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14
Compositional Modeling: Finding the Right Model for the Job
, 1991
"... Faikenhainer, B. and K.D. Forbus, Compositional modeling: finding the right model for the job, Artificial Intelligence 51 ( 1991 ) 95143. ..."
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Cited by 214 (21 self)
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Faikenhainer, B. and K.D. Forbus, Compositional modeling: finding the right model for the job, Artificial Intelligence 51 ( 1991 ) 95143.
QPC: A Compiler from Physical Models into Qualitative Differential Equations
 In Proceedings of the Eighth National Conference on Artificial Intelligence
, 1990
"... Qualitative reasoning can, and should, be decomposed into a modelbuilding task, which creates a qualitative differential equation (QDE) as a model of a physical situation, and a qualitative simulation task, which starts with a QDE, and predicts the possible behaviors following from the model. In su ..."
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Cited by 59 (17 self)
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Qualitative reasoning can, and should, be decomposed into a modelbuilding task, which creates a qualitative differential equation (QDE) as a model of a physical situation, and a qualitative simulation task, which starts with a QDE, and predicts the possible behaviors following from the model. In support of this claim, we present QPC, a model builder that takes the general approach of Qualitative Process Theory [ Forbus, 1984 ] , describing a scenario in terms of views, processes, and influences. However, QPC builds QDEs for simulation by QSIM, which gives it access to a variety of mathematical advances in qualitative simulation incorporated in QSIM. We present QPC and its approach to Qualitative Process Theory, provide an example of building and simulating a model of a nontrivial mechanism, and compare the representation and implementation decisions underlying QPC with those of QPE [ Falkenhainer and Forbus, 1988; Forbus, 1990 ] . Introduction There have been a variety of producti...
Qualitative and Quantitative Simulation: Bridging the Gap
 Artificial Intelligence
, 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 semiquantitative simulation is to use numeric intervals to represe ..."
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Cited by 44 (1 self)
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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 semiquantitative simulation is to use numeric intervals to represent incomplete quantitative information. In this research we demonstrate semiquantitative simulation using intervals in an implemented semiquantitative simulator called Q3. Q3 progressively refines a qualitative simulation, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques. Q3's simulations are based on a technique we call step size refinement. While a pure qualitative simulation has a very coarse step size, representing the state of a system trajectory at relatively few qualitatively distinct states, Q3 interpolates newly expl...
Qualitative Models in Interactive Learning Environments: An Introduction
 INTRODUCTION TO A SPECIAL ISSUE OF INTERACTIVE LEARNING ENVIRONMENTS ON "THE USE OF QUALITATIVE REASONING TECHNIQUES IN INTERACTIVE LEARNING ENVIRONMENTS"
, 1998
"... ..."
AccessLimited Logic  A language for knowledgerepresentation
, 1990
"... AccessLimited Logic (ALL) is a language for knowledge representation which formalizes the access limitations inherent in a network structured knowledgebase. Where a deductive method such as resolution would retrieve all assertions that satisfy a given pattern, an accesslimited logic retrieves all ..."
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Cited by 16 (3 self)
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AccessLimited Logic (ALL) is a language for knowledge representation which formalizes the access limitations inherent in a network structured knowledgebase. Where a deductive method such as resolution would retrieve all assertions that satisfy a given pattern, an accesslimited logic retrieves all assertions reachable by following an available access path. The time complexity of inference is thus a polynomial function of the size of the accessible portion of the knowledgebase, rather than the size of the entire knowledgebase. AccessLimited Logic, though incomplete, still has a well defined semantics and a weakened form of completeness, Socratic Completeness, which guarantees that for any query which is a logical consequence of the knowledgebase, there exists a series of queries after which the original query will succeed. We have implemented ALL in Lisp and it has been used to build several nontrivial systems, including versions of Qualitative Process Theory and Pearl's probability networks. ALL is a step toward providing the properties clean semantics, efficient inference, expressive power which will be necessary to build large, effective knowledge
Automated Modeling of Physical Systems in the Presence of Incomplete Knowledge
, 1993
"... This dissertation presents an approach to automated reasoning about physical systems in the presence of incomplete knowledge which supports formal analysis, proof of guarantees, has been fully implemented, and applied to substantial domain modeling problems. Predicting and reasoning about the behavi ..."
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Cited by 11 (2 self)
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This dissertation presents an approach to automated reasoning about physical systems in the presence of incomplete knowledge which supports formal analysis, proof of guarantees, has been fully implemented, and applied to substantial domain modeling problems. Predicting and reasoning about the behavior of physical systems is a difficult and important task that is essential to everyday commonsense reasoning and to complex engineering tasks such as design, monitoring, control, or diagnosis. A capability for automated modeling and simulation requires ffl expressiveness to represent incomplete knowledge, ffl algorithms to draw useful inferences about nontrivial systems, and ffl precise semantics to support meaningful guarantees of correctness. In order to clarify the structure of the knowledge required for reasoning about the behavior of physical systems, we distinguish between the model building task which builds a model to describe the system, and the simulation task which uses the mo...
A diagnostic algorithm based on models at different level of abstraction
 Proc. 11th IJCAI
, 1989
"... The difficulties encountered in applying knowledgebased system technology to complex industrial environments have made the need for representing and using deep knowledge about physical systems increasingly clear to system designers. A rather large number of approaches to modeling and reasoning with ..."
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Cited by 8 (0 self)
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The difficulties encountered in applying knowledgebased system technology to complex industrial environments have made the need for representing and using deep knowledge about physical systems increasingly clear to system designers. A rather large number of approaches to modeling and reasoning with deep knowledge have been experimented, but the impact of these new techniques, often referred to as model based reasoning, on real applications is still poor. This paper presents a novel modelbased diagnostic method, whose distinctive features make it practical for diagnostic problem solving in automated systems for monitoring continuous processes. The method we introduce makes use of models at different levels of abstraction, qualitative and quantitative. In particular, we discuss an algorithm based on a quantitative, realvalued algebraic model, and a qualitative causal model that can be easily derived from the former in an automated way. The causal model is used for candidate generation, and the realvalued model for validation/rejection of candidates. 1
HigherOrder Derivative Constraints in Qualitative Simulation
 Artificial Intelligence
, 1991
"... 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 in ..."
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Cited by 7 (3 self)
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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 secondorder derivatives, and evaluation and application of the sign of second and thirdorder derivatives of variables, resulting in tractable simulation of important qualitative models. Caution is requir...
The Use of Partial Quantitative Information with Qualitative Reasoning
, 1991
"... v Table of Contents vii List of Tables xii List of Figures xiii 1. Introduction and Overview 1 1.1 Motivating Overview : : : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Specific Benefits of QualitativeQuantitative Simulation : : : : : 4 1.3 Previous work : : : : : : : : : : : : : : : : : : : ..."
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Cited by 5 (1 self)
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v Table of Contents vii List of Tables xii List of Figures xiii 1. Introduction and Overview 1 1.1 Motivating Overview : : : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Specific Benefits of QualitativeQuantitative Simulation : : : : : 4 1.3 Previous work : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 1.3.1 Qualitativequantitative work in VLSI : : : : : : : : : : 7 1.3.2 Qualitativequantitative spatial reasoning : : : : : : : : : 8 1.3.3 QualitativeQuantitative work in temporal ordering : : : 8 1.3.4 Interval and inequality reasoning : : : : : : : : : : : : : 9 1.3.5 Qualitativequantitative simulation work : : : : : : : : : 10 1.4 Outline of Remaining Chapters : : : : : : : : : : : : : : : : : : 14 2. Q2: Adding Interval Information to Qualitative Simulation 15 2.1 Overview : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 15 2.2 Propagation of Incomplete Quantitative Information : : : : : : : 16 2.2.1 Types of quantitative propagation constrain...
Compiling devices and processes
 Presented at 4th International Workshop on Qualitative Physics
, 1990
"... This paperpresents anewapproach for exploiting Truth Maintenance Systems(TMSs in which the inference engine can convey locality in its knowledge to the TMS. This approach is ideally suited for systems which reason about the physical world because much of knowledge is inherently local, i.e., the cons ..."
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Cited by 5 (0 self)
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This paperpresents anewapproach for exploiting Truth Maintenance Systems(TMSs in which the inference engine can convey locality in its knowledge to the TMS. This approach is ideally suited for systems which reason about the physical world because much of knowledge is inherently local, i.e., the constraints for a particular component or process usually only interact with constraints of physically adjacent components and processes. The new TMSs operate with a set of arbitrary propositional formulae and use general Boolean Constraint Propagation(BCP) to answer queries about whether a particular literal follows from the formulae. Our TMS exploits the observation that if propositional formulae are converted to their prime implicates, then BCP is both efficient and logically complete. This observation allows the problem solver to influence the degree of completeness oftheTMS by controllinghowmany prime implicates are constructed. This control is exerted by using the locality in the original task to guide which combinations of formulae should be reduced to their prime implicates. We show that conveying locality to the TMS is an important strategy for qualitative physics problem solvers. For example, at a minimum formulae corresponding to a single component (or commonly occurring combinations) model should be compiled into prime implicates in order to minimize runtime cost. When confluence models are used, the results of using ourTMS subsume those of the qualitative reasolution rule. This approach has been implemented and tested both within AssumptionBased Truth Maintenance Systems and LogicBased Truth Maintenance Systems. 1