One of the objectives of the How Things Work
Project is to develop techniques for generating causal explanations, in
natural language, of the simulated behavior of physical devices. A method for
generating such explanations has been implemented in the Device Modeling
Environment (DME) [3, 4]. DME is a system that helps formulate mathematical
simulation models from a library of model fragments using a Compositional
Modeling approach, and simulate the modeled systems using qualitative and
DME generates text and graphics to describe the system being simulated, by
composing information from a model library and a trace of the simulation [1, 2].
Although the underlying behavior prediction is accomplished with numerical
integration and symbolic reasoning operating on mathematical models, the
explanations are abstracted and filtered for human consumption. Causality,
salience, and level of detail are determined using domain-independent
heuristics. Since these heuristics can't anticipate the exact information
needs of the reader, further information is always available. Included with
each explanation is a set of follow-up questions. The reader can ask for more
information by choosing one of these questions. The path of questions and
explanations is generated on demand on the basis of the reader choices.
Example Scenario: Self-explanatory Simulation
We have produced an
example scenario in which a moderately complex fluid
system is simulated under various conditions to test operator procedures. An
engineer has assembled a model of the system using DME, and the results are
presented as a large hypertext web of explanations linked by follow-up
questions to other explanations. Such a hypertext web can be incorporated into
multimedia email conversations.
Consider how such a simulation might be used in an email conversation among
engineers. We've mocked up an example email
message from a (fictitious) NASA engineer to the the designers of the
Reaction Control System used on the Space Shuttles. In the message the
engineer refers to a scenario which is a simulation of the RCS and the actions
taken by a human operator. The engineer refers to the beginning of the
scenario (the pages of the virtual document in which DME summarizes the entire
scenario), and also points into particular explanations of specific
quantiuties of interest.
How It Works: Virtual Documents
This example demonstrates how the machine-generated, interactive explanation
can be delivered in a hypermedia environment such as the World Wide Web. In
Web parlance, the product is called a virtual document. In our current
implementation, DME acts as an HTTP server that dynamically generates "pages"
of explanations in response to "requests" from the http client (e.g., Mosaic).
Each request is a query to DME that has been encoded as a virtual document
address (a URL). Since DME generates these queries (i.e. the follow-up
questions), the user need not worry about the query syntax and can traverse
the document using simple user interface gestures such as clicking on
The entire web of explanations for this scenario, if saved to disk, would take
several hundred megabytes (most of which is text). Thus, this example
demonstrates both the potential functionality of machine-generated
documentation and the practicality of using a server to deliver virtual
For more information, see the Explanation Generation section of the
How Things Work Project Overview, and the references below.
- Gruber, T. R. & Gautier, P. O.(1992). Machine-generated
Explanations of Engineering Models: A compositional modeling approach.
Proceedings of the 13th International Joint Conference on Artificial
Intelligence, Chambery, France, August 1993. pp 1502-1508. Distributed
by Morgan Kaufmann Publishers, Inc., San Mateo, CA.
Also available as Technical Report KSL-92-85.
- Gautier, P. O. & Gruber, T. R. (1993). Generating Explanations of
Device Behavior Using Compositional Modeling and Causal Ordering.
Proceedings of the Eleventh National Conference on Artificial
Intelligence. Washington, DC, July 1993. pp 264-270. Published by AAAI
Press, Menlo Park, CA.
Also available as Technical Report KSL-93-06.
- Iwasaki, Y. and Low, C.M. Model generation and simulation of device behavior with continuous and discrete changes. Intelligent Systems Engineering, 1(2), 1993.
- Iwasaki, Y. and Levy, A. Y. Automated model selection for simulation. Knowledge Systems Laboratory, Stanford University, Technical Report KSL-93-11,
- T. R. Gruber and D. M. Russell. Generative Design
Rationale: Beyond the Record and Replay Paradigm. In T. Moran and J. H. Carroll, (Eds.), Design Rationale: Concepts, Techniques, and Use. Lawrence Erlbaum Associates, 1994, in press. Available as Technical Report KSL-92-59.
Tom Gruber <firstname.lastname@example.org>