The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Qualitative process theory
Abstract
Abstract Objects move, collide, flow, bend, heat up, cool down, stretch, compress, and boil. These and other things that cause changes in objects over time are intuitively characterized as processes . To understand commonsense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation for quantity in terms of inequalities, called the quantity space . This paper describes the basic concepts of qualitative process theory, several different kinds of reasoning that can be performed with them, and discusses its implications for causal reasoning. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string, but not push with it.
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Citations
Cites
Maintaining knowledge about temporal intervals
Allen, J. F. (1983) · Communications of the ACM · doi:10.1145/182.358434 · Cited by 7,593 · cited 3×
“Recently several attempts have been made to model temporal reasoning by Allen [1] and McDermott [30] .”
BackgroundMethodSTRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
Fikes, R. E., & Nilsson, N. J. (1971) · Artificial Intelligence · doi:10.1016/0004-3702(71)90010-5 · Cited by 4,882
Cited by
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
“These efforts have grown tomodel physical processes (Forbus 1984) or tomodel a series of actions as scripts (Schank and Abelson 2013) for stories—both of which are not depicted in a single static image but which play roles in an image’s story (Vedantam et al.”
BackgroundThe structure-mapping engine: Algorithm and examples
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989) · Artificial Intelligence · doi:10.1016/0004-3702(89)90077-5 · Cited by 1,454
Self-citationCausal Models: How People Think about the World and Its Alternatives
Sloman, S. (2005) · doi:10.1093/acprof:oso/9780195183115.001.0001 · Cited by 677
LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE
Biswas, G., Leelawong, K., Schwartz, D., Vye, N., & The Teachable Agents Group at Vande (2005) · Applied Artificial Intelligence · doi:10.1080/08839510590910200 · Cited by 462
A Framework for Knowledge-Based Temporal Abstraction
Shahar, Y. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00025-2 · Cited by 461
Metadata
- Authors
- Forbus, K. D.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1984-12
- Volume
- 24
- Issue
- 1-3
- Pages
- 85-168
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2053190303
- Cited by
- 2,013
- References
- 2
- FWCI
- 58.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Forbus, K. D. (1984). Qualitative process theory. Artificial Intelligence. https://doi.org/10.1016/0004-3702(84)90038-9
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