Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology (ACM TIST) is a
new scholarly journal that publishes the highest quality papers on intelligent
systems, applicable algorithms and technology with a multi-disciplinary
perspective. An intelligent system is one that uses artificial intelligence (AI)
techniques to offer important services (e.g., as a component of a larger system)
to allow integrated systems to perceive, reason, learn, and act intelligently in
the real world.
ACM TIST is published quarterly (four issues a year). Each issue has 5-8 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers. Published articles can be accessed through the
ACM Digital
Library. To facilitate open access, meta data on the published papers can be freely accessed. Authors can
post their accepted manuscripts and supplementary material online for others to download.
SCI Expanded Index and EI Index (ISSN:2157-6904)
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work: ACM introduces a new publishing license agreement, an updated
copyright transfer agreement, and a new author-pays option which allows
for perpetual open access through the ACM Digital Library. For more
information, visit the ACM Author Rights webpage at http://authors.acm.org.
Survey Papers Published in ACM TIST
Featured Articles
Research directions in agent communication
(published in Vol.4, No.2)
Amit K. Chopra(1), Alexander Artikis(2), Jamal Bentahar(3), Marco Colombetti(4), Frank Dignum(5), Nicoletta Fornara(6), Andrew J. I. Jones(7), Munindar P. Singh(8), Pinar Yolum(9)
(1) University of Trento, Italy
(2) NCSR "Demokritos", Greece
(3) Concordia University, OR
(4) University of Lugano, Politecnico di Milano, Italy
(5) Utrecht University, The Netherlands
(6) University of Lugano, Switzerland
(7) King's College London, UK
(8) North Carolina State University, NC
(9) Bogazici University, Turkey
Increasingly, software engineering involves open systems consisting of autonomous and heterogeneous participants or agents who carry out loosely coupled interactions. Accordingly, understanding and specifying communications among agents is a key concern. A focus on ways to formalize meaning distinguishes agent communication from traditional distributed computing: meaning provides a basis for flexible interactions and compliance checking.
Over the years, a number of approaches have emerged with some essential and some irrelevant distinctions drawn among them. As agent abstractions gain increasing traction in the software engineering of open systems, it is important to resolve the irrelevant and highlight the essential distinctions, so that future research can be focused in the most productive directions.
This article is an outcome of extensive discussions among agent communication researchers, aimed at taking stock of the field and at developing, criticizing, and refining their positions on specific approaches and future challenges. This article serves some important purposes, including identifying (1) points of broad consensus; (2) points where substantive differences remain; and (3) interesting directions of future work.
(Read more)
Stereotypical trust and bias in dynamic multiagent systems
(published in Vol.4, No.2)
Chris Burnett(1), Timothy J. Norman(1), Katia Sycara(2)
(1) University of Aberdeen, UK
(2) Carnegie Mellon University, Pittsburgh, PA
Large-scale multiagent systems have the potential to be highly dynamic. Trust and reputation are crucial concepts in these environments, as it may be necessary for agents to rely on their peers to perform as expected, and learn to avoid untrustworthy partners. However, aspects of highly dynamic systems introduce issues which make the formation of trust relationships difficult. For example, they may be short-lived, precluding agents from gaining the necessary experiences to make an accurate trust evaluation. This article describes a new approach, inspired by theories of human organizational behavior, whereby agents generalize their experiences with previously encountered partners as stereotypes, based on the observable features of those partners and their behaviors. Subsequently, these stereotypes are applied when evaluating new and unknown partners. Furthermore, these stereotypical opinions can be communicated within the society, resulting in the notion of stereotypical reputation. We show how this approach can complement existing state-of-the-art trust models, and enhance the confidence in the evaluations that can be made about trustees when direct and reputational information is lacking or limited. Furthermore, we show how a stereotyping approach can help agents detect unwanted biases in the reputational opinions they receive from others in the society. (Read more)
Could Social Media Be Used to Detect Disease Outbreaks? (report by Science Daily)
Nowcasting Events from the Social Web with Statistical Learning
(published in Vol.3, No.4)
Vasileios Lampos(1), Nello Cristianini(1)
(1) University of Bristol, UK
We present a general methodology for inferring the occurrence and magnitude of an event or phenomenon
by exploring the rich amount of unstructured textual information on the social part of the web. Having
geo-tagged user posts on the microblogging service of Twitter as our input data, we investigate two case
studies. The first consists of a benchmark problem, where actual levels of rainfall in a given location and
time are inferred from the content of tweets. The second one is a real-life task, where we infer regional
Influenza-like Illness rates in the effort of detecting timely an emerging epidemic disease. Our analysis
builds on a statistical learning framework, which performs sparse learning via the bootstrapped version of
LASSO to select a consistent subset of textual features from a large amount of candidates. In both case
studies, selected features indicate close semantic correlation with the target topics and inference, conducted
by regression, has a significant performance, especially given the short length - approximately one year– of
Twitter's data time series. (Read more)
PTIME: Personalized assistance for calendaring
(published in Vol.2, No.4)
Pauline M. Berry(1), Melinda Gervasio(1), Bart Peintner(1), Neil Yorke-Smith(1,2)
(1) SRI International, Menlo Park, CA
(2) American University of Beirut, Beirut, Lebanon
In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME (Personalized Time Management) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively learn scheduling preferences, adapting to its user over time. The agent allows its user to flexibly express requirements for new meetings, as they would to an assistant. It interfaces with commercial enterprise calendaring platforms, and it operates seamlessly with users who do not have PTIME. This article overviews the system design and describes the models and technical advances required to satisfy the competing needs of preference modeling and elicitation, constraint reasoning, and machine learning. We further report on a multifaceted evaluation of the perceived usefulness of the system. (Read more)
Call For Papers Manuscript Central
| Upcoming Special Issues | Submission Deadline | CFP |
| Visual Understanding with RGB-D Sensors | June 30, 2013 | PDF HTML |
| Intelligent Healthcare Informatics | September 30, 2013 | PDF HTML |
| Recommender System Benchmarking | December 15, 2013 | PDF HTML |
| Participatory Sensing and Crowd Intelligence | December 31, 2013 | PDF HTML |
Last change: May 18, 2013
