- author = "Telmo {de Lucena Torres de Menezes}",
- title = "Evolutionary Computational Intelligence for Multi-Agent Simulations",
- school = "Universidade de Coimbra, Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Informatica",
- year = "2008",
- address = "Portugal",
- month = dec,
- keywords = "genetic algorithms, genetic programming, Computational Intelligence, Multi-Agent Simulations, Evolutionary Computation, Complex Systems, Artificial Life, Emergence of Group-Behaviour, Bloat Control",
-
URL = "
http://telmomenezes.com/resources/Telmo_Menezes_PhD_Thesis.pdf",
-
URL = "
https://old.cisuc.uc.pt/publication/show/2458",
-
URL = "http://hdl.handle.net/10316/9904",
- size = "294 pages",
-
abstract = "The growing interest in multi-agent simulations,
influenced by the advances in fields like the sciences
of complexity and artificial life is related to a
modern direction in computational intelligence
research. Instead of building isolated artificial
intelligence systems from the top-down, this new
approach attempts to design systems where a population
of agents and the environment interact and adaptation
processes take place.
We present a novel evolutionary platform to tackle the problem of evolving computational intelligence in multi-agent simulations. It consists of an artificial brain model, called the gridbrain, a simulation embedded evolutionary algorithm (SEEA) and a software tool, LabLOVE.
The gridbrain model defines agent brains as heterogeneous networks of computational building blocks. A multi-layer approach allows gridbrains to process variable-sized information from several sensory channels. Computational building blocks allow for the use of base functionalities close to the underlying architecture of the digital computer. Evolutionary operators were devised to permit the adaptive complexification of gridbrains.
The SEEA algorithm enables the embedding of evolutionary processes in a continuous multiagent simulation in a non-intrusive way. Co-evolution of multiple species is possible. Two bioinspired extensions to the base algorithm are proposed, with the goal of promoting the emergence of cooperative behaviours.
The LabLOVE tool provides an object model where simulation scenarios are defined by way of local interactions. The representation of simulation object features as symbols mitigates the need for pre-defined agent sensory and action interfaces. This increases the freedom of evolutionary processes to generate diversified behaviors.
Experimental results are presented, where our models are validated. The role of the several genetic operators and evolutionary parameters is analysed and discussed. Insights are gained, like the role of our recombination operator in bloat control or the importance of neutral search. In scenarios that require cooperation, we demonstrate the emergence of synchronisation behaviours that would be difficult to achieve under conventional approaches. Kin selection and group selection based approaches are compared. In a scenario where two species are in competition, we demonstrated the emergence of specialisation niches without the need for geographical isolation.",
-
notes = "Thesis submitted to the University of Coimbra in
partial fulfilment of the requirements for the degree
of Doctor of Philosophy in Informatics
Engineering.
Supervisor Doctor Ernesto Jorge Fernandes Costa Full Professor of the Department of Informatics Engineering of the Faculty of Sciences and Technology of the University of Coimbra)",
