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Professor Thomas Müller and Professor Hans Briegel have been carrying out research on a machine learning model for several years that differs significantly from alternative artificial intelligence (AI) learning models. The philosopher from Konstanz and the theoretical physicist from the University of Innsbruck have integrated methods of philosophical action theory and quantum optics. Their "Projective Simulation" learning model has already been successfully applied in basic research.
Together with the Innsbruck physicist Dr. Katja Ried, the researchers have now adapted this AI model for realistic application to biological systems. The current issue of the scientific journal PLoS One discusses how the learning model can be used to model and reproduce locusts' specific swarming behaviour.
Demand - Models
Demand for models that are "closer to biology"
To carry out their interdisciplinary collaborative research, the scientists used data on locust behaviour from the Centre for the Advanced Study of Collective Behaviour in Konstanz, which carries out research on collective behaviour. Biologists in particular are demanding that models explaining collective behaviour be designed to be "closer to biology."
Models - Physicists - Individuals - Force - Result
Most current models were devised by physicists who assume that interacting individuals are influenced by a physical force. As a result, they don't necessarily perceive individuals within swarms to be agents, but instead, as points such as interacting magnetization units on a grid. "The models work well in physics and have a good empirical basis there. However, they do not model the interaction between living individuals," says Thomas Müller.
Projective Simulation is a learning model originally developed by Hans Briegel and is based on agents who do not react to events in a pre-programmed fashion. Instead, they are capable of learning. These "learning agents" are coded as individuals with different behavioural dispositions who interact with their environment by perceiving and reacting to sensory input. For this...
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