We are attempting to develop a novel agent-based simulation modelling framework based on principles adopted from software engineering (object oriented analysis and design) to help studying the behaviour of elephants (or any other types of animals) in captivity.
http://www.cs.nott.ac.uk/~pos/projects.html#ElephantWelfareProject
Unlike current models that use an agent based approach for defining the agents and their interactions we want to use UML (use case diagrams, sequence diagrams, class diagrams, and state machine diagrams) for defining our agents and their interaction. But we also want to embed some theoretical knowledge about animal behaviour in our agent definitions. So in the end it will be a mixture between software agents and social simulation agents usually used in the field.
To give you an idea what I am talking about here is a link to a presentation I recently gave to some of my colleagues from the economics department. Although it does not feature animal behaviour the problem we are approaching is similar to that described above – trying out a novel approach to defining agents in a field where UML is relatively unknown.
http://www.cs.nott.ac.uk/~pos/gtmssig/slides/gtmssigSiebers2013-09-13v3.pdf
Do you have any tips for us? Any references we should look at? Any similar projects you know of?
Very interesting! Swarm particle optimization method is often used to solve the problem of finding the global optimum of a dimensional function. The basis of method are the swarm intelligence algorithms, which concern with the design of intelligent multi-agent systems based on the collective behavior of insects (ants, termites, bees, and wasps) or other animal societies (flocks of birds and schools of fish).
Below I am giving some references:
[1] H. Schmeck and J. Branke, Designing evolutionary algorithms for dynamic optimization problems. Theory and application of evoluacionary computation: recent trends, S. Tsutsui and A. Ghosh, Eds., 2002, pp. 239-262.
[2] J. Kennedy, R. Eberhart and Y. Shi, Swarm Intelligence, Los Altos, CA: Morgan Kaufmann, 2001.
[3] K. Parsopoulos and M. Vrahatis, "Recent approaches to global optimization problems through particle swarm optimization," Natural Computing, pp. 235-306, 2002.
[4] T. Blackwell, R. Poli and J. Kennedy, "Particle swarm optimization. An Overview," Swarm Intell., vol. 1, p. 33–57, 2007.
There exist many technical aspects of the algorithms, which recently I have been interested too in a different area of application. So if you have any questions we can discuss it.
I would like to add the following references:
Agent interaction, multiple perspectives, and swarming simulation
http://dl.acm.org/citation.cfm?doid=1838206.1838284
Representing Social Structures in UML
http://link.springer.com/chapter/10.1007%2F3-540-70657-7_1
O-MaSE: a customisable approach to designing and building complex, adaptive multi-agent systems
http://inderscience.metapress.com/index/212244T4U6P55314.pdf
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