Agent based systems coupled with machine learning algorithms

Current on-going research focusing on unsupervised learning algorithms in order to classify and cluster behavioral inputs and integrate them within the design process, as a form-finding phase through a non-linear morph of behavior parameters.

AGENT BASED SYSTEMS COUPLED WITH MACHINE LEARNING ALGORITHMS

Self-organizing maps (SOM) are used here, (SOM represents a type of unsupervised artificial neural network), to reduce the high dimensionality of the data whilst also retain the high-dimensional non-linear associations. As SOMs can create associations between inputs, the map can suggest an overview of possibilities within the given parameter space, without the need of manually tweaking the system’s parameters by the designer.

The selected 8 models are used as inputs for the SOM. The resulting map creates an interpolation between inputs and creates a location rule, placing similar models closer on the map while placing further apart dissimilar models.

AGENT BASED SYSTEM TRACE FOLLOW BEHAVIOR PARAMETERS

VIEW ANGLE

COHESION MAGNITUDE

COESHION VIEW RANGE

SEPARATION MAGNITUDE

SEPARATION VIEW RANGE

FIELD MIN INTENSITY

FIELD MAX INTENSITY

STEP MAGNITUDE

OVERALL SYSTEM LENGTH

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BEHAVIOR PARAMETERS INFLUENCE DISTRIBUTION

K-mean clustering is used here to analyze parameter distributions on the self-organizing map (SOM), and help cluster and classify insightful behavioral patterns, and quantify the influence each has on the outcome.

#java #python #tensorflow #som #unsupervisedlearning #multiagentalgorithms #behavior