K. Forbus and B. Falkenhainer. Self-Explanatory Simulations: An integration of qualitative and quantitative knowledge. In Proceedings of AAAI-90, pages 380--387, 1990.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....; population(transport 1) 1 MO , MO : Monotonic function I , I : Influenced Figure 4. A generated causal network. 4 Explanation Generation Once the causal network is obtained, we can generate explanations of the system s behavior by propagating qualitative values in the net work [Forbus and Falkenhainer, 1990] [Forbus and Falkenhainer, 1992] By tracing a loop in the causal network, we obtain an explanation: transition from search to attracted transition from attracted to trace transition from trace to transport In the generated explanatiom the reasoner finds a positive feedback among the ....

K. D. Forbus and B. Falkenhainer. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In Proc. of AAAI90, pages 380 387, 1990.

....of the influence would represent that fact. To simulate a turgid (not wilting) plant whose turgor pressure is dropping, the simulator would omit this influence until turgor pressure drops below the threshold. A variety of simulators are capable of simulating scenario models in this way [14, 15, 17]. Using this approach, the modeler need only build one scenario model to answer a question, rather than building a different model for different states of the scenario. 3.1 Adequacy Intuitively, a scenario model is adequate for answering a given prediction question if it satisfies two criteria. ....

....qualitative models, it has not been used to generate numerical models. There are two possible ways to generate numerical equations from influences. First, the domain knowledge can provide a numerical equation for each useful combination of influences on a variable. Forbus and Falkenhainer [17] have successfully used that approach. Second, each influence can specify how it combines with other influences, such as whether it is an additive term, a multiplicative term, or otherwise. After the model is constructed, equations can be generated using these specifications. Farquhar [13] has ....

Kenneth D. Forbus and Brian Falkenhainer. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pages 380--387, Menlo Park, CA, 1990. AAAI Press.

....to understand and change. There has been much recent work on automating their construction (e.g. Yang, 1992, Rosenberg and Karnopp, 1983, Abelson and Sussman, 1987, Palmer and Cremer, 1992 ] To this, the Qualitative Physics community has contributed the idea of a self explanatory simulator [ Forbus and Falkenhainer, 1990, Forbus and Falkenhainer, 1992 ] When using such a system, a person need only specify the basic entities, quantities, and equations governing the system to be simulated. From these, the program automatically prepares and runs a numeric simulation. It also keeps a record of its reasoning so it ....

....the basic entities, quantities, and equations governing the system to be simulated. From these, the program automatically prepares and runs a numeric simulation. It also keeps a record of its reasoning so it can explain the simulated behavior. Such a simulator has three primary advantages [ Forbus and Falkenhainer, 1990 ] 3 Many thanks to the members of the E 3 project, especially Mike Salisbury and Dorothy Neville. Pandu Nayak kindly provided Common Lisp causal ordering code. Elisha Sacks and Eric Boesch kindly provided the RungeKutta numeric integration code. This research was funded in part by National ....

[Article contains additional citation context not shown here]

K. Forbus and B. Falkenhainer. Self-Explanatory Simulations: An integration of qualitative and quantitative knowledge. In Proceedings of AAAI-90, pages 380--387, 1990.

....a motor constant. In this case, the relevance of motors to torque is inferred via the equation. The resulting explanation is that the motor replacement changed the motor constant, which resulted in a new torque via the equation. This way, simulators that generate quality explanations for humans [7, 19] can be part of the knowledge medium. For example, explainable simulation can be used to capture specifications of intended function and context of use that are hard to represent formally [14, 15] In general, if formal reasoning is used to infer relevance, then the resulting proof can serve as an ....

Kenneth D. Forbus and Brian Falkenhainer. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In AAAI-90, pages 380--387, Boston, 1990.

....[39] Oren [40] has developed the definition of multimodel 4 to formalize models containing several submodels, only one of which is put into effect at any time. Other groups in the AI community have also addressed the use of multiple models to support multi level reasoning architectures [24] [41], 42] Cellier [43] developed an approach to combined continuous discrete event models implemented in a GASP language extension. Praehofer [44] extended the Discrete Event System Specification (DEVS) 8] to provide a formalism and a simulation environment for specifying combined ....

K. D. Forbus and B. Falkenhainer, "Self-Explanatory Simulations: An Integration of Qualitative and Quantitative Knowledge, " in AAAI, pp. 380 -- 387, 1990.

....that has emerged from recent work in AI attempting to automate this process. Our characterization is based on the compositional modeling method given by Falkenhainer and Forbus [13] Nayak s refinements [12] and related model formulation and simulation systems (QPC [14] SIMGEN [15] , DME [16] SIMLAB [17] On the basis of experience in using this technology to support engineering design [18] we find concepts and approaches that may be applicable to the modeling problem for intelligent systems in general. Any approach to model formulation must account for the ....

....library. The model is created to answer a specific query, and can be used in a tutorial setting to answer questions about the predicted behavior, such as what causes a change in a parameter. The equation model and explanation routines can be compiled into an efficient, custom simulation program [15] . DME [16, 25] uses a similar approach to model the electrical power system on the Hubble Space Telescope and the propulsion steering system on the Space Shuttle. DME can also generate natural language explanations of behavior and causality, which can be used in an operator training setting. ....

Forbus, K. D. and B. Falkenhainer. "Self-explanatory simulations: An integration of qualitative and quantitative knowledge." Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, 380-387, AAAI Press/The MIT Press, 1990.

....behaviors of interest, and assumed operating environment, and the system generates a low level equation model for simulation. It can then explain the predicted behavior in terms of the high level descriptions. DME s explanation approach is most similar to the approach taken in the SIMGEN systems [3,7]. Like DME, these systems use a compositional modeling method of model formulation, perform numeric simulation, and generate causal explanations. However, they use a qualitative model for explanation and a parallel quantitative model for simulation. DME generates qualitative explanations from the ....

K. D. Forbus & B. Falkenhainer. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. Proceedings of the Eighth National Conference on Artificial Intelligence, Boston, pages 380-387. AAAI Press/ The MIT Press, 1990.

....of Q2 and NSim can be intersected and must be consistent, otherwise the behaviour can be refused. SQSim can make more precise predictions than each of the methods alone could do. However, SQSim inherits the limitations of QSim as discussed above. Based on QPT (Forbus 1984) SimGen is presented in (Forbus Falkenhainer 1990; 1992) SimGen performs a qualitative analysis on a qualitative model to predict possible qualitative behaviours. For each behaviour a numeric model can be inferred for a numeric simulation run. Pika (Amador, Finkelstein, Weld 1993) is another self explanatory simulator, similar to SimGen. In ....

Forbus, K. D., and Falkenhainer, B. 1990. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In Proc. 8th National Conf. on Artificial Intelligence (AAAI-90), 380--387. Menlo Park, CA: AAAI Press/The MIT Press.

....not scale. Qualitative models have been used to generate explanations in tutoring and training systems [10,21] DME s explanation system can also generate explanations on such models (using QSIM [15] for simulation) Qualitative models have known limitations of scale. Work on the SIMGEN systems [5,9] was the first to achieve the effect of qualitative explanation using numerical simulation models. SIMGEN also uses a compositional modeling approach, and explains simulation data using the derivation of equations from model fragments. The SIMGEN strategy is to build parallel qualitative and ....

K. D. Forbus & B. Falkenhainer. Self-explanatory simula - tions: An integration of qualitative and quantitative knowledge. AAAI-90, pp. 380-387, 1990.

....a turbulent model. They form an assumption class predicated on the flow s Reynold s number. 2 Indirect influences are not really additive. Woods [85] redefines influences in a fully additive way. 13 Process structures give a rich insight into the system s behavior which is key for reasoning [34]. Process centered models are highly composable compared to the devicecentered one that can only composed via interconnection. In exchange for composability one has to trade for compactness. De Kleer s components hybrid systems in the form of Finite State Machines (FSMs) capturing the multiple ....

....These states would be modeled in three unrelated view in the QP theory. Although in both cases explicit modeling assumptions help documenting the models, finite state models add structure to the domain because they define assumption classes between a device s operating modes. As mentioned in [34] both approaches have their own benefits depending on the type of modeling required. Up to now process centered languages have been preferred in the qualitative reasoning community. The main reason is that their semantics translate directly in first order logic predicates which are supported by ....

[Article contains additional citation context not shown here]

Kenneth D. Forbus and Brian Falkenhainer. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In Proc. 8th National Conf. on Artificial Intelligence (AAAI-90), pages 380--387. AAAI Press/The MIT Press, 1990.

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Forbus, K. and Falkenhainer, B. "Self-explanatory simulations: An integration of qualitative and quantitative knowledge", AAAI-90, August, 1990.

....the numerical models that currently implement the functional vocabulary. By supporting qualitative reasoning about control systems, we should be able to build programs that provide critiques of a student s design. 2. We are extending the Feedback MiniLab to use self explanatory simulators [10,11] as system simulations. Using self explanatory simulators should both improve the quality of explanations and support more interesting student critiques. For example, if the qualitative analysis predicts that the student s controller won t correct properly The initial version of this system was ....

Forbus, K. and Falkenhainer, B. Self-explanatory simulations: An integration of qualitative and quantitative knowledge", Proceedings of AAAI-90, August, 1990.

....and limited generativity. 8.2. Technical innovations and surprises CyclePad relies on a synthesis of existing AI techniques. However, it does incorporate several extensions to the state of the art: The idea of structured explanation systems started with self explanatory simulators [19], which constructed the entire explanation system and its contents at simulator compilation time. As Section 6.2 discussed, CyclePad demonstrates that structured explanation systems can also be productively used with dynamically generated inferences. The closest precursors are presentation based ....

K.D. Forbus, B. Falkenhainer, Self-explanatory simulations: An integration of qualitative and quantitative knowledge, in: Proc. AAAI-90, Boston, MA, 1990, pp. 380--387.

.... in which students consider alternate energy resources for homes (cf. 31] In collaboration with Marcia Linn s group at Berkeley, we are adding complementary simulation based activities to their successful thermal curriculum [32,33,34,35] These activities use self explanatory simulators [22,23] as a way of allowing students to explore the various outcomes of their design choices in making a solar house. The other curriculum concerns ecosystems, using as a hook the creation of a life support system for a Mars colony. This will provide an arena for students to explore the requirements of ....

Forbus, K. and Falkenhainer, B. "Self-explanatory simulations: An integration of qualitative and quantitative knowledge", AAAI-90, August, 1990.

....developed components. Two of the components were complete intelligent learning environments in their own right: Active Illustrations [Forbus, 1997] enable learners to experiment with simulations of scientific phenomena, and to receive explanations about the causal influences behind the results [Forbus Falkenhainer 1990; 1995] Belvedere [Suthers Jones, 1997; Suthers et al. 1997] provides learners with an evidence mapping facility for recording relationships between statements labeled as hypotheses and data . A Scientific Argumentation Coach [Paolucci et al. 1996] guides students to seek empirical ....

Forbus, K. & Falkenhainer, B. (1990.) Self-explanatory simulations: An integration of qualitative and quantitative knowledge, Proceedings of AAAI-90.

....5 e.g. CORBA (http: www.omg.org news begin.htm) 6 e.g. XML (http: www.w3.org TR PR xml.html) 7 e.g. the IMS LTSC work mentioned in footnote 2. Component Based Construction of a Science Learning Space causal influences behind the results [Forbus Falkenhainer 1990; 1995] Belvedere [Suthers Jones, 1997; Suthers et al. 1997] provides learners with an evidence mapping facility for recording relationships between statements labeled as hypotheses and data . A Scientific Argumentation Advisor [Paolucci et al. 1996] guides students to seek empirical ....

....manner to achieve the proper educational experience. Domain Generality of Science Learning Space Components Active Illustrations, Belvedere and the Experimentation Tutor Agent were all designed to be relatively domain independent. Active Illustrations rely on self explanatory simulators [Forbus Falkenhainer, 1990, 1995] which can be automatically compiled for any domain for which there is a domain theory that can be expressed in Qualitative Process theory [Forbus, 1984] In practice, this means domains that can be described in terms of systems of ordinary differential Koedinger, K.R. Suthers, D.D. ....

Forbus, K. and Falkenhainer, B. (1990.) Self-explanatory simulations: An integration of qualitative and quantitative knowledge, Proceedings of AAAI-90.

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Forbus, K., & Falkenhainer, B. (1990). Self Explanatory Simulations: An integration of qualitative and quantitative knowledge. Proceedings of AAAI-90. 380-387.

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K. Forbus and B. Falkenhainer. Self-Explanatory Simulations: An integration of qualitative and quantitative knowledge. In Proceedings of AAAI-90, pages 380--387, 1990.

No context found.

Forbus, Kenneth D. and Falkenhainer, Brian 1990. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90). 380--387.

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Forbus, K. D., Falkenhainer, B.: Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In: Proceedings of the Eighth National Conference on Arti cial Intelligence. AAAI Press (1990) 380-387.

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Forbus, K.D. and Falkenhainer, B. Self-explanatory simulations: An integration of qualitative and quantitative knowledge. In Proceedings of the 8th National Conference on Artificial Intelligence, pages 380--387, 1990.

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Draft of 7/5/00 34 Forbus, K. and Falkenhainer, B. Self-explanatory simulations: An integration of qualitative and quantitative knowledge, Proceedings of AAAI-90. Forbus, K. and Falkenhainer, B. 1995. Scaling up Self-Explanatory Simulators: Polynomial-time Compilation. Proceedings of IJCAI-95, Montreal, Canada.

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Brian Falkenhainer and Kenneth D. Forbus. Self-Explanatory Simulations: An Integration of Qualitative and Quantitative Knowledge. AAAI-90 380--387.

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Brian Falkenhainer and Kenneth D. Forbus. Self-Explanatory Simulations: An Integration of Qualitative and Quantitative Knowledge. AAAI-90 380--387.

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IJCAI-89. Forbus, D. K. and Falkenhainer, B. (1990). Self-explanatory simulations: An integration of qualitative and quantitative knowledge. AAAI-90.

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