LiU Electronic Press
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Author:
Haslum, Patrik (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Title:
Prediction as a Knowledge Representation Problem: A Case Study in Model Design
Department:
Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab
Linköping University, The Institute of Technology
Publication type:
Licentiate thesis, monograph (Other academic)
Language:
English
Publisher: Institutionen för datavetenskap
Pages:
106
Series:
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971; 942
Year of publ.:
2002
URI:
urn:nbn:se:liu:diva-5724
Permanent link:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5724
ISBN:
91-7373-331-8
Subject category:
Computer Science
SVEP category:
Computer science
Keywords(en) :
WITAS, Unmanned irial Vehicle (UAV), helicopter, raffic surveillance, remote photogrammetry
Abstract(en) :

The WITAS project aims to develop technologies to enable an Unmanned Airial Vehicle (UAV) to operate autonomously and intelligently, in applications such as traffic surveillance and remote photogrammetry. Many of the necessary control and reasoning tasks, e.g. state estimation, reidentification, planning and diagnosis, involve prediction as an important component. Prediction relies on models, and such models can take a variety of forms. Model design involves many choices with many alternatives for each choice, and each alternative carries advantages and disadvantages that may be far from obvious. In spite of this, and of the important role of prediction in so many areas, the problem of predictive model design is rarely studied on its own.

In this thesis, we examine a range of applications involving prediction and try to extract a set of choices and alternatives for model design. As a case study, we then develop, evaluate and compare two different model designs for a specific prediction problem encountered in the WITAS UAV project. The problem is to predict the movements of a vehicle travelling in a traffic network. The main difficulty is that uncertainty in predictions is very high, du to two factors: predictions have to be made on a relatively large time scale, and we have very little information about the specific vehicle in question. To counter uncertainty, as much use as possible must be made of knowledge about traffic in general, which puts emphasis on the knowledge representation aspect of the predictive model design.

The two mode design we develop differ mainly in how they represent uncertainty: the first uses coarse, schema-based representation of likelihood, while the second, a Markov model, uses probability. Preliminary experiments indicate that the second design has better computational properties, but also some drawbacks: model construction is data intensive and the resulting models are somewhat opaque.

Note:
Report code: LiU-Tek-Lic-2002:15.
Presentation:
2002-04-29, 00:00 (English)
Supervisor:
Doherty, Patrick (Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab) (Linköping University, The Institute of Technology)
Available from:
2002-11-20
Created:
2002-11-20
Last updated:
2009-05-18
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