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1994
Abstract Qualitative reasoning uses incomplete kmiowlcdge to coni—pute a description of the possible behaviors for dynamic systems. For complex systems containing a large number of variables and constraints, t. he simulation frequently is inti-actable or results in a large, incomprehensible behavioral description. Abstraction and aggm-egation techniques are reqiied during the simulation to eliminate irrelevant details and highlight the imnportant characteristics of the behavior.
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.
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.
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
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.
Artificial Intelligence, 1993
Qualitative reasoning about physical systems has become one of the most productive areas in AI in recent years, due in part to the 1984 special issue of Artificial Intelligence on that topic. My contribution to that issue was a paper entitled "Commonsense reasoning about causality: deriving behavior from structure" [9]. From my perspective, that paper laid out a research program that has continued to be productive to this day, and promises to continue well into the future. After establishing a framework for qualitative reasoning, the primary technical contribution of the paper was a simple, clear representation for qualitative structure and behavior, abstracted from ordinary differential equations. My subsequent Artificial Intelligence paper, "Qualitative simulation" [ 10 ], made that abstraction relation precise, presented the vastly improved QSIM algorithm for qualitative simulation, and used the abstraction relation to prove the soundness and incompleteness of QSIM. I will discuss developments in qualitative simulation in my retrospective on that paper [12], and concentrate here on the larger issue of reasoning with qualitative models.
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.
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.
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...
Proceedings of the ... AAAI Conference on Artificial Intelligence, 2014
Qualitative reasoning can play an important role in early stage design. Currently, engineers explore the design space using simulation models built in languages such as Modelica. To make qualitative reasoning useful to them, designs specified in their languages must be translated into a qualitative modeling language for analysis. The contribution of this paper is a sound and effective mapping between Modelica and qualitative reasoning. To achieve a sound mapping, we extend envisioning, the process of generating all relevant qualitative behaviors, to support Modelica's declarative events. For an effective mapping, we identify three classes of additional constraints that should be inferred from the Modelica representation thereby exponentially reducing the number of unrealizable trajectories. We support this contribution with examples and a case study. 1 Qualitative reasoning and design Qualitative reasoning (QR) (Kuipers 1994)(de Kleer and Brown 1984)(Forbus 1984), which automates reasoning about the continuous world using abstraction, can play an important role in early stage design. Unfortunately, the languages of QR have not made inroads into engineering practice, and, consequently, QR has not been applied in industrial settings, with a few notable exceptions (e.g., (Struss and Price 2004)). Instead, engineers use simulation models built in languages such as Modelica (Fritzson 2004), to understand the possible behaviors of their design. To make QR useful to them, it is necessary to operate directly from the engineer's models. QR captures the relevant differences in behavior resulting from significant differences in parameter values. Thus, QR can play an important role in early stage design. We work to realize this promise as part of the DARPA Adaptive Vehicle Make 1 (AVM) program. AVM seeks to dramatically reduce the cost and time required to design, verify and manufacture complex cyber-physical systems. We integrated qualitative reasoning into the CyPhy toolchain (Simko et al. 2012) for use by designers (Lattmann et al. 2014). The Cy-Phy toolchain uses the Modelica language to model hybrid
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.
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.
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 1992
2005
This paper presents an approach to generate structured explanations of system behaviour based on qualitative simulations. This has been implemented in WiziGarp, a domain-independent interactive learning environment. The main issue addressed here is how to manage the complexity of a simulation in order to generate adequate explanations. These are presented to the user in the form of different kinds of diagrams, accompanied by explantory dialogue.
IEEE Transactions on Systems, Man, and Cybernetics, 1992
A qualitative model of an expert's mental model of a complex system (advanced nuclear power plant) was developed from the qualitative physics of confluences. This model was implemented as a qualitative simulation using an object-oriented extension to Common Lisp (Flavors). An existing method for dynamic constraint satisfaction was found to be inadequate for complex systems. Invisible connections for flow compatibility, control connections, iterative propagation, and embedded propagation were among the new features provided for derivation of causal ordering. Deterministic output was guaranteed through stochastic state transition. A parametric Monte Carlo simulation study was performed using a fictitious loop fragment, and changes were observed in flow rate change through a pump. State transition models provided excellent fits to the simulation data. Analysis of the state models showed that all conditions converged to steady state. Strictly forward (with the now) propagation facilitated consistency within intermediate pre-equilibrium states and convergence as compared to forward propagation with limited backward propagation. Uncertainty bias inhibited propagation of premature incorrect values. The psychological plausibility of qualitative simulation models was evaluated. A further extension of mythical causality is suggested, for which constraint propagation executes on multiple levels of aggregation.
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1998
2008
Creating qualitative models is often considered an art rather than a scientific undertaking, lacking a structured methodology that supports formalisation of ideas. This hampers the already difficult process of building qualitative models. This paper presents a methodology that structures and supports the capture of conceptual knowledge about system behaviour using a qualitative approach. The framework defines a protocol for representing content that supports the development of a conceptual understanding of systems and how they behave. The methodology supports modellers in two ways. It structures and explicates the work involved in building models. It also facilitates easier comparison and evaluation of intermediate and final results of modelling efforts.
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.
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.
1996
This paper demonstrates that qualitative reasoning plays a crucial role for both an efficient and physi- cally correct approach to the automated formulation of an accurate quantitative model which explains a set of observations. The model which "best" repro- duces the measured data is selected within a model space whose elements are constructed by exploiting specific knowledge and techniques of
2003
Qualitative simulation (QS) is an area of artificial intelligence that represent continuos and discrete aspects like space, time and quantity with little information, and makes inferences using symbolic data to represent physical quantities. Tradicionally, QS uses a global statebased representation to represent the behavior of the system. To qualitative simulate a system, some initial values are normally given, along with qualitative differential equations (QDEs). With this information, a qualitative simulation algorithm evaluates all possible combinations of qualitative values and filters out inconsistent states considering qualitatives constraints. This is a combinational process which normally requires exponential time. In this paper, a new algorithm is described which simulates individual components independently and joins their behavioral graphs together until a global behavioral graph is obtained. This algorithm achieves substantial reductions in time and it is polynomial with respect to the number of components. It is shown how with this algorithm is possible to simulate industrial plants of hundreds of variables within a few minutes.