The central problem of Scientific Discovery is to find scientific regularity in experimental observations.
In general, any technique may be employed, from wild guesses to careful explorations of mathematical models to
discover possible scientific explanations. Historically, inspired guessing has been the predominate technique,
but with the massive data sets from genetics and other disciplines now available, more disciplined ways of searching
for underlying laws are needed. One of these is Scientific Discovery: Systematic Methods.
WHAT IT IS
- Programs that
automate complex and creative scientific tasks.
- New systematic methods of scientific inference, even if its automation is not yet feasible.
- New representation or classification of science that enhances efforts to systematize it.
- New opportunities for known systematic methods.
- Recent scientific achievement where the computer played an essential creative role.
- New heuristics for scientific research, e.g., that promises to make practicable some aspect
of automated scientific reasoning.
- Computational models of historical discoveries in science.
- Cognitive studies of the scientific process that promise to contribute to computational approaches. "
Stanford AI Symposium "Systematic Methods of Scientific Discovery"
Computational cognition is the study of the computational basis of learning and inference by mathematical modeling,
computer simulation, and behavioral experiments, seeking to learn the basis behind the processing of information (Wikipedia)
The discipline of intelligent systems has evolved towards its critical goal of embedding non-computable human sensations,
perceptions and creativity into machines in 21st century.
This critical goal can only be achieved with the help of a new engineering paradigm called machinself (from machine itself).
Before building a machinself we need to build cognition systems into machines; namely, to invent a computational cognition.
This is a multidisciplinary challenge. "International Journal of Computational Cognition"
We study the computational basis of human learning and inference.
Through a combination of mathematical modeling, computer simulation, and behavioral experiments,
we try to uncover the logic behind our everyday inductive leaps:
constructing perceptual representations, separating "style" and "content" in perception,
learning concepts and words, judging similarity or representativeness, inferring causal connections,
noticing coincidences, predicting the future.
"Computational Cognitive Science Group (Department of Brain and Cognitive Sciences, MIT)"