The 15th International Conference on AI and Law (ICAIL 2015) will be held in San Diego, California, USA, June 8-12, 2015, at the University of San Diego, at the Kroc Institute, under the auspices of the International Association for Artificial Intelligence and Law (IAAIL), an organization devoted to promoting research and development in the field of AI and law with members throughout the world. The conference is held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and with ACM SIGAI (the Special Interest Group on Artificial Intelligence of the Association for Computing Machinery).
Thimm, Matthias (Universität Koblenz-Landau) | Villata, Serena (Laboratoire d'Informatique, Signaux et Systèmes de Sophia-Antipolis (I3S)) | Cerutti, Federico (Cardiff University) | Oren, Nir (University of Aberdeen) | Strass, Hannes (Leipzig University) | Vallati, Mauro (University of Huddersfield)
We review the First International Competition on Computational Models of Argumentation (ICMMA’15). The competition evaluated submitted solvers performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.
The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages and standards for rule technologies. RuleML is a leading conference to build bridges between academe and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rule engines/business rule management systems; semantic web rule languages and rule standards; rule-based event processing languages (EPLs) and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules. The 9th International Web Rule Symposium (RuleML 2015) was held in Berlin, Germany, August 2-5. This report summarizes the events of that conference.
Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.
Trampuš, Mitja (Jozef Stefan Institute) | Fuart, Flavio (Jozef Stefan Institute) | Pighin, Daniele (Google Inc.) | Štajner, Tadej (Jozef Stefan Institute) | Berčič, Jan (Jozef Stefan Institute) | Novak, Blaz (Jozef Stefan Institute) | Rusu, Delia (Jozef Stefan Institute) | Stopar, Luka (Jozef Stefan Institute) | Grobelnik, Marko (Jozef Stefan Institute)
For most events of at least moderate significance, there are likely tens, often hundreds or thousands of online articles reporting on it, each from a slightly different perspective. If we want to understand an event in depth, from multiple perspectives, we need to aggregate multiple sources and understand the relations between them. However, current news aggregators do not offer this kind of functionality. As a step towards a solution, we propose DiversiNews, a real-time news aggregation and exploration platfom whose main feature is a novel set of controls that allow users to contrast reports of a selected event based on topical emphases, sentiment differences and/or publisher geolocation. News events are presented in the form of a ranked list of articles pertaining to the event and an automatically generated summary. Both the ranking and the summary are interactive and respond in real time to user’s change of controls. We validated the concept and the user interface through user tests with positive results.
Active problem solving has been shown to be one of the most effective ways to acquire complex skills. Whether one is learning a programming language by implementing a computer program, or learning calculus by solving problems, context sensitive feedback and guidance are crucial to keeping problem solving efforts fruitful and efficient. This article reviews AI-based algorithms that can diagnose student difficulties during active problem solving and serve as the basis for providing context-sensitive and individualized guidance. The article also describes the crucial role sensor based estimates of cognitive resources such as working memory capacity and attention can play in enhancing the diagnostic capabilities of intelligent instructional systems.
Ford, Kenneth M. (Florida Institute for Human and Machine Cognition (IHMC)) | Hayes, Patrick J. (Florida Institute for Human and Machine Cognition (IHMC)) | Glymour, Clark (Florida Institute for Human and Machine Cognition (IHMC)) | Allen, James (Florida Institute for Human and Machine Cognition (IHMC))
This introduction focuses on how human-centered computing (HCC) is changing the way that people think about information technology. The AI perspective views this HCC framework as embodying a systems view, in which human thought and action are linked and equally important in terms of analysis, design, and evaluation. This emerging technology provides a new research outlook for AI applications, with new research goals and agendas.
This article is aimed at helping AI researchers and practitioners imagine roles intelligent technologies might play in the many different and varied ecosystems in which people learn. My observations are based on learning sciences research of the past several decades, the possibilities of new technologies of the past few years, and my experience as program officer for the National Science Foundation’s Cyberlearning and Future Learning Technologies program. My thesis is that new technologies have potential to transform possibilities for fostering learning in both formal and informal learning environments by making it possible and manageable for learners to engage in the kinds of project work that professionals engage in and learn important content, skills, practices, habits, and dispositions from those experiences. The expertise of AI researchers and practitioners is critical to that vision, but it will require teaming up with others — for example, technology imagineers, educators, and learning scientists.