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.

---

Survey Papers Published in ACM TIST

---

Featured Articles

Planning solar array operations on the international space station (published in Vol.2, No.4)
Sudhakar Y. Reddy⁽¹⁾, Jeremy D. Frank⁽²⁾, Michael J. Iatauro⁽²⁾, Matthew E. Boyce⁽²⁾, Elif Kurklu⁽²⁾, Mitchell Ai-Chang⁽²⁾, Ari K. Jonsson⁽³⁾
(1) The Boeing Company, Huntington Beach, CA
(2) NASA Ames Research Center
(3) Reykjavik University

Flight controllers manage the orientation and modes of eight large solar arrays that power the International Space Station (ISS). The task requires generating plans that balance complex constraints and preferences. These considerations include context-dependent constraints on viable solar array configurations, temporal limits on transitions between configurations, and preferences on which considerations have priority. The Solar Array Constraint Engine (SACE) treats this operations planning problem as a sequence of tractable constrained optimization problems. SACE uses constraint management and automated planning capabilities to reason about the constraints, to find optimal array configurations subject to these constraints and solution preferences, and to automatically generate solar array operations plans. SACE further provides flight controllers with real-time situational awareness and what-if analysis capabilities. SACE is built on the Extensible Universal Remote Operations Planning Architecture (EUROPA) model-based planning system. EUROPA facilitated SACE development by providing model-based planning, built-in constraint reasoning capability, and extensibility. This article formulates the planning problem, explains how EUROPA solves the problem, and provides performance statistics from several planning scenarios. SACE reduces a highly manual process that takes weeks to an automated process that takes tens of minutes. (Read more)

Spatiotemporal correlations in criminal offense records (published in Vol.2, No.4)
Jameson L. Toole⁽¹⁾, Nathan Eagle⁽²⁾, Joshua B. Plotkin⁽³⁾
(1) Massachusetts Institute of Technology, Cambridge, MA
(2) The Santa Fe Institute, Santa Fe, NM
(3) The University of Pennsylvania, Philadelphia, PA

With the increased availability of rich behavioral datasets, we present a novel application of tools to analyze this information. Using criminal offense records as an example, we employ cross-correlation measures, eigenvalue spectrum analysis, and results from random matrix theory to identify spatiotemporal patterns on multiple scales. With these techniques, we show that most significant correlation exists on the time scale of weeks and identify clusters of neighborhoods whose crime rates are affected simultaneously by external forces. (Read more)

Nowcasting Events from the Social Web with Statistical Learning (To appear in ACM TIST)
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.

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
Social Web Mining	February 1, 2012	PDF Word

* The dates and times above are in EST (Eastern Standard Time)

Last change: January 26, 2011

Association for
Computing Machinery