Following is a code sample that depicts the members of a group as ants
(turtles of NetLogo),
to setup-food ;; patch procedure
;; setup one food source on the right
if ((distance (0.7 * max-pxcor) 0) < 5)
[ set food-source-number 1 ]
end
Performance Evaluation
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IL – Individual Learning
CL – Collaborative Learning
CL using ACO – Collaborative Learning using Ant Colony Optimization
CKG – Content Knowledge Gained
CA – Content Analysed
HOT – Higher Order Thinking involved
Thus, it is very clear that CL and CL using ACO score more than IL on all
the parameters.
• Higher Order Thinking.
• Correlation of knowledge gained and application of it is more prominent in
case of Collaborative Learning using Ant Colony Optimization.
CONCLUSIONS & FUTURE WORK
We have represented the group members as the ants in the Ant Colony
Optimization model, wherein each member’s pursuit of knowledge is
equated to the search of each ant for food; thus, throwing light on the fact
that as a collective unit, the work of each individual may be put to the best
use of all. To implement the same, we have chosen the Ant Colony
Optimization (or any other user-defined equivalent) model from the model
libraries of NetLogo. We have simulated the Collaborative Learning
environment through an Ant Colony and shown how members optimise
their learning. We know that the intensity of possibility to follow a given
path of acquiring knowledge may reduce if other members don’t follow it.
However, the rate of degradation may also vary depending on the means of
learning under consideration and the composition of the group. Thus, if the
rate of degradation is taken care of dynamically, the system would then be
adaptable to a wider range of real-world scenarios. This remains our area of
research for future.
REFERENCES
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Courtiat, “A Flexible Architecture for Collaborative Browsing”.
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