A model for reasoning about persistence and causation
Abstract
Reasoning about change requires predicting how long a proposition, having become true, will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe that a proposition persists indefinitely simply because there is no way for the agent to infer a contravening proposition with certainty. In this paper, we describe a model of causal reasoning that accounts for knowledge concerning cause‐and‐effect relationships and knowledge concerning the tendency for propositions to persist or not as a function of time passing. Our model has a natural encoding in the form of a network representation for probabilistic models. We consider the computational properties of our model by reviewing recent advances in computing the consequences of models encoded in this network representation. Finally, we discuss how our probabilistic model addresses certain classical problems in temporal reasoning (e. g., the frame and qualification problems).
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Bayesian Network arose as a Directed Acyclic Graph representation model for statistical models in use, such as NB and HMM, the latter considered the simplest dynamic BN.
“Among these techniques, Bayesian Network (BN) arose as a Directed Acyclic Graph (DAG) representation model for the statistical models in use [352], such as NB and HMM -the latter considered as the simplest dynamic BN [107, 110]-.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [110]
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- Authors
- Dean, T., & Kanazawa, K.
- Affiliations
- Brown University (US)
- Venue
- Computational Intelligence
- Publisher
- Wiley
- Published
- 1989-02
- Volume
- 5
- Issue
- 2
- Pages
- 142-150
- ISSN
- 0824-7935 (ISSN-L)
- 1467-8640
- OpenAlex
- W2020294948
- Cited by
- 999
- References
- 17
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 15 · 2023: 10 · 2022: 11 · 2021: 30 · 2020: 25 · 2019: 29 · 2018: 23 · 2017: 41 · 2016: 27 · 2015: 43 · 2014: 37 · 2013: 67 · 2012: 57
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computational Intelligence (publishedVersion)
- Funders
- National Science Foundation
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- persistence, causality, causal reasoning, temporal reasoning, probabilistic graphical models, belief revision, frame problem, qualification problem, reasoning about change
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