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Argumentation Community Group

The Argumentation Community Group will facilitate and promote the use of the Web for all forms of argumentation. The group will discuss and design both argumentation representation formats and systems.

Note: Community Groups are proposed and run by the community. Although W3C hosts these conversations, the groups do not necessarily represent the views of the W3C Membership or staff.

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Moral Reasoning Systems

Introduction

Automated reasoning is a branch of artificial intelligence dedicated to understanding different aspects of reasoning; moral reasoning is reasoning concerned with morality. Automated moral reasoning is a research topic pertaining to the understanding of and the modeling and simulation of moral reasoning.

Moral Reasoning Systems and Education

Five varieties of moral reasoning systems with educational applications to consider are indicated.

Firstly, there is a variety of moral reasoning system with console-based or text-based user interfaces, a variety which possibly makes use of custom programming languages. This variety requires some specialized expertise to use, resembling, perhaps, computer algebra systems, automated theorem provers and proof assistants.

Secondly, there is a variety of moral reasoning system which interoperates with software applications requiring less specialized expertise to use, software where the users needn’t be computer programmers. Examples include decision support systems, software which support individual or organizational decision-making activities.

Thirdly, there is a variety of moral reasoning system with natural language and multimodal user interfaces. This variety includes dialog systems, virtual humans, intelligent personal assistants and intelligent tutoring systems. This variety can conveniently answer, discuss and advise larger numbers of users with regard to questions that they might ask, including in educational contexts.

Fourthly, there is a variety of moral reasoning system which interoperates with the processing and generation of stories, fables, parables or exemplums. This variety can be of use in processing the moral messages of literary texts and generating literary texts which teach moral messages.

Fifthly, there is a variety of moral reasoning system which interoperates with interactive digital entertainment, serious games, simulations and learning environments. This variety interoperates with virtual interactive storytellers, virtual directors, drama managers, experience managers and other educational narrative technologies.

Comparative Moral Reasoning

We can consider that moral reasoning systems could load “configuration and data” before providing outputs for inputs or questions. Such configuration and data include: axiomatic systems, philosophies, schools of thought, principles, beliefs, values, models of characters, self-models or role models, and generic models of cultural stereotypes. How system outputs vary based upon variations of loaded configuration and data is interesting.

We can envision systems which can simulate moral reasoning per the stages of moral development from models, for instance Kohlberg’s. We can envision systems which can simulate moral reasoning per multiple belief systems, philosophies or schools of thought. We can envision systems which can compare reasoning from across various configurations or loaded data, across various philosophies or schools of thought, and can provide explanation and argumentation as components of system output.

Automated Moral Reasoning and Planning

Automated planning and scheduling is a branch of artificial intelligence concerned with the realization of strategies or action sequences. Planning algorithms are often instrumental to generating the behavior of intelligent systems and robotics.

Machine ethics, or computational ethics, is a part of the ethics of artificial intelligence concerned with the moral behavior of artificially intelligent systems. Moral reasoning components should be interoperable with planning and scheduling components.

Uses of planning are much broader than robotics. Uses of planning extend into every sector, into industry, academia, science, military and government, and into public policy. Combinations of planners and moral reasoning can provide societal benefits transcending robotics and machine ethics.

Conclusion

Moral reasoning systems can provide broad societal benefits including computer-aided moral reasoning, computer-aided authoring of literature, new tools for philosophy, law, social sciences, the digital humanities, new decision support and public policy technologies, and new tools for education.

 

References

Barber, Heather, and Daniel Kudenko. “Generation of Adaptive Dilemma-based Interactive Narratives.” IEEE Transactions on Computational Intelligence and AI in Games 1, no. 4 (2009): 309-326.

Colyvan, Mark, Damian Cox, and Katie Steele. “Modelling the Moral Dimension of Decisions.” Noûs 44, no. 3 (2010): 503-529.

French, Simon. Decision Theory: An Introduction to the Mathematics of Rationality. Halsted Press, 1986.

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.

Greco, Salvatore, J. Figueira, and M. Ehrgott. “Multiple Criteria Decision Analysis.” Springer’s International series (2005).

Harmon, Sarah. “An Expressive Dilemma Generation Model for Players and Artificial Agents.” In Twelfth Artificial Intelligence and Interactive Digital Entertainment Conference. 2016.

Hodhod, Rania. “Interactive Narrative and Intelligent Tutoring for Ill-Defined Domains.” (2008).

Hodhod, Rania, and Daniel Kudenko. “Interactive Narrative and Intelligent Tutoring for Ethics Domain.” Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and Feedback in Ill-Defined Domains. (2008): 13.

Hodhod, Rania, Daniel Kudenko, and Paul Cairns. “AEINS: Adaptive Educational Interactive Narrative System to Teach Ethics.” In AIED 2009: 14th International Conference on Artificial Intelligence in Education Workshops Proceedings, p. 79. 2009.

Hodhod, Rania, Daniel Kudenko, and Paul Cairns. “Serious Games to Teach Ethics.” AISB’09: Artificial and Ambient Intelligence (2009).

Lapsley, Daniel K. Moral Psychology. Westview Press, 1996.

Mancherjee, Kevin, and Angela C. Sodan. “Can Computer Tools Support Ethical Decision Making?.” ACM SIGCAS Computers and Society 34, no. 2 (2004): 1.

McLaren, Bruce M. “Extensionally Defining Principles and Cases in Ethics: An AI Model.” Artificial Intelligence 150, no. 1 (2003): 145-181.

McLaren, Bruce M. “Computational Models of Ethical Reasoning: Challenges, Initial Steps, and Future Directions.” IEEE intelligent systems 21, no. 4 (2006): 29-37.

Prakken, Henry, and Giovanni Sartor. “Law and Logic: A Review from an Argumentation Perspective.” Artificial intelligence 227 (2015): 214-245.

Rahwan, Iyad, Simon D. Parsons, and Nicolas Maudet. Argumentation in Multi-agent Systems. Springer-Verlag Berlin Heidelberg, 2010.

Robbins, Russell W., William A. Wallace, and Bill Puka. “Supporting Ethical Problem Solving: An Exploratory Investigation.” In Proceedings of the 2004 SIGMIS conference on Computer personnel research: Careers, culture, and ethics in a networked environment, pp. 134-143. ACM, 2004.

Saptawijaya, Ari, and Luís Moniz Pereira. “Towards Modeling Morality Computationally with Logic Programming.” In International Symposium on Practical Aspects of Declarative Languages, pp. 104-119. Springer International Publishing, 2014.

Schrier, Karen. “EPIC: A Framework for Using Video Games in Ethics Education.” Journal of Moral Education 44, no. 4 (2015): 393-424.

Sharipova, Mayya, and Gordon McCalla. “Supporting Students’ Interactions over Case Studies.” In International Conference on Artificial Intelligence in Education, pp. 772-775. Springer International Publishing, 2015.

Tappan, Mark B., and Lyn Mikel Brown. “Stories Told and Lessons Learned: Toward a Narrative Approach to Moral Development and Moral Education.” Harvard Educational Review 59, no. 2 (1989): 182-206.

Tappan, Mark B. “Hermeneutics and Moral Development: Interpreting Narrative Representations of Moral Experience.” Developmental Review 10, no. 3 (1990): 239-265.

Tappan, Mark B., and Packer, M. (Eds.). Narrative and Storytelling: Implications for Understanding Moral Development. New Directions for Child Development, #54. San Franciso Jossey-Bass, 1991.

Vitz, Paul C. “The Use of Stories in Moral Development: New Psychological Reasons for an Old Education Method.” American Psychologist 45, no. 6 (1990): 709.

Generating and Detecting Persuasive Rhetoric

How can software detect persuasion in rhetoric and dialog occurring between people or between people and dialog systems?

In Opinion Polling Systems and Virtual Opinion Pollsters, I broached dialog systems which interact with users to collect their opinions. Presented was that virtual pollsters should adhere to the best practices of survey methodology and questionnaire construction, cognizant of questionnaire construction issues, question sequence issues, question wording issues and other issues with dialogs.

A broader matter, broached in E-Participation, Decision Support Systems, Multi-document Natural Language Processing and Cognitive Bias Mitigation, is one of detecting persuasion, persuasion occurring in documents, dialogs and transcripts, persuasion from humans and from natural language generation and dialog systems. For those interested, some publications are indicated about persuasion.

Persuasion

Cialdini, Robert B. “The science of persuasion.” (2004).

Kacprzak, Magdalena, Anna Sawicka, Andrzej Zbrzezny, and Katarzyna Zukowska. “A formal model of an argumentative dialogue in the management of emotions.” Pozna n Reasoning Week: 59.

Majone, Giandomenico. Evidence, argument, and persuasion in the policy process. Yale University Press, 1989.

Prakken, Henry. “Models of persuasion dialogue.” In Argumentation in artificial intelligence, pp. 281-300. Springer US, 2009.

van Benthem, Johan. Argumentation in artificial intelligence. Edited by Iyad Rahwan, and Guillermo R. Simari. Vol. 47. Heidelberg: Springer, 2009.

Walton, Douglas. Media argumentation: dialectic, persuasion and rhetoric. Cambridge University Press, 2007.

Generating Persuasive Rhetoric

Devereux, Joseph, and Chris Reed. “Strategic argumentation in rigorous persuasion dialogue.” In International Workshop on Argumentation in Multi-Agent Systems, pp. 94-113. Springer Berlin Heidelberg, 2009.

Marcu, Daniel. “The conceptual and linguistic facets of persuasive arguments.” In Proceedings of the ECAI {96 Workshop, Gaps and Bridges: New Directions in Planning and Natural Language Generation, pages 43 {46, Budapest,
Hungary. 1996.

Moulin, Bernard, Hengameh Irandoust, Micheline Bélanger, and Gaëlle Desbordes. “Explanation and argumentation capabilities: Towards the creation of more persuasive agents.” Artificial Intelligence Review 17, no. 3 (2002): 169-222.

Rosenfeld, Ariel, and Sarit Kraus. “Strategical Argumentative Agent for Human Persuasion.” In ECAI 2016: 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands-Including Prestigious Applications of Artificial Intelligence (PAIS 2016), vol. 285, p. 320. IOS Press, 2016.

Detecting Persuasive Rhetoric

Stance Classification Using Dialogic Properties of Persuasion by Marilyn A. Walker, Pranav Anand, Robert Abbott and Ricky Grant,

Allen, James F., and C. Raymond Perrault. “Analyzing intention in utterances.” Artificial intelligence 15, no. 3 (1980): 143-178.

Gilbert, Henry T. “Persuasion detection in conversation.” PhD diss., Monterey, California. Naval Postgraduate School, 2010.

Ortiz, Pedro. “Machine learning techniques for persuasion detection in conversation.” PhD diss., Monterey, California. Naval Postgraduate School, 2010.

Young, Joel, and Pedro Ortiz. “Automated Persuasion Detection in Conversation.” GSTF Journal on Computing 1, no. 3 (2011).

Analysis of Visual Persuasion

Joo, Jungseock, Weixin Li, Francis F. Steen, and Song-Chun Zhu. “Visual persuasion: Inferring communicative intents of images.” In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 216-223. IEEE, 2014.

Joo, Jungseock. “Visual Persuasion in Mass Media: A Computational Framework for Understanding Visual Communication.” PhD diss., UNIVERSITY OF CALIFORNIA, LOS ANGELES, 2015.

Narrative Persuasion

Hamby, Anne, David Brinberg, and James Jaccard. “A Conceptual Framework of Narrative Persuasion.” Journal of Media Psychology (2016).

Hamby, Anne, David Brinberg, and Kim Daniloski. “Reflecting on the journey: Mechanisms in narrative persuasion.” Journal of Consumer Psychology (2016).

Query Analysis, Planning and Optimization utilizing Database Schema Metadata and Ontology

Advancements to database schema metadata and ontology advance query analysis, query planning and query optimization. Advancements to database schema metadata advance the analysis of query plans, for example those outputted by the SQL keywords DESCRIBE, EXPLAIN and EXPLAIN EXTENDED.

1. Database software should version to provide the capability to annotate database schemas, tables, columns and relations with (a) URI-based identifiers, (b) URI-based classes, and (c) entire RDF graphs.

2. New SQL syntax should be of use to access the URI-based identifiers, classes and graph-based metadata about database schemas, tables, columns and relations.

3. New ontologies should be authored and standardized to provide new features for databases and data usage scenarios.

4. Database software and logic programming environments should version to utilize standard API for interoperation.

New usage scenarios include: (a) measurement, calculation or estimation of data about specific queries or query plans upon one or more data resources, the measurements, calculations or estimations pertaining to various privacy topics, preserving privacy in big data, (b) the processing of representations of, i.e. expression trees of, arbitrarily large queries or query plans upon one or more data resources to determine whether the specific queries or query plans meet various criteria to access the data resources indicated in the queries or query plans, and (c) the alignment of data resources and of data from multiple data resources.

Conferences

13th IEEE International Conference on Advanced and Trusted Computing. Track 3: Privacy Preservation in Big Data. http://atc2016.sciencesconf.org/.

Software Analysis, Automated Theorem Proving, Plan and Argument Analysis

The technology of static program analysis, automated theorem proving, computer algebra systems, formula editors, automated planning and scheduling, plan rationale, argumentation software, argument analysis software, related document authoring and editing software as well as the features and ergonomics of such software are of interest to our group.

Towards software or software plugins that can provide argumentation-related features, broadly, some links are provided including to web-based mathematics and planning domain authoring and editing software.

See Also

Static Program Analysis, Automated Theorem Proving, Computer Algebra System,
Formula Editor, Automated Planning and Scheduling, Planning Domain Definition Language, Argument Map

Hyperlinks

Static Program Analysis
List of Tools for Static Code Analysis

Automated Theorem Proving
List of Theorem Provers and Proof Assistants

Computer Algebra Systems
List of Computer Algebra Systems

Mathematics Document Editing Software
WebLurch, Lurch (Video)

Automated Planning and Scheduling Software
Planning and Scheduling

Planning Domain Document Editing Software
PDDL Studio, myPDDL, Planning.Domains (Editor.Planning.Domains)

Argument Analysis Software
List of Argument Mapping Software, Web-based Collaboration Software

Natural Language Technology and Public Opinion Polling

Web-based opinion polls can be enhanced by natural language processing technology. Uses of natural language technology include processing text-based responses to the questions of opinion polls, surveys or questionnaires, including why people answered one or more previous questions as they did, using natural language to, for instance, explain their reasoning. Uses of forms enhanced with natural language user input capabilities include team scenarios, collaborative software, i.e. business software, as well as public opinion polling.

Websites or apps could make use of forms enhanced with text-based user input elements, forms enhanced by natural language technology. En route to client-side natural language technology, cloud-based technologies could provide such services.

In addition to processing bulk quantities of completed opinion polls, surveys or questionnaires, where multi-document processing could enhance the results of such processing, possible services include determining whether a natural language processing service can parse text-based user input elements’ text contents, in the elements’ contexts, while the user is typing, while the user in on a page, or before they conclude a multipage form.

Dialogue systems technology can provide users with, beyond text-based forms, the convenience of spoken language opinion polls, surveys or questionnaires. Natural language technology can also enhance the design of opinion polls, surveys or questionnaires, processing the text of sequences of or flowcharts of questions.

Hyperlinks

Siri, Google Now, Cortana

Project Oxford (LUIS), IBM Watson, SkyPhrase, Semantria, Wolfram Alpha

SIGdial Bibliography

Lists of dialogue systems by Staffan Larsson
Lists of dialogue systems by Dan Bohus

Workshop on Argument Mining 2014
Workshop on Argument Mining 2015

Frontiers and Connections between Argumentation Theory and Natural Language Processing

The Technology of Meetings, Lectures, Discussion Panels, Dialogues, Argumentation and Debates

The technology of meetings, lectures, discussion panels, dialogues, argumentation and debates are of interest to our group. Some topics in the overlap of artificial intelligence with meetings support technology are discussed, meetings occurring in all organizations, in all sectors, academia, science, industry and government.

Individuals also meet to to do civics, to participate in townhall discussions, to participate in the democracies of their neighborhoods or cities. Accordingly, meetings support technology can enhance Web-based civic engagement. Meetings support technology can empower individuals, organizations and communities, pertaining to the operation of governments and to the transparency of governments, city, state and federal.

The topics presented include the recording of meetings with modern sensors, multiparty speech recognition, obtaining transcripts from meetings, the processing of the data from arrays of sensors, such as pointclouds and 3D audio, into photographs, video, 3D video as well as binaural, surround sound or ambisonic audio. Software technology topics include conveniencing meeting participants as well as production teams with advanced features.

Ten topics are presented:

  1. Obtaining 3D data, pointclouds, from multiple sensors. Obtaining 3D audio from multiple sensors. Obtaining photographs, video, 3D video, binaural audio, surround sound, ambisonics from sensor data.
  2. Natural language understanding, sound source localization, multiperson speech recognition, multiperson nonverbal gesture recognition.
  3. Transcription, topic modeling, keyword generation, enhancing the indexing of video, video segments, video clips.
  4. Modeling meetings, lectures, discussion panels, dialogues, argumentation and debates; detecting events, categorizing events.
  5. Interpreting meetings, interpreting narratives or storyboards from meetings, summarizing meetings, motions of attention during meetings.
  6. The virtual cinematography or videography utilizing virtual cameras; the capability to position virtual cameras in space, to adjust virtual camera settings, to move virtual cameras around to obtain photographs or videos.
  7. The capability to, beyond outputting one video stream, output multiple simultaneous multimedia streams, multiple simultaneous virtual cameras, as per multiview video.
  8. The processing of photographs or video cinematography from meetings; utilizing photographs, videos as well as pointclouds data, machine learning from human photographers, videographers.
  9. The storage of pointcloud video, archiving of the raw preprocessed 3D data; the indexing, search, retrieval of 3D multimedia content.
  10. The summarization of sets of meetings, dashboard summarizations of sets of meetings.

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The Semantics of Multimedia Tracks

Argumentation formats of interest to our group include multimedia, multimedia-based technologies. The semantics of multimedia tracks can enhance numerous use cases. For example, for a MPEG file containing presenter, presentation, e.g. a slideshow, video software, including Web browsers, can provide user interfaces to utilize the multiple tracks of audio, video or data content. Multimedia tracks’ semantics can enhance the portability of features with multimedia files, without requiring the multimedia files to be in the contexts of HTML documents for such features, though such documents could be interoperable with such features through JavaScript.

Metadata standards, extensible ontology, vocabulary and API for multimedia track metadata, such as XMPMPEGMatroska and WebM, can provide enhanced viewing experiences and features. Multimedia tracks described semantically can also be interrelated semantically.

Extensible semantic metadata ontology, vocabularies, including the expressiveness of XMP, MPEG, Matroska and WebM, and JavaScript API can facilitate enhanced features, uses of tracks, of track-based data, and the portability of multimedia objects. Also possible are XML or RDF based multimedia data tracks.

The Styling of Content and Mathematical Notations by Semantics-based CSS Selectors

Semantics enhances the selection and styling of content; varieties of semantic selection include: (1) selecting upon URI items in white space separated lists of TERMorCURIEorAbsIRI values, (2) selecting upon parallel markup structure and reference combinators, and (3) graph-based selections with SPARQL expressiveness.

Selecting upon URI items in white space separated lists of TERMorCURIEorAbsIRI values, such as @xhtml:role, @rdf:type, @rdfa:typeof or @epub:type, could be expressed with a syntax resembling:

An example of selecting upon parallel markup structure, e.g. MathML content markup and parallel markup, and reference combinators:

Ontologydescription logic and semantic reasoning can enhance the functionality of selection based upon URI items in TERMorCURIEorAbsIRI attribute values, selection based upon the parallel markup structure and reference combinators and of graph-based selection. Semantics-based selectors could be as expressive as SPARQL.

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XML/RDF Hybrid Documents

Documents can align XML, tree-based, document content with graph-based semantics, RDF semantics. Documents can interface as both trees and graphs. A solution for document and modular document component semantics is extending document object model interfaces.

Document object model elements such as Element, HTMLElement, HTMLObjectElement as well as custom elements can be extended with a semantics function which serializes the element into a graph. Utilizing the RDFJS API,

Term Element.semantics(Sink sink);

The semantics function produces triples or quads into a provided sink and returns a Term, either a BlankNode or NamedNode, which maps with the document object model element. The default implementation can perform recursion, add semantics (document markup semantics, structural semantics, attributes such as @xhtml:role, @rdf:type, @rdfa:typeof or @epub:type, microformats and RDFa) and then return a mapped Term.

Such a function would entail a convenience: document.body.semantics(sink);

Web components, custom elements, could include a means of specifying such semantics, in addition to structure, styling and scripting, by overriding the semantics function.

Possible for the aforementioned mapping between Terms and Elements

Element document.getElementByTerm(Term term);
Term document.getTermByElement(Element element);

Uses include enhancing the Web-based and desktop-based indexing, search and retrieval of documents and document metadata. Multimedia documents, including with custom elements, can map to graph-based representations utilizing ontologies such as document structural ontologies or to forthcoming digital textbook ontologies.

https://www.w3.org/2003/03/rdf-in-xml.html
https://www.w3.org/TR/rdf-interfaces/
https://www.w3.org/TR/rdf-interfaces/#graphs
https://www.w3.org/TR/rdf-api/
https://www.w3.org/TR/rdfa-syntax/
http://infomesh.net/2002/rdfinhtml/
https://www.w3.org/community/rdfjs/
https://www.w3.org/community/rdfjs/wiki/Comparison_of_RDFJS_libraries
https://github.com/rdfjs/representation-task-force/blob/master/interface-spec.md
https://github.com/linkeddata/rdflib.js

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Argumentation Formats

Our group discusses all argumentation formats, use cases and standardization topics to enhance each existing format as well as potential new formats. Kinds of argumentation of interest to our group include: conversational, mathematical, scientific, interpretive, legal and political.

A list of existing argumentation formats:

Akoma Ntoso
Argument Interchange Format (AIF)
Argument Markup Language (AML)
LegalDocumentXML (LegalDocML)
Legal Knowledge Interchange Format (LKIF)
Mizar
Open Mathematical Documents (OMDoc)
Proof Markup Language (PML)
SALT Rhetorical Ontology (SRO)
Thousands of Problems for Theorem Provers (TPTP)
Thousands of Solutions for Theorem Provers (TSTP)