The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Formal systems for persuasion dialogue
Abstract
This article reviews formal systems that regulate persuasion dialogues. In such dialogues two or more participants aim to resolve a difference of opinion, each trying to persuade the other participants to adopt their point of view. Systems for persuasion dialogue have found application in various fields of computer science, such as non-monotonic logic, artificial intelligence and law, multi-agent systems, intelligent tutoring and computer-supported collaborative argumentation. Taking a game-theoretic view on dialogue systems, this review proposes a formal specification of the main elements of dialogue systems for persuasion and then uses it to critically review some of the main formal systems for persuasion. The focus of this review will be on regulating the interaction between agents rather than on the design and behaviour of individual agents within a dialogue.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Toulmin, S. E. (2003) · doi:10.1017/cbo9780511840005 · Cited by 7,101
Cited by
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302
The Persuasive Power of Large Language Models
Breum, S. M., Egdal, D. V., Mortensen, V. G., Møller, A. G., & Aiello, L. M. (2023) · arXiv · doi:10.48550/arxiv.2312.15523 · Cited by 57
The Persuasive Power of Large Language Models
Breum, S. M., Egdal, D. V., Gram Mortensen, V., Møller, A. G., & Aiello, L. M. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31304 · Cited by 57
Metadata
- Authors
- PRAKKEN, H.
- Venue
- The Knowledge Engineering Review
- Publisher
- Maximum Academic Press
- Published
- 2006-06
- Volume
- 21
- Issue
- 2
- Pages
- 163-188
- ISSN
- 0269-8889 (ISSN-L)
- 1469-8005
- OpenAlex
- W2100758964
- Cited by
- 297
- References
- 1
- FWCI
- 17.22
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
