Qualitative simulation predicts the set of possible behaviors...

Traditionally, qualitative simulation uses a global, state--based representation to describe the behavior of the modeled system. For larger, more complex systems this representation proves extremely inefficient since it provides a complete temporal ordering of all potential distinctions leading to a large, complex behavioral description that obscures relevant distinctions, or even fails to terminate. The model decomposition and simulation algorithm (DecSIM) uses a divide and conquer approach to qualitative simulation. Variables within the system are partitioned into components. Each component is viewed as a separate system and is simulated using a state--based representation limited to the variables within the component. Interactions between components are reasoned about separately. DecSIM provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used. Introduction In The Scien...

In this paper, we propose a practical framework for characterizing, evaluating and selecting reformulation techniques for reasoning about physical systems, with the long-term goal of automating the selection and application of these techniques. We view reformulation as a mapping from one encoding of a problem to another. A problem-solving task is in turn accomplished by the application of a sequence of reformulations to an initial problem encoding to produce a final encoding that addresses the task. Our framework provides the terminology to specify the conditions under which a particular reformulation technique is applicable, the cost associated with performing the reformulation, and the effects of the reformulation with respect to the problem encoding. As such it provides the vocabulary to characterize the selection of a sequence of reformulation techniques as a planning problem. Our framework is sufficiently flexible to accommodate previously proposed properties and metrics for reformulation. We have used the framework to characterize a variety of reformulation techniques, three of which are presented in this paper.

ion: A scalable technique for avoiding irrelevant distinctions during qualitative simulation Daniel J. Clancy and Benjamin Kuipers Department of Computer Sciences University of Texas at Austin, Texas 78712 clancy@cs.utexas.edu and kuipers@cs.utexas.edu Abstract One of the major factors hindering the use of qualitative simulation techniques to reason about the behavior of complex dynamical systems is intractable branching due to a phenomenon called chatter. Chatter occurs when a variable's direction of change is constrained only by continuity within a region of the state space. This results in intractable, potentially infinite branching within the behavioral description due to irrelevant distinctions in the direction of change. Dynamic chatter abstraction provides a general purpose, scalable solution that abstracts chattering regions of the state space into a single state within the behavioral description. Chattering regions are identified via a dynamic analysis of the model and the ...

In this paper, we propose a practical framework for characterizing, evaluating and selecting reformulation techniques for reasoning about physical systems, with the long-term goal of automating the selection and application of these techniques. We view reformulation as a mapping from one encoding of a problem to another. A problem-solving task is in turn accomplished by the application of a sequence of reformulations to an initial problem encoding to produce a final encoding that addresses the task. Our framework provides the terminology to specify the conditions under which a particular reformulation technique is applicable, the cost associated with performing the reformulation, and the effects of the reformulation with respect to the problem encoding. As such it provides the vocabulary to characterize the selection of a sequence of reformulation techniques as a planning problem. Our framework is sufficiently flexible to accommodate previously proposed properties and metrics for refor...

One of the factors hindering the wide--spread application of qualitative simulation techniques is the difficulty encountered when developing a qualitative model. Analyzing the resulting behavioral description and revising the model in response to this analysis requires a significant amount of expertise and is often left up to the modeler. As a result, developing a qualitative model is difficult for users who are not familiar with the field. Furthermore, this process is often not addressed within the literature making it difficult for such a user to obtain the necessary expertise except through trial and error. This paper addresses the process of model revision and presents a set of tools and methods to assist in the performance of this task. It also demonstrates how qualitative simulation can be used to obtain a more detailed understanding of the dynamical properties of the modeled system. The tools presented help the modeler extract information from a complex behavioral description by...

We present a novel methodology to integrate qualitative knowledge from different case studies on Global Change related issues into a single framework. The method is based on the concept of qualitative differential equations (QDEs) which represents a mathematically well-defined approach to investigate classes of ordinary differential equations (ODEs) used in conventional modeling exercises. These classes are defined by common qualitative features, e.g. monotonicity, signs, etc. Using the QSIMAlgorithm it is possible to derive the set of possible solutions of all ODEs in the class. Using this one can formulate a common, qualitatively specified cause-effect scheme valid for all case studies. The scheme is validated by testing it against the actually observed histories in the study regions with respect to their reconstruction by the corresponding QDE. The method is outlined theoretically and exemplary applied to the problem of land-use changes due to smallholders agriculture in de...

Many of the techniques developed within the field of artificial intelligence focus on methods of searching an exponentially large solution space for a solution that satisfies a given set of constraints. Certain problems, however, require a complete description of the solution thus restricting the application of these techniques. To tractably solve problems of this nature, the solution space must be transformed into a more compact representation that highlights relevant information. An example of this type of problem is encountered when using qualitative simulation to reason about the behavior of an imprecisely defined dynamical system. In this paper, we present abstraction and problem decomposition techniques that have been used to solve many of the complexity problems encountered when performing a qualitative simulation. Abstraction is used to eliminate irrelevant distinctions within unconstrained regions of the solution space while problem decomposition is used to ...

Many of the techniques developed within the field of artificial intelligence focus on methods of searching an exponentially large solution space for a solution that satisfies a given set of constraints. Certain problems, however, require a complete description of the solution thus restricting the application of these techniques. To tractably solve problems of this nature, the solution space must be transformed into a more compact representation that highlights relevant information. An example of this type of problem is encountered when using qualitative simulation to reason about the behavior of an imprecisely defined dynamical system.