The final workshop is now available. The formal workshop proceedings are at http://ceur-ws.org/Vol-1024/. The links below are for the preliminary papers presented at the conference.
July 15, 2013
Seattle, Washington, USA
Workshop as part of the 2013 Uncertainty in Artificial Intelligence Conference (http://auai.org/uai2013).
May 17th May 20th (Extended)
May 24th May 27th (Extended)
http://auai.org/uai2013 (Don't forget to register for the workshop in addition to the main conference.)
Over 29 years, the annual Uncertainty in Artificial Intelligence (UAI) conference has explored complex models, many partially or fully Bayesian, which attempt to capture some of the complexities of human reasoning and decision making. As the capacity of modern computing has increased, so has the complexity of the models explored by the UAI community: complexity defined by many variables and many parameters which must be estimated from data or tuned to expert opinion. Implementing these models in practice often requires going beyond the theoretical development of the models: the difficulties arising from the practical application of UAI models has been the constant focus of the Bayesian Applications Workshop.
Due to the past success of the Bayesian Applications Workshop, there will this year for the first time be two workshops with an applications theme. The theme of this applications workshop is spatial, temporal, and network data. One example of such data is the following. We are interested in thinking about the uncertainty in "mobile data," which may come from a GPS-enabled phone or a car. In mobile applications, one important aspect is the uncertainty associated with modeling things in the context of space and time as you are moving. In automotive, for example - you are moving, but with constraints, and these constraints impact your "search space." You are typically only looking for potential destinations in the cone of where you are traveling towards. There is also a time window over which certain goals are relevant and the goodness of a goal changes as you move. The social network of the driver and the passengers could also play a role.
Another example of spatial, temporal, and network data is this: A scientist or an engineer (at ESA, NASA, USGS, or elsewhere) develops a probabilistic model in the form of a Bayesian network or a Markov random field. This is typically a declarative model of the domain - for example of earth fault motion due to earthquakes. A computer scientist or computer engineer is then concerned about how to efficiently compile and execute the model in order to compute posterior distributions or estimate parameters on a multi-core CPU, a GPU, a Hadoop cluster, a supercomputer, or another computer architecture. How well has this model worked in different domains, what are current challenges and opportunities?
The focus this workshop is on models that deal with spatial, temporal, and network data as studied by the UAI community. In particular, we invite papers on the following themes:
· Recommendation, plan recognition, and link prediction under uncertainty
· Role of user interfaces and user interaction, visualization, speech, dialogue management, etc.
· Combining data and expert knowledge in models
· Data fusion: combination of data of different types, including interaction between spatial, temporal, and network data
· Hardware and software platforms for handling complex models and large data sets
· Integration of data collected over time and space while the state of the system may also be changing (for example, a driver's goals may change as the car moves)
· Support for hard or soft real-time response; safety and security
· Integration with techniques from other disciplines, such as aerospace, automotive, biology, computer networking, earth science, ecology, education, electrical engineering, feedback control, medicine, software engineering, ...
· Handling of missing or incomplete data, including models that are not identifiable
· Affective, emotional, context-aware, and considerate user interfaces and user interactions
· Applications in automotive, aerospace, smart phones, mobility, electrical power networks, smart grid, social networks, ecology, medicine, earth sciences, etc.
· System health management, including diagnosis, prognosis, and detection
This list is meant to be suggestive and not exhaustive; other papers with an application focus are welcome. Also welcome are papers which represent work in progress, explore a practical problem or issue without a final resolution, or pose challenging problems related to uncertainty for spatial, temporal, or network data. Workshop papers will be selected with the goal of stimulating discussion of critical issues within the community of practice.
Note that the workshop is not restricted to a particular vertical market or discipline. Instead, the workshop seeks to cross-fertilize and inspire across disciplines, with a focus on how issues related to modeling of spatial, temporal, and network data is handled.
Submissions will be peer reviewed and papers will be published online. Authors who wish to withhold their paper from publication (either because it contains references to proprietary data, or because they wish to publish it later at a different venue) can request that only the abstract be published.
Submissions will be peer reviewed and papers will be published online. Authors who wish to withhold their paper from publication (either because it contains references to proprietary data, or because they wish to publish it later at a different venue) can request that only the abstract be published. Papers should follow the general UAI conference guidelines as to format and length, although these will be more loosely enforced.
Abstracts should be submitted by May 17, 2013, with full papers
due on May 24, 2013. Abstracts should be submitted by May 20,
2013, with full papers due on May 27, 2013. Author notifications are expected around June
7, 2013. For questions contact the chair at email@example.com.
There are several collocated workshops. Papers may only be submitted to and presented at one of them. If the program committee feels that a paper would be a better fit in a different UAI-13 workshop, they may pass the paper along to the other workshop's committee.
This workshop is offered as part of the 29th Uncertainty in Artificial Intelligence conference (UAI 2013), July 11-15th in Seattle, WA. It will be collocated with other UAI workshops on the last day of the conference, July 15th. Registration is handled through the main conference web site, which also gives information about lodging and travel. One registration fee covers all of the workshops: people who register for this workshop may split their time between all the workshops.
· Chair: Ole J. Mengshoel, CMU
· Dennis Buede, Innovative Decisions
· Asela Gunawardana, Microsoft
· Jennifer Healey, Intel
· Oscar Kipersztok, Boeing
· Branislav Kveton, Technicolor
· Helge Langseth, NTNU
· Tomas Singliar, Boeing
· Enrique Sucar, INAOE
· Tom Walsh, MIT