Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T17:23:30.507Z Has data issue: false hasContentIssue false
  • English
  • Français

Introduction to the TPLP Special Issue on User-oriented Logic Programming and Reasoning Paradigms

Published online by Cambridge University Press:  15 February 2019

STEFAN ELLMAUTHALER  and
CLAUDIA SCHULZ
STEFAN ELLMAUTHALER
Affiliation:
Leipzig University, Leipzig, Germany (e-mail: [email protected])
CLAUDIA SCHULZ
Affiliation:
Ubiquitous Knowledge Processing (UKP) Lab, TU Darmstadt, Darmstadt, Germany (e-mail: [email protected])
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

With the rise of machine learning, and more recently the overwhelming interest in deep learning, knowledge representation and reasoning (KRR) approaches struggle to maintain their position within the wider Artificial Intelligence (AI) community. Often considered as part of the good old-fashioned AI (Haugeland 1985) – like a memory of glorious old days that have come to an end – many consider KRR as no longer applicable (on its own) to the problems faced by AI today (Blackwell 2015; Garnelo et al. 2016). What they see are logical languages with symbols incomprehensible by most, inference mechanisms that even experts have difficulties tracing and debugging, and the incapability to process unstructured data like text.

Type
Editorial
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 2: User-Oriented Logic Programming and Reasoning Paradigms , March 2019 , pp. 109 - 113
Copyright
Copyright © Cambridge University Press 2019 

References

Alkhalifa, E. M. 2006. Effects of learner misconceptions on learning. In Proceedings of the IADIS International Conference Cognition and Exploratory Learning in Digital Age, International Association for Development of the Information Society (IADIS), 123–128.Google Scholar
Alviano, M., Dodaro, C., Leone, N. and Ricca, F. 2015. Advances in WASP. In Proceedings of the 13th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’15), Springer International Publishing, 40–54.Google Scholar
Baral, C. and Giacomo, G. D. 2015. Knowledge representation and reasoning: What’s hot. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press, 4316–4317.Google Scholar
Blackwell, A. F. 2015. Interacting with an inferred world: The challenge of machine learning for humane computer interaction. In Proceedings of the Fifth Decennial Aarhus Conference on Critical Alternatives (CA’15), 169–180.Google Scholar
Busoniu, P.-A., Oetsch, J., Pührer, J., Skocovsky, P. and Tompits, H. 2013. SeaLion: An eclipse-based IDE for answer-set programming with advanced debugging support. Theory and Practice of Logic Programming 13, 4–5, 657–673.CrossRefGoogle Scholar
Cabalar, P., Fandinno, J. and Fink, M. 2014. Causal graph justifications of logic programs. Theory and Practice of Logic Programming 14, 4–5, 603–618.CrossRefGoogle Scholar
Committee on Undergraduate Science Education 1997. Science Teaching Reconsidered: A Handbook. National Academy Press, Washington, DC.Google Scholar
Dodaro, C., Gasteiger, P., Reale, K., Ricca, F. and Schekothihin, K. 2018. Debugging non-ground ASP programs: Technique and graphical tool. Theory and Practice of Logic Programming (Special Issue on User-Oriented Logic Programming and Reasoning Paradigms).Google Scholar
Fandinno, J. and Schulz, C. 2018. Answering the “why” in answer set programming - A survey of explanation approaches. Theory and Practice of Logic Programming (Special Issue on User-Oriented Logic Programming and Reasoning Paradigms).Google Scholar
Febbraro, O., Reale, K. and Ricca, F. 2010. A visual interface for drawing ASP programs. In Proceedings of the 25th Italian Conference on Computational Logic (CILC’10), CEUR-WS.org.Google Scholar
Febbraro, O., Reale, K. and Ricca, F. 2011. ASPIDE: Integrated development environment for answer set programming. In Proceedings of the 11th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR’11), Springer-Verlag Berlin Heidelberg, 317–330.Google Scholar
Garnelo, M., Arulkumaran, K. and Shanahan, M. 2016. Towards deep symbolic reinforcement learning. CoRR abs/1609.05518.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Lühne, P., Obermeier, P., Ostrowski, M., Romero, J., Schaub, T., Schellhorn, S. and Wanko, P. 2018. The potsdam answer set solving collection 5.0. KI - Künstliche Intelligenz 32, 2, 181–182.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2012. Answer Set Solving in Practice. Morgan & Claypool Publishers.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2014. Clingo = ASP + control: Preliminary report. CoRR abs/1405.3694.Google Scholar
Haugeland, J. 1985. Artificial Intelligence: The Very Idea. Massachusetts Institute of Technology, Cambridge, MA, USA.Google Scholar
Kloimüllner, C., Oetsch, J., Pührer, J. and Tompits, H. 2011. Kara: A system for visualising and visual editing of interpretations for answer-set programs. In Revised Selected Papers of the 19th International Conference on Applications of Declarative Programming and Knowledge Management (INAP’11) and the 25th Workshop on Logic Programming (WLP’11), 325–344.Google Scholar
Marcopoulos, E. and Zhang, Y. 2018. onlineSPARC: A programming environment for answer set programming. Theory and Practice of Logic Programming (Special Issue on User-Oriented Logic Programming and Reasoning Paradigms).Google Scholar
Oetsch, J., Pührer, J. and Tompits, H. 2018. Stepwise debugging of answer-set programs. Theory and Practice of Logic Programming 18, 1, 30–80.CrossRefGoogle Scholar
Riguzzi, F. and Cota, G. 2016. Probabilistic logic programming tutorial. The Association for Logic Programming Newsletter 29, 1, 1–1.Google Scholar
Rodosthenous, C. T. and Michael, L. 2018. Web-star: A visual web-based IDE for a story comprehension system. Theory and Practice of Logic Programming (Special Issue on User-Oriented Logic Programming and Reasoning Paradigms).Google Scholar
Schulz, C. and Toni, F. 2016. Justifying answer sets using argumentation. Theory and Practice of Logic Programming 16, 01, 59–110.CrossRefGoogle Scholar
Shchekotykhin, K. M. 2015. Interactive query-based debugging of ASP programs. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15), AAAI Press, 1597–1603.Google Scholar
Shoham, Y. 2015. Why knowledge representation matters. Communications of the ACM 59, 1, 47–49.CrossRefGoogle Scholar
Vosinakis, S., Anastassakis, G. and Koutsabasis, P. 2016. Teaching and learning logic programming in virtual worlds using interactive microworld representations. British Journal of Educational Technology 49, 1, 30–44.CrossRefGoogle Scholar
Wielemaker, J., Riguzzi, F., Kowalski, B., Lager, T., Sadri, F. and Calejo, M. 2018. Using swish to realise interactive web based tutorials for logic based languages. Theory and Practice of Logic Programming (Special Issue on User-Oriented Logic Programming and Reasoning Paradigms).Google Scholar
Yuen, T. T., Reyes, M. and Zhang, Y. 2018. Introducing computer science to high school students through logic programming. Theory and Practice of Logic Programming (Special Issue on User-Oriented Logic Programming and Reasoning Paradigms).Google Scholar