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.
Baarslag, Tim (University of Southampton) | Aydoğan, Reyhan (Delft University of Technology) | Hindriks, Koen V. (Delft University of Technology) | Fujita, Katsuhide (Tokyo University of Agriculture and Technology) | Ito, Takayuki (Nagoya Institute of Technology) | Jonker, Catholijn M. (Delft University of Technology)
The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.
Russell, Stuart (University of California, Berkeley) | Dietterich, Tom (Oregon State University) | Horvitz, Eric (Microsoft) | Selman, Bart (Cornell University) | Rossi, Francesca (University of Padova) | Hassabis, Demis (DeepMind) | Legg, Shane (DeepMind) | Suleyman, Mustafa (DeepMind) | George, Dileep (Vicarious) | Phoenix, Scott (Vicarious)
Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents — systems that perceive and act in some environment. In this context, "intelligence" is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document [see page X] gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law and philosophy to computer security, formal methods and, of course, various branches of AI itself. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.
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.
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.