Unless it is a school essay, arguments don't go that way.
It is usually harder to encompass what a node (an atomic fact) is and what a link is (it usually goes beyond "support" and "counter"). Very often, this structure is not a tree. Maybe a DAG with weighted edges, but if it were that straightforward - knowledge graphs would simply work.
Instead of rehashing the same tree approach, we should adopt something closer to an LLM-embedding approach - for a given statement, we should have "relevant statements" with an additional dimension if it supports, counters, expands, provides an example, and so on. In this case, it wouldn't even be a DAG.
https://umap-learn.readthedocs.io/en/latest/basic_usage.html
> Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data
1. The data is uniformly distributed on Riemannian manifold;
2. The Riemannian metric is locally constant (or can be approximated as such);
3. The manifold is locally connected.
There's a lamport paper lying around somewhere that also talks about representing arguments and proofs as DAGs.
Thanks!
1. https://aclanthology.org/2020.argmining-1.1/
2. https://paperswithcode.com/paper/opendebateevidence-a-massiv... (under review at a main conference, but we had an acceptance to ARGMIN 2024 at ACL 2024 which we declined)
I'm very interested in talking with the authors of this work about how we can think about structured argumentation notations like this for the American Competitive debate community. American Competitive Debaters have their own informal markdown-like structures and fuzzy-syntax of their formatting alongside so much jargon that I really want to see how it can map to something like Argdown.
I'm wondering if this could this be used for something like comparing alternatives to solve a problem. In that case I'd expect the root to be a description of the problem, then alternatives, then pros-cons for those alternatives.
I'd never heard of this at all before despite searching, so I imagine there's a lot I don't know.
Edit: and I also don't see a way to do multiple arguments like that in a single document, unless you mean as a soft linkage.
OP, if you’re the author: any plans for next steps? I’ll be folding this into my upcoming book and website (and almost certainly extending it a bit), so I’d be curious to hear if there’s other large scale projects underway.
Beautiful docs btw, this style should be a lesson for all of us. I guess you’d expect someone interested in arguments to write clearly lol
Also, Mermaid (https://mermaid.js.org/) exists.
https://en.wikipedia.org/wiki/List_of_logic_symbols
These can be especially helpful for Causal Analysis based on System Theory (CAST) which is similar to root cause analysis plus uses more logical dependencies.
At work, we have architectural decision logs, for example how to structure our authentication, how to deploy services and such. Some of these decisions are fairly obvious and straightforward, but some decisions come from days and weeks of discussions between different teams and people over the past few years.
This looks like an interesting way to provide an overview of what has been considered and what the take of the group is on these different points. This would allow new people in the teams to challenge things more substantially instead of going through four steps we've been through several times already.
I've used it a few times. For example when justifying a project of migrating a large legacy app to docker deployment.
It's also useful for getting a specific kind of result from LLMs - GPT knows how to summarise discussions/conflicts in ArgDown format.
Edit: it's... "up", but suddenly Turkish and broken: https://arguman.org
A short overview of the Argumentation theory and tooling field:
“Within computer science, the ArgMAS workshop series (Argumentation in Multi-Agent Systems), the CMNA workshop series,[34] and the COMMA Conference, are regular annual events attracting participants from every continent. The journal Argument & Computation is dedicated to exploring the intersection between argumentation and computer science. ArgMining is a workshop series dedicated specifically to the related argument mining task. Data from the collaborative structured online argumentation platform Kialo has been used to train and to evaluate natural language processing AI systems such as, most commonly, BERT and its variants.” https://en.m.wikipedia.org/wiki/Argumentation_theory
https://en.m.wikipedia.org/wiki/Argument_technology
https://en.m.wikipedia.org/wiki/Argumentation_framework
Sibling tooling (with help from Sonnet and Wikipedia):
1. Argument Interchange Format (AIF): This is a standardized format for representing argumentative structures in a machine-readable way. It's used in various academic tools and research projects. 2006
2. Rationale: A software tool developed for academic use, particularly in teaching critical thinking and argument analysis. It offers more structured mapping capabilities than Kialo. 2004
3. Araucaria: An open-source argument mapping software developed by researchers at the University of Dundee. It's designed for analyzing and diagramming arguments. 2001
4. ArgDown: A markdown-like language for creating argument maps, which can be useful for programmatic approaches to argument analysis. 2016
5. OVA (Online Visualization of Argument): A web-based tool for argument analysis and visualization, developed by researchers at the University of Dundee. 2010
6. Argunet: An open-source argument mapping software that allows for collaborative work and integrates with a database of arguments. 2007
7. AGORA-net: A web-based platform for argument reconstruction and evaluation, used in academic settings. 2013
8. Kialo 2017 https://en.m.wikipedia.org/wiki/Kialo
9. ADA tools. Gregor Betz (2022). "Natural-Language Multi-Agent Simulations of Argumentative Opinion Dynamics". Journal of Artificial Societies and Social Simulation. 25: 2. arXiv:2104.06737. doi:10.18564/jasss.4725. S2CID 233231231 https://www.gregorbetz.de/
10. Argument Analytics http://analytics.arg.tech/
11. IBM's Grand Challenge, Project Debater 2011- Published in Nature in March 2021 https://en.m.wikipedia.org/wiki/Project_Debater
12. German research funder, DFG's nationwide research programme on Robust Argumentation Machines, RATIO. 2019 https://spp-ratio.de/
13. UK nationwide deployment of The Evidence Toolkit by the BBC. 2019 https://www.bbc.co.uk/teach/young-reporter/articles/z6v3hcw
Though I doubt that arguments in this restricted form are more expressive than the original representation.
