Toward data-driven solutions to interactive dynamic influence diagrams Article Swipe
Yinghui Pan
,
Jing Tang
,
Biyang Ma
,
Yifeng Zeng
,
Zhong Ming
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s10115-021-01600-5
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s10115-021-01600-5
With the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.
Related Topics
Concepts
Computer science
Influence diagram
Bottleneck
Cluster analysis
Machine learning
Decision tree
Domain (mathematical analysis)
Artificial intelligence
Context (archaeology)
Domain knowledge
Dynamic decision-making
Process (computing)
Data mining
Mathematics
Mathematical analysis
Biology
Paleontology
Embedded system
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10115-021-01600-5
- https://link.springer.com/content/pdf/10.1007/s10115-021-01600-5.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3183539237
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3183539237Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10115-021-01600-5Digital Object Identifier
- Title
-
Toward data-driven solutions to interactive dynamic influence diagramsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-08Full publication date if available
- Authors
-
Yinghui Pan, Jing Tang, Biyang Ma, Yifeng Zeng, Zhong MingList of authors in order
- Landing page
-
https://doi.org/10.1007/s10115-021-01600-5Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10115-021-01600-5.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s10115-021-01600-5.pdfDirect OA link when available
- Concepts
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Computer science, Influence diagram, Bottleneck, Cluster analysis, Machine learning, Decision tree, Domain (mathematical analysis), Artificial intelligence, Context (archaeology), Domain knowledge, Dynamic decision-making, Process (computing), Data mining, Mathematics, Mathematical analysis, Biology, Paleontology, Embedded system, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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