What is SPACENET?
SPACENET is an EU funded Human Capital Mobility Network that brings
together the eleven major European spatial reasoning groups.
Participating laboratories are sited in eight European countries,
including both EU and EFTA states. The multi-disciplinary nature of
spatial reasoning is reflected in the inclusion of Computer Science,
Artificial Intelligence, Cognitive Science, Linguistics and Geographic
Information System groups.
What are the objectives of SPACENET?
- Integrate existing European research on qualitative spatial reasoning.
- Provide a framework for bilateral and multilateral communication
between researchers thus supporting European research in this area.
- Provide mechanisms for training and enhancing the skills of
workers in this field of research.
- Provide means of dissemination of research from within the
network both internally and externally to the network.
How will these objectives be met?
- A series of SPACENET workshops will bring together scientists from
all partner sites, together with a number of researchers outside the network, on a regular basis.
- A program of bilateral exchange visits between partners will seek to
foster co-operative research projects.
- Workshop reports and an electronic news network will enable partners to
share research information.
- The network is actively engaged in producing a comprehensive textbook
that will cover all aspects of qualitative spatial reasoning.
What is qualitative spatial reasoning?
Qualitative spatial reasoning is a field which has defined itself over
the last few years as researchers in a variety of subject areas
have recognised the extent to which they have interests in common.
In all these areas, sophisticated
automated reasoning about the spatial relations between physical
objects or regions of space is of fundamental importance; and in many
cases, this must be done without precise, quantitative information
about these relations. Typically, some knowledge of the topological
relationships between the entities of interest may be available, along
with incomplete and imprecise information about distances, directions
and relative sizes; and from this partial information, useful
conclusions must be drawn. Examples of the kind of question for which
qualitative spatial reasoning is required can be drawn from the fields
mentioned: Identify the islands in the lake and the largest one.
Which parts of the network of tunnels can the robot traverse without
getting stuck? Could the collection of objects in the scene fit
together to make a spherical or cylindrical shell? When cog A is
turned clockwise, will cog B turn and if so, in which direction?
These are all examples of the kind of problem human beings solve (and
sometimes fail to solve) without making precise measurements; if we
are to maximise the potential of computer systems to help them, we
must understand the principles that make possible these forms of
reasoning. This is not to assume that computer systems will
necessarily use the same methods as human reasoners; but the fact that
people can answer such questions constitutes a form of proof that
usable methods exist. The qualitative spatial reasoning community has
set itself the task of finding them.