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Qualitative simulation is a useful method for predicting the possible qualitatively distinct behaviors of an incompletely known mechanism described by a system of qualitative differential equations (QDEs). Under some circumstances, sparse information about the derivatives of variables can lead to intractable branching (or "chatter") representing uninteresting or even spurious distinctions among qualitative behaviors. The problem of chatter stands in the way of real applications such as qualitative simulation of models in the design or diagnosis of engineered systems. One solution to this problem is to exploit information about higherorder derivatives of the variables. We demonstrate automatic methods for identification of chattering variables, algebraic derivation of expressions for second-order derivatives, and evaluation and application of the sign of second- and third-order derivatives of variables, resulting in tractable simulation of important qualitative models. Caution is requir...

This paper describes a comprehensive and systematic framework for building mixed continuous/discrete, i.e., hybrid physical system models. Hybrid models are a natural representation for embedded systems (physical systems with digital controllers) and for complex physical systems whose behavior is simplified by introducing discrete transitions to replace fast nonlinear dynamics. In this paper we focus on two classes of abstraction mechanisms, viz., time scale and parameter abstractions, discuss their impact on building hybrid models, and then derive the transition semantics required to ensure that the derived models are consistent with physical system principles. The transition semantics are incorporated into a formal model representation language, which is used to derive a computational architecture for hybrid systems based on hybrid automata. This architecture forms the basis for a variety of hybrid simulation, analysis, and verification algorithms. A complex example of a colliding rod system demonstrates the application of our modeling framework. The divergence of time and behavior analysis principles are applied to ensure that physical principles are not violated in the definition of the discrete transition model. The overall goal is to use this framework as a basis for developing systematic compositional modeling and analysis schemes for hybrid modeling of

structural description : : : : : : : : : : : : : : : : : : : : 31 4.7 FR templates : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31 4.8 Component class hierarchy : : : : : : : : : : : : : : : : : : : : : : 32 4.9 Organizing SFFs : : : : : : : : : : : : : : : : : : : : : : : : : : : : 33 4.10 Additional component features : : : : : : : : : : : : : : : : : : : : 33 4.11 The Task Definition : : : : : : : : : : : : : : : : : : : : : : : : : : 34 V The Algorithm : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 35 5.1 Simplify the structural description : : : : : : : : : : : : : : : : : : 35 5.2 Analyze the structural description : : : : : : : : : : : : : : : : : : 36 5.2.1 Special-method : : : : : : : : : : : : : : : : : : : : : : : : : 37 5.2.2 Fetch-match-sff-method : : : : : : : : : : : : : : : : : : : : 39 5.2.3 Consolidate-analyze-method : : : : : : : : : : : : : : : : : : 44 5.2.4 Partition-method : : : : : : : : : : : : : : : : : : : : : : : : 45 5.2.5 Co...

This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplified problem, and generalization of the results obtained. Our emphasis in this work is to demonstrate the effectiveness of this approach in addressing complexity and grain size issues that affect qualitative simulation. The thought experiment methodology is presented formally, the implementation of a problem solver called TEPS is briefly discussed, and the methodology is compared with related techniques such as approximation, aggregation, and exaggeration. 1

This paper discusses the application of the thought experiment methodology to qualitative reasoning. Problem solving using this technique involves simplification of the original problem, solution of the simplifie d problem, and generalization of the results obtained. Our emphasis in this work is to demonstrate th e effectiveness of this approach in addressing complexity and grain size issues that affect qualitative simulation. The thought experiment methodology is presented formally, the implementation of a problem solve r called TEPS is briefly discussed, and the methodology is compared with related techniques such as approximation, aggregation, and exaggeration. 1

ion and Causality: Variance of Causal Ordering under Abstraction Operations Yumi Iwasaki Knowledge Systems Laboratory Stanford University 701 Welch Rd., Palo Alto, CA 94304, USA Appeared in the Proceedings of the Pacific Rim International Conference on Artificial Intelligence, 1990. Abstract This paper examines the relationship between causal ordering and two temporal abstraction techniques, namely generation of mixed models from dynamic models and aggregation of nearly decomposable models. The paper defines two notions of causal equivalence between an original model and its abstraction or its elaboration. Then, it shows that when the constraint for a uniform temporal grain size for a model is enforced, both elaborating and abstracting a model can significantly alter the causal ordering. 2 Table of Contents 1 Introduction .....................................................................................................................1 2 Model Abstraction Techniques ......

. A dynamic environment, such as a production process, a communication network, highway traffic, etc., may contain a huge amount of information, changing with time, which is a valuable resource for understanding the general behavior of the environment, discovering the regularities and anomalies currently happening in the environment, controlling an evolution process, and intelligent modeling or managing the environment. Unfortunately, the data generated in a dynamic environment are often expressed in low level primitives and in huge volumes. Because of the dynamic, continuous and rapid changes of the information flow, it is difficult to catch the regularities and anomalies in a dynamic environment and react promptly for real-time applications. In this study, a knowledge discovery technique is integrated with data sampling and active database techniques to discover interesting behaviors of a dynamic environment and react intelligently to the environment changes. The discove...