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Aepia es una asociación formada por docentes, investigadores y profesionales que comparten intereses en temas relacionados con la Inteligencia Artificial.

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La Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA) es un foro bienal abierto a investigadores de todo el mundo para presentar y discutir los últimos avances científicos y tecnológicos en Inteligencia Artificial. Como en las anteriores ediciones, los objetivos son: facilitar la diseminación de nuevas ideas y experiencias, fortalecer los lazos entre los distintos grupos de investigación implicados, promover el trasvase de conocimiento entre nuevos investigadores y grupos consolidados, y ayudar a difundir los nuevos desarrollos hacia la sociedad.

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11th International Conference on Scalable Uncertainty Management (SUM)

October 4-6, 2017 - Granada, Spain

http://idbis.ugr.es/sum2017/

The 11th International Conference on Scalable Uncertainty Management (SUM) will be held in Granada, Spain on October 4-6, 2017. The conference will bring together researchers who are working with imperfect information in fields such as artificial intelligence, databases, data mining, information retrieval, and risk analysis with
the aim of fostering collaboration and cross-fertilization of ideas from different communities.

An originality of SUM is that it gives a large space to tutorials about a large range of topics related to uncertainty management. Each tutorial provides a 45 minute survey of one of the research areas in the scope of the conference.

Topics of Interest:

We solicit papers on the management of large amounts or complex kinds of uncertain, incomplete, or inconsistent information. We are particularly interested in papers that focus on bridging gaps, for instance between different communities, between numerical and symbolic approaches, or between theory and practice. Topics of interest include (but are not limited to):
- Imperfect information in databases
- Methods for modeling, indexing, and querying uncertain databases
- Top-k queries, skyline query processing, and ranking
- Approximate, fuzzy query processing
- Uncertainty in data integration and exchange
- Uncertainty and imprecision in geographic information systems
- Probabilistic databases and possibilistic databases?
- Data provenance and trust
- Data summarization
- Very large datasets
- Imperfect information in information retrieval and semantic web applications
- Approximate schema and ontology matching
- Uncertainty in description logics and logic programming
- Learning to rank, personalization, and user preferences
- Probabilistic language models
- Combining vector-space models with symbolic representations
- Inductive reasoning for the semantic web
- Imperfect information in artificial intelligence
- Statistical relational learning, graphical models, probabilistic inference
- Argumentation, defeasible reasoning, belief revision
- Weighted logics for managing uncertainty
- Reasoning with imprecise probability, Dempster-Shafer theory, possibility theory
- Approximate reasoning, similarity-based reasoning, analogical reasoning
- Planning under uncertainty, reasoning about actions, spatial and temporal reasoning
- Incomplete preference specifications
- Learning from data
- Risk analysis
- Aleatory vs. epistemic uncertainty
- Uncertainty elicitation methods
- Uncertainty propagation methods
- Decision analysis methods
- Tools for synthesizing results

Accepted long and short papers will be published by Springer in the Lecture Notes in Artificial Intelligence series. Authors of an accepted long or short paper will be expected to sign copyright release forms, and one author is expected to give a presentation at the conference. Authors of accepted abstracts will be expected to present their work
during the conference, but the extended abstracts will not be published in the LNCS/LNAI proceedings (they will be made available in a separate booklet).

IMPORTANT DATES:
- Deadline for submissions: April 3, 2017
- Notification: May 22, 2017
- Camera-ready copies: June 12, 2017
- Conference: October 4-6, 2017

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