The PEO project investigates methods for representation and
interpretation of scientific and natural processes and events.
Mammoth amounts of data, that pertains to solar activity,
atmospheric conditions, ocean currents etc, is being collected
at various observatories, and this project is one attempt
to digest it. These observations are temporally and spatially
related by the processes and events happening when the data was
being collected. We want to infer these proce>sses and events by
munching data using their machine readable descriptions.
The first goal is to develop a theory to interpret processes.
The theory must help 1. decide whether a process can happen
given a set of conditions, and if it can happen, 2. what is the
effect that it will have, 3. being aware of other processes
that may be occurring concurrently. Given current state and
conditions the theory should help predict changes in the state
if the process occurred. In contrast given some observations,
the theory must help predict if the process could have played a
role in changes noticed in the observations.
Next step would be to develop a language for representing
processes such that the process descriptions can be
unambiguously interpretted using the theory developed in the
In the last step we would focus on the development of an
efficient, scalable, open-source reasoner that can munch process
descriptions in the language developed in the second phase of
the project and observations to guess the processes and events
that were in play when the data was collected, along with
justifications for the drawn inferences.
See also: http://tw.rpi.edu/proj/tami/Combining_FOL_QR_CLP_DEs