Argument technology

Argument technology is a sub-field of artificial intelligence that focuses on applying computational techniques to the creation, identification, analysis, navigation, evaluation and visualisation of arguments and debates. In the 1980s and 1990s, philosophical theories of arguments were leveraged to handle key computational challenges, such as modeling non-monotonic and defeasible reasoning and designing robust coordination protocols for multi-agent systems. At the same time, mechanisms for computing semantics of Argumentation frameworks were introduced as a way of providing a calculus of opposition[1] for computing what it is reasonable to believe in the context of conflicting arguments.

With these foundations in place, the area was kick-started by a workshop held in the Scottish Highlands in 2000, the result of which was a book coauthored by philosophers of argument, rhetoricians, legal scholars and AI researchers.[2] Since then, the area has been supported by various dedicated events such as the International Workshop on Computational Models of Natural Argument (CMNA)[3] which has run annually since 2001; the International Workshop on Argument in Multi Agent Systems (ArgMAS) annually since 2004; the Workshop on Argument Mining,[4] annually since 2014, and the Conference on Computational Models of Argument (COMMA),[5] biennially since 2006. Since 2010, the field has also had its own journal, Argument & Computation,[6] which was published by Taylor & Francis until 2016[7] and since then by IOS Press.

One of the challenges that Argument Technology faced was a lack of standardisation in the representation and underlying conception of argument in machine readable terms. Many different software tools for manual argument analysis, in particular, developed idiosyncratic and ad hoc ways of representing arguments which reflected differing underlying ways of conceiving of argumentative structure.[8] This lack of standardisation also meant that there was no interchange between tools or between research projects, and little re-use of data resources that were often expensive to create. To tackle this problem, the Argument Interchange Format set out to establish a common standard that captured the minimal common features of argumentation which could then be extended in different settings.

Argment technology has applications in a variety of domains, including education, healthcare, policy making, and risk management and has a variety of sub-fields, methodologies and technologies.[9]

Technologies

Argument assistant
See also: Proof assistant and Computer-assisted proof

An argument assistant is a software which conveniences users when writing arguments. Argument assistants can convenience users as they compose content and as they review content from one other, including in dialogical contexts. In addition to Web services, such functionalities can be provided through the plugin architectures of word processor software or those of Web browsers. Internet forums, for instance, can be greatly enhanced by such software tools and services.

Argument blogging
See also: Argument Web

ArguBlogging is software which allows its users to select portions of hypertext on webpages in their Web browsers and to agree or disagree with the selected content, posting their arguments to their blogs with linked argument data.[10] It is implemented as a bookmarklet, adding functionality to Web browsers and interoperating with blogging platforms such as Blogger and Tumblr.

Argument mapping
Main article: Argument map

Argument maps are visual, diagrammatic representations of arguments. Such visual diagrams facilitate diagrammatic reasoning and promote one's ability to grasp and to make sense of information rapidly and readily. Argument maps can provide structured, semi-formal frameworks for representing arguments using interactive visual language.

Argument mining
Main article: Argument mining

Argument mining, or argumentation mining, is a research area within the natural language processing field. The goal of argument mining is the automatic extraction and identification of argumentative structures from natural language text with the aid of computer programs.

Main article: Search engine (computing)

An argument search engine is a search engine that is given a topic as a user query and returns a list of arguments for and against the topic.[11] Such engines could be used to support informed decision-making or to help debaters prepare for debates.

Automated argumentative essay scoring
Main article: Automated essay scoring

The goal of automated argumentative essay scoring systems is to assist students in improving their writing skills by measuring the quality of their argumentative content.[12][13]

Debate technology

Debate technology focuses on human-machine interaction and in particular providing systems that support, monitor and engage in debate. One of the most high-profile examples of debating technology is IBM's Project Debater which combines scripted communication with very large-scale processing of news articles to identify and construct arguments on the fly in a competitive debating setting. Debating technology also encompasses tools aimed at providing insight into debates, typically using techniques from data science. These analytics have been developed in both academic[14] and commercial[15] settings.

Decision support system
Main articles: Decision support system and Intelligent decision support system

Argument technology can enhance decision support systems and intelligent decision support systems.

Ethical decision support system
See also: Moral reasoning

An ethical decision support system is a decision support system which supports users in moral reasoning and decision-making.[16][17]

Main articles: Legal expert system and Applications of artificial intelligence to legal informatics

A legal decision support system is a decision support system which supports users in legal reasoning and decision-making.

Explainable artificial intelligence
Main articles: Explainable artificial intelligence, Argument § Explanations, and Explanation

An explainable or transparent artificial intelligence system is an artificial intelligence system whose actions can be easily understood by humans.

Intelligent tutoring system
Main article: Intelligent tutoring system

An intelligent tutoring system is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. The intersection of argument technology and intelligent tutoring systems includes computer systems which aim to provide instruction in: critical thinking, argumentation,[18] ethics,[19] law,[20] mathematics,[21] and philosophy.

Main article: Legal expert system

A legal expert system is a domain-specific expert system that uses artificial intelligence to emulate the decision-making abilities of a human expert in the field of law.

Machine ethics
Main article: Machine ethics
See also: Moral reasoning

Machine ethics is a part of the ethics of artificial intelligence concerned with the moral behavior of artificially intelligent beings. As humans argue with respect to morality and moral behavior, argument can be envisioned as a component of machine ethics systems and moral reasoning components.

Proof assistant
Main articles: Proof assistant and Computer-assisted proof

In computer science and mathematical logic, a proof assistant or interactive theorem prover is a software tool to assist with the development of formal proofs by human-machine collaboration. This involves some sort of interactive proof editor, or other interface, with which a human can guide the search for proofs, the details of which are stored in, and some steps provided by, a computer.

References
  1. Prakken, Henry (2014). "The ASPIC+ framework for structured argumentation: a tutorial". Argument & Computation. 5 (1): 31–62.
  2. Reed, C. & Norman, T.J. (eds) Argumentation Machines. Kluwer, 2003.
  3. Computational Models of Natural Argument
  4. Proceedings of Workshops on Argument Mining.
  5. Computational Models of Argument conference series
  6. Journal of Argument & Computation
  7. Journal of Argument & Computation Archived 2012-02-21 at the Wayback Machine
  8. Scheuer, O.; Loll, F.; Pinkwart, N.; McLaren, B.M. "Computer-supported argumentation: A review of the state of the art". Computer-Supported Collaborative Learning. 5 (1): 43–102.
  9. Bex, Floris J., Floriana Grasso, Nancy Green, Fabio Paglieri, and Chris Reed. Argument Technologies: Theory, Analysis, and Applications. London: College Publications, 2017.
  10. Bex, Floris, Mark Snaith, John Lawrence, and Chris Reed. "Argublogging: An application for the argument web." Web Semantics: Science, Services and Agents on the World Wide Web 25 (2014): 9-15.
  11. Aharoni, Ehud; et al. (2014). "Claims on demand–an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora". Proceedings of COLING 2014: 6–9.
  12. Stab, Christian; Gurevych, Iryna (2014). "Identifying argumentative discourse structures in persuasive essays". Proceedings of EMNLP 2014: 46–56. doi:10.3115/v1/D14-1006.
  13. Green, Nancy L. "Towards automated analysis of student arguments." In International Conference on Artificial Intelligence in Education, pp. 591-594. Springer, Berlin, Heidelberg, 2013.
  14. "Argument Analytics". Retrieved 11 August 2020.
  15. "Kialo". Retrieved 11 August 2020.
  16. Mancherjee, Kevin, and Angela C. Sodan. "Can computer tools support ethical decision making?." ACM SIGCAS Computers and Society 34, no. 2 (2004): 1.
  17. Mathieson, Kieran. "Towards a design science of ethical decision support." Journal of Business Ethics 76, no. 3 (2007): 269-292.
  18. Loll, Frank, Niels Pinkwart, Oliver Scheuer, and Bruce M. McLaren. "Towards a flexible intelligent tutoring system for argumentation." In Advanced Learning Technologies, 2009. ICALT 2009. Ninth IEEE International Conference on, pp. 647-648. IEEE, 2009.
  19. Goldin, Ilya M., Kevin D. Ashley, and Rosa L. Pinkus. "Introducing PETE: computer support for teaching ethics." In Proceedings of the 8th international conference on Artificial intelligence and law, pp. 94-98. ACM, 2001.
  20. Ashley, Kevin D., and Vincent Aleven. "Toward an intelligent tutoring system for teaching law students to argue with cases." In Proceedings of the 3rd international conference on Artificial intelligence and law, pp. 42-52. ACM, 1991.
  21. Ritter, Steven, John R. Anderson, Kenneth R. Koedinger, and Albert Corbett. "Cognitive Tutor: Applied research in mathematics education." Psychonomic bulletin & review 14, no. 2 (2007): 249-255.
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