|
Showing 1 - 3 of
3 matches in All Departments
Generating Abstraction Hierarchies presents a completely automated
approach to generating abstractions for problem solving. The
abstractions are generated using a tractable, domain-independent
algorithm whose only inputs are the definition of a problem space
and the problem to be solved and whose output is an abstraction
hierarchy that is tailored to the particular problem. The algorithm
generates abstraction hierarchies that satisfy the `ordered
monotonicity' property, which guarantees that the structure of an
abstract solution is not changed in the process of refining it. An
abstraction hierarchy with this property allows a problem to be
decomposed such that the solution in an abstract space can be held
invariant while the remaining parts of a problem are solved. The
algorithm for generating abstractions is implemented in a system
called ALPINE, which generates abstractions for a hierarchical
version of the PRODIGY problem solver. Generating Abstraction
Hierarchies formally defines this hierarchical problem solving
method, shows that under certain assumptions this method can reduce
the size of a search space from exponential to linear in the
solution size, and describes the implementation of this method in
PRODIGY. The abstractions generated by ALPINE are tested in
multiple domains on large problem sets and are shown to produce
shorter solutions with significantly less search than problem
solving without using abstraction. Generating Abstraction
Hierarchies will be of interest to researchers in machine learning,
planning and problem reformation.
Generating Abstraction Hierarchies presents a completely automated
approach to generating abstractions for problem solving. The
abstractions are generated using a tractable, domain-independent
algorithm whose only inputs are the definition of a problem space
and the problem to be solved and whose output is an abstraction
hierarchy that is tailored to the particular problem. The algorithm
generates abstraction hierarchies that satisfy the `ordered
monotonicity' property, which guarantees that the structure of an
abstract solution is not changed in the process of refining it. An
abstraction hierarchy with this property allows a problem to be
decomposed such that the solution in an abstract space can be held
invariant while the remaining parts of a problem are solved. The
algorithm for generating abstractions is implemented in a system
called ALPINE, which generates abstractions for a hierarchical
version of the PRODIGY problem solver. Generating Abstraction
Hierarchies formally defines this hierarchical problem solving
method, shows that under certain assumptions this method can reduce
the size of a search space from exponential to linear in the
solution size, and describes the implementation of this method in
PRODIGY. The abstractions generated by ALPINE are tested in
multiple domains on large problem sets and are shown to produce
shorter solutions with significantly less search than problem
solving without using abstraction. Generating Abstraction
Hierarchies will be of interest to researchers in machine learning,
planning and problem reformation.
This book illustrates the first connection between the map user
community and the developers of digital map processing technologies
by providing several applications, challenges, and best practices
in working with historical maps. After the introduction chapter, in
this book, Chapter 2 presents a variety of existing applications of
historical maps to demonstrate varying needs for processing
historical maps in scientific studies (e.g., thousands of
historical maps from a map series vs. a few historical maps from
various publishers and with different cartographic styles). Chapter
2 also describes case studies introducing typical types of
semi-automatic and automatic digital map processing technologies.
The case studies showcase the strengths and weaknesses of
semi-automatic and automatic approaches by testing them in a symbol
recognition task on the same scanned map. Chapter 3 presents the
technical challenges and trends in building a map processing,
modeling, linking, and publishing framework. The framework will
enable querying historical map collections as a unified and
structured spatiotemporal source in which individual geographic
phenomena (extracted from maps) are modeled (described) with
semantic descriptions and linked to other data sources (e.g.,
DBpedia, a structured version of Wikipedia). Chapter 4 dives into
the recent advancement in deep learning technologies and their
applications on digital map processing. The chapter reviews
existing deep learning models for their capabilities on geographic
feature extraction from historical maps and compares different
types of training strategies. A comprehensive experiment is
described to compare different models and their performance.
Historical maps are fascinating to look at and contain valuable
retrospective place information difficult to find elsewhere.
However, the full potential of historical maps has not been
realized because the users of scanned historical maps and the
developers of digital map processing technologies are from a wide
range of disciplines and often work in silos. Each chapter in this
book can be read individually, but the order of chapters in this
book helps the reader to first understand the "product
requirements" of a successful digital map processing system, then
review the existing challenges and technologies, and finally follow
the more recent trend of deep learning applications for processing
historical maps. The primary audience for this book includes
scientists and researchers whose work requires long-term historical
geographic data as well as librarians. The secondary audience
includes anyone who loves maps!
|
|