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1991
Abstract: One main problem in qualitative simulation is that it often produces too detailed qualitative descriptions of a system's possible behaviors, or produce a lot of behaviors that differ very slightly. This paper addresses the problem of summarizing the result of a qualitative simulation to make it more perspicuous. Two causes of behavior proliferation have been identified and two algorithms to aggregate behaviors that do not differ significantly are presented.
1992
Abstract Most qualitative simulation techniques perform simulation at a single level of detail highlighting a fixed set of distinctions. This can lead to intractable branching within the behavioral description. The complexity of the simulation can be reduced by eliminating uninteresting distinctions through various abstraction techniques. Behavior aggregation eliminates occurrence branching by providing a hybrid between a behavior tree representation and a history based description.
1998
Abstract Traditionally, qualitative simulation uses a global, state--based representation to describe the behavior of an imprecisely defined dynamical system. This representation, however, is inherently limited in its ability to scale to larger systems since it provides a complete temporal ordering of all unrelated events thus resulting in combinatoric branching in the behavioral description. The Dec-SIM qualitative simulation algorithm addresses this problem using a divide and conquer approach.
1997
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. This paper presents two general abstraction techniques that solve the problem of chatter. Eliminating the problem of chatter signi cantly extends the range of models that can be tractably simulated using qualitative simulation.
Artificial Intelligence, 1993
Qualitative reasoning about physical systems has become one of the most active and productive areas in AI in recent years. While there are many different kinds of qualitative reasoning, the central role is played by qualitative simulation: prediction of the possible behaviors consistent with incomplete knowledge of the structure of physical system. In the retrospective [8] on my 1984 paper, "Commonsense reasoning about causality: deriving behavior from structure", I describe the framework for qualitative reasoning that has motivated this work, and the applications that have come out of that framework. That paper [5 ] includes the conjecture that the structural and behavioral representations for qualitative simulation could be rigorously shown to be abstractions of ordinary differential equations and their solutions. My 1986 paper, "Qualitative simulation", established that conjecture and legitimized the term qualitative differential equation or QDE. It also presented the clear and efficient QSIM algorithm. In this retrospective, I describe aspects of the body of work on qualitative simulation that has developed from there.
1997
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.
2002
Traditional Simulation solves problems executing their model on computers using numeric information but Qualitative Simulation has slightly different characteristics although it is basically a reasoning technique, which makes reasoning about physical systems, implemented using simulation’s problem solution approach. From the solution approach point of view, they also have some similar characteristics. Since both of disciplines advance on their own roads, it makes a gap between them. This paper aims to clarify the differences and similarities between two disciplines and bridge the gap in conceptual level
1997
Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semi-quantitative simulation is to use numeric intervals to represent incomplete quantitative information. In this research we demonstrate semi-quantitative simulation using intervals in an implemented semi-quantitative simulator called Q3.
2010
This paper presents a divide-and-conquer approach that aims at making QSIM simulation tractable. We consider dynamical systems the structure of which can be represented by compartments. The system model is decomposed into sub-models tightly connected through shared variables on the basis of the analysis of the causality relations intrinsically captured by the compartmental model structure. The sub-models are separately simulated but their behaviors are constrained by the information on the shared variables generated from the simulation. The partition of the complete model into smaller ones prevents the construction of temporal correlations between variables in different sub-models, and thus the generation of a complete temporal ordering of all unrelated events that is one of the major causes of intractable branching in qualitative simulation. The strategy we propose is discussed through a case study in the eld of Plant Pathology, namely the germination process of Plasmopara viticola...
Advances in Intelligent and Soft Computing, 2009
In multi-agent based simulations, providing various and consistent behaviors for the agents is an important issue to produce realistic and valid results. However, it is difficult for the simulations users to manage simultaneously these two elements, especially when the exact influence of each behaviorial parameter remains unknown. We propose in this paper a generic model designed to deal with this issue: easily generate various and consistent behaviors for the agents. The behaviors are described using a normative approach, which allows increasing the variety by introducing violations. The generation engine controls the determinism of the creation process, and a mechanism based on unsupervised learning allows managing the behaviors consistency. The model has been applied to traffic simulation with the driving simulation software used at Renault, SCANeR c II, and experimental results are presented to demonstrate its validity.
IEEE Transactions on Systems, Man, and Cybernetics, 1993
An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing three significant advantages over existing techniques. Firstly, it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors
1996
Abstract Qualitative simulation is often a useful tool for studying the behavior of physical systems. However, in some cases it can overwhelm with detail. Behavior graphs with hundreds of states may obscure the basic patterns of behavior that a qualitative model was intended to explore. This paper describes a method for abstracting any behavior graph according to user-speci ed criteria that are simple and natural to provide.
Artificial Intelligence, 1986
Qualitative simulation is a key inference process in qualitative causal reasoning. However, the precise meaning of the different proposals and their relation with differential equations is often unclear. In this paper, we present a precise definition of qualitative structure and behavior descriptions as abstractions of differential equations and continuously differentiable functions. We present a new algorithm for qualitative simulation that generalizes the best features of existing algorithms, and allows direct comparisons among alternate approaches. Starting with a set of constraints abstracted from a differential equation, we prove that the qsim algorithm is guaranteed to produce a qualitative behavior corresponding to any solution to the original equation. We also show that any qualitative simulation algorithm will sometimes produce spurious qualitative behaviors: ones which do not correspond to any mechanism satisfying the given constraints. These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions.
Qualitative simulation is a well-known reasoning technique that involves the use of simulation technologies. Reasoning is made to determine qualitative values and change directions of system variables, and it is done for each time point and time interval following the time point. Qualitative variables possess continuous qualitative value sets that are discretized by landmark points. Qualitative simulation uses qualitative time representation and its quantitative value is of no interest. The main purpose of this study was to develop a technique to determine time steps for a quantitative simulation under guidance of qualitative information. The proposed technique determined time advances using qualitative and quantitative information together to obtain a robust time step as wide as possible for simulation time advances. For this purpose, sign algebraic properties and derivation roots of quantitative equations and qualitative variable values with their change directions were used to compute time advances. In the approach, qualitative simulation determined landmark points to be advanced, and quantitative simulation calculated the duration required. Using the proposed algorithm, the simulation is advanced instead of iterating simulation time for a predefined time step and checking whether or not there is any activity in the interval, directly to the time points that are qualitatively different.
1997
Abstract 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.
1996
In this paper we present an event-based approach to qualitative simulation. We suggest that the behaviour of a system with time is best measured in terms of the landmark events that occur i.e. events that result in interesting changes to the system being modelled. For us, a behaviour model corresponds not to a sequence of qualitative state descriptions but to a set of event sequences | the things that actually happen to the system rather than the way it happens to be at certain times. Although we have a simple implementation of our system, our primary purpose in developing it is to derive a high level, event-based, nonmonotonic language for specifying qualitative simulation systems. We not only illustrate how a qualitative simulation program can be directly speci ed (and implemented) in our language, we also sketch how qualitative simulation systems from the literature can be de ned and reconstructed in our calculus.
2000
Qualitative simulations can be seen as knowledge models that capture insights about system behaviour that should be acquired by learners. A problem that learners encounter when interacting with qualitative simulations is the overwhelming amount of knowledge detail represented in such models. As a result, the discovery space grows too large, which hampers the knowledge construction process of the learner. In
1985
Abstract: Qualitative simulation is a key inference process in qualitative causal reasoning, In this paper, we present the QSIM algorithm, a new algorithm for qualitative simulation that generalizes the best features of rxisting algorithms, and allows direct comparisons among alternate approaches.
1993
Abstract Most qualitative simulation techniques perform simulation at a single level of detail highlighting a xed set of distinctions. This can lead to intractable branching within the behavioral description. The complexity of the simulation can be reduced by eliminating uninteresting distinctions. Behavior abstraction provides a hierarchy of behavioral descriptions allowing the modeler to select the appropriate level of description highlighting the relevant distinctions. Two abstraction techniques are presented.
Artificial Intelligence, 1990
This paper examines qualitative simulation (QS) from the phase space perspective of dynamic systems theory. QS consists of two steps: transition analysis determines the sequence of qualitative states that a system traverses and global interpretation derives its long-term behavior . I recast transition analysis as a search problem in phase space and replace the assorted transition rules with two algebraic conditions. The first condition determines transitions between arbitrarily shaped regions in phase space, as opposed to QS which only handles n-dimensional rectangles . It also provides more accurate results by considering only the boundaries between regions . The second condition determines whether nearby trajectories approach a fixed point asymptotically. It obtains better results than QS by exploiting local stability properties . I recast global interpretation as a search for attractors in phase space and present a global interpretation algorithm for systems whose local behavior determines global behavior uniquely.
1992
Qualitative modeling and simulation make it feasible to predict the possible behaviors of a mechanism consistent with an incomplete state of knowledge. Qualitative modeling can be useful for monitoring complex mechanisms, hard to model analytically or numerically, or for diagnosis of faulty mechanisms, whose model is unknown.