| | |
Summary: Representing Temporal Knowledge
for Case-Based Prediction
Martha Dørum Jære1
, Agnar Aamodt2
, Pål Skalle3
1
Borak S.L., Ronda de Poniente N4, Tres Cantos, CP 28760, Madrid, Spain
2
Artificial Intelligence Research Institute, IIIA, Spanish Council for Scientific Research, CSIC,
08193 Bellaterra, Barcelona, Spain.
3
Department of Petroleum Technology, Norwegian University of Science and Technology, NO-
7491, Trondheim, Norway.
Abstract. Cases are descriptions of situations limited in time and space. The
research reported here introduces a method for representation and reasoning
with time-dependent situations, or temporal cases, within a knowledge-
intensive CBR framework. Most current CBR methods deal with snapshot
cases, descriptions of a world state at a single time stamp. In many time-
dependent situations, value sets at particular time points are less important than
the value changes over some interval of time. Our focus is on prediction
|
