Reducing Ambiguity by Learning Assembly Behaviour

In this paper we present a technique for automatically generating constraints on parameter derivatives that reduce ambiguity in the behaviour prediction. Starting with a behaviour prediction using an initial library containing general domain knowledge the technique employs feedback about correct and incorrect states of behaviour and knowledge about the causal dependencies between the parameters in the model in order to determine the constraints that remove the incorrect or undesired states of behaviour that result from ambiguity. In addition, the technique points out the assembly of physical objects to which the generated constraints apply.