UZH-Logo

Maintenance Infos

Proceedings of the 3rd Planning to Learn Workshop (WS9) at ECAI 2010


Proceedings of the 3rd Planning to Learn Workshop (WS9) at ECAI 2010. Edited by: Kietz, J U; Bernstein, A; Brazdil, P (2010). Lisbon, Portugal: Dynamic and Distributed Information Systems Group.

Abstract

The task of constructing composite systems, that is systems composed of more than one part, can be seen as interdisciplinary area which builds on expertise in different domains. The aim of this workshop is to explore the possibilities of constructing such systems with the aid of Machine Learning and exploiting the know-how of Data Mining. One way of producing composite systems is by inducing the constituents and then by putting the individual parts together. For instance, a text extraction system may be composed of various subsystems, some oriented towards tagging, morphosyntactic analysis or word sense disambigua- tion. This may be followed by selection of informative attributes and finally generation of the system for the extraction of the relevant information. Machine Learning tech- niques may be employed in various stages of this process. The problem of constructing com- plex systems can thus be seen as a problem of planning to resolve multiple (possibly interacting) tasks. So, one important issue that needs to be addressed is how these multiple learning pro- cesses can be coordinated. Each task is resolved using certain ordering of operations. Meta-learning can be useful in this process. It can help us to retrieve previous solutions conceived in the past and re-use them in new settings. The aim of the workshop is to explore the possibilities of this new area, offer a forum for exchanging ideas and experience concerning the state-of-the art, permit to bring in knowledge gathered in different but related and relevant areas and outline new direc- tions for research. It is expected that the workshop will help to create a sub-community of ML / DM researchers interested to explore these new venues to ML / DM problems and help thus to advance the research and potential for new type of ML / DM systems.

The task of constructing composite systems, that is systems composed of more than one part, can be seen as interdisciplinary area which builds on expertise in different domains. The aim of this workshop is to explore the possibilities of constructing such systems with the aid of Machine Learning and exploiting the know-how of Data Mining. One way of producing composite systems is by inducing the constituents and then by putting the individual parts together. For instance, a text extraction system may be composed of various subsystems, some oriented towards tagging, morphosyntactic analysis or word sense disambigua- tion. This may be followed by selection of informative attributes and finally generation of the system for the extraction of the relevant information. Machine Learning tech- niques may be employed in various stages of this process. The problem of constructing com- plex systems can thus be seen as a problem of planning to resolve multiple (possibly interacting) tasks. So, one important issue that needs to be addressed is how these multiple learning pro- cesses can be coordinated. Each task is resolved using certain ordering of operations. Meta-learning can be useful in this process. It can help us to retrieve previous solutions conceived in the past and re-use them in new settings. The aim of the workshop is to explore the possibilities of this new area, offer a forum for exchanging ideas and experience concerning the state-of-the art, permit to bring in knowledge gathered in different but related and relevant areas and outline new direc- tions for research. It is expected that the workshop will help to create a sub-community of ML / DM researchers interested to explore these new venues to ML / DM problems and help thus to advance the research and potential for new type of ML / DM systems.

Downloads

548 downloads since deposited on 10 Feb 2011
51 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Edited Scientific Work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Date:17 August 2010
Deposited On:10 Feb 2011 08:31
Last Modified:05 Apr 2016 14:44
Publisher:Dynamic and Distributed Information Systems Group
Number of Pages:87
Additional Information:The 19th European conference on artificial intelligence (ECAI 2010)
Free access at:Official URL. An embargo period may apply.
Official URL:http://www.ifi.uzh.ch/ddis/fileadmin/pdf/kietz/Proc-3rd-PlanLearn-ECAI-ws09.pdf
Other Identification Number:1423
Permanent URL: https://doi.org/10.5167/uzh-44846

Download

[img]
Preview
Content: Published Version
Filetype: PDF
Size: 6MB

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations