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This paper gives a new algorithm for the efficient stochastic qualitative simulation, which can suppress the number of generated behaviors using the existing probabilities of states. In the new algorithm, the minimum existence probability, which is the lower bound of existence probability of states being preserved, is estimated with the Monte Carlo method. As a result, the states that are insignificant for envisioning useful behaviors are removed before generating them. In addition, the prefetch of measurement to check consistency of the states with the measurement in advance contributes to efficiency of the states generation. Through the simulation of the large scale building air conditioning systems, we clarify that the number of states can be reduced under computable order and the simulation terminates in real time. I IntroductionSince the system structure of building air conditioning systems is various and changeable, rule-based fault diagnosis is impractical because of frequent changes of rules. Modelbased approach[l, 21 is more suitable to diagnosis of such targets. In the model-based approach, a model for describing structures and functions of the target system is prepared in advance, and possible behaviors of the model of the system are derived. By comparing t h e behaviors derived based on the model with the observed behavior of IEEE Catalog Number: 95TH8081 the system, a part in failure is identified.Qualitative simulation is one of the effective methodologies to derive behaviors using model of the systerriI3, 4, 51. It has a n advantage of simulating the model without C O I I I~ plicated functions, because the elaborate physical mechanisms can be expressed with simple causal relations by using qualitative models.However, because of ambiguity caused by simplicity of the qualitative model, enormous number of behaviors a,re generated [6]. Especially, since the number of sensors in a. building air conditioning system is limited by constraint on costs, it is difficult t o prune a lot of improbable behaviors using real measured values.We have already proposed a stochastic qualita.tive simulation to cope with this problem[7, 81. In this method, all states, whose series specify the behaviors of'thc? system, have existence probabilities. Since states with relatively small existence probability are eliminated, explosive increase of behaviors can be prevented. Although this method has succeeded in reducing possible behaviors, however, the number of tenta.tive sta.tes generated before pruning becomes intractable in terms of memory c:;i.ixic:ity and execution time to apply the nietliod to largt? s(:aIc a i r conditioning systems.In this paper, we present a new algorithm for tlrt! stochastic q uali ta.t ive si r n 11 1a.t ion t 11 at ai iris ;t t niorc cfficient execution toward real-timc diagnosis of large: s(:i~Iv building air conditioning systcinis. This mcithotl introduces miriirnurri existencc proba.t>ility, which is tlcfin(id as

A model has been developed to estimate optimal mix of traffic types under environmental constraints in the future. The model constructed is a linear-programming model, where the share of each traffic means among zones is determined so as to minimize the generalized cost obtained as a sum of the trip cost and travel time converted into a money term under various constraints, such as the capacity of each transportation facility and the permissible amount of pollutant emission. By applying to the actual data of the OD survey in 1990 in Osaka, it has been confirmed that the model can explain fairly well the actual situations of modal split among different traffic means. The impacts of policies of imposing control on nitrogen oxide ( NOx ) emission as well as carbon dioxide ( C02 ) emission were investigated for Osaka, city in the year 2010. It has been shown from scenario analyses using the model that it would be impossible to satisfy the environmental constraints only by reducing unit NOx exhaust coefficient of private car and taxi, and that electric car and natural-gas firing bus should be introduced to the rate of 3.0 3.1 % and 0.97 1.2 % of the total traffic means, respectively.

Qualitative reasoning is one of the powerful methods for the fault diagnosis of the complicated systems. The stochastic qualitative reasoning method, one of qualitative reasoning methods, has proposed. However, it is hard to apply this method to large scale systems because of the enormous generation of behavior patterns.Efficient qualitative reasoning is proposed in this paper. In this method, the minimum existence probability of the generating behavior patterns is estimated by the Monte-Carlo method. The minimum existence probability is used to depress the number of generating behavior patterns. In addition, the improbable behavior patterns in the future are pruned off by future real measured values patterns. This method can be applied to the diagnosis for the large scale systems.