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Yoichiro Nakakuki

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AAAI Conference 1992 Conference Paper

Adaptive Model-Based Diagnostic Mechanism Using a Hierarchical Model Scheme

Yoichiro Nakakuki

This paper describes an adaptive model-based diagnostic mechanism. Although model-based systems are more robust than heuristic-based expert systems, they generally require more computation time. Time consumption can be significantly reduced by using a hierarchical model scheme, which presents views of the device at several different levels of detail. We argue that in order to employ hierarchical models effectively, it is necessary to make economically rational choices concerning the trade-off between the cost of a diagnosis and its precision. The mechanism presented here makes these choices using a model diagnosabiliiy criterion which estimates how much information could be gained by using a candidate model. It takes into account several important parameters, including the level of diagnosis precision required by the user, the computational resources available, the cost of observations, and the phase of the diagnosis. Experimental results demonstrate the effectiveness of the proposed mechanism.

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AAAI Conference 1990 Conference Paper

Inductive Learning in Probabilistic Domain

Yoichiro Nakakuki

This paper describes an inductive learning method in probabilistic domain. It acquires an appropriate probabilistic model from a small amount of observation data. In order to derive an appropriate probabilistic model, a presumption tree with least description length is constructed. Description length of a presumption tree is defined as the sum of its code length and log-likelihood. Using a constructed presumption tree, the probabilistic distribution of future events can be presumed appropriately from observations of occurrences in the past. This capability enables the efficiency of certain kinds of performance systems, such as diagnostic system, that deal with probabilistic problems. The experimental results show that a model-based diagnostic system performs efficiently by making good use of the learning mechanism. In comparison with a simple probability estimation method, it is shown that the proposed approach requires fewer observations, to acquire an appropriate probabilistic model.

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