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  • Discovering constraints for inductive process modeling [Elektronski vir]
    Todorovski, Ljupčo ; Bridewell, Will ; Langley, Pat
    Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combinations of these ... components. Previous research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted constraints. In this paper, we report an approach to discovering such constraints from a set of models that have been ranked according to their error on observations. Our approach adapts inductive techniques for supervised learning to identify process combinations that characterize accurate models. We evaluate the methodʼs ability to reconstruct known constraints and to generalize well to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstruct known modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.
    Source: Proceedings of the Twenty-sixth AAAI Conference on Artificial Intelligence and the Twenty-fourth Innovative Applications of Artificial Intelligence conference, 22-26 july 2012, Toronto, Ontario, Canada [Elektronski vir] (Str. 256-262)
    Type of material - conference contribution ; adult, serious
    Publish date - 2012
    Language - english
    COBISS.SI-ID - 4051118

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