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Representing Temporal Knowledge for Case-Based Prediction

Summary: Representing Temporal Knowledge
for Case-Based Prediction
Martha Dørum Jære1
, Agnar Aamodt2
, Pål Skalle3
Borak S.L., Ronda de Poniente N4, Tres Cantos, CP 28760, Madrid, Spain
Artificial Intelligence Research Institute, IIIA, Spanish Council for Scientific Research, CSIC,
08193 Bellaterra, Barcelona, Spain.
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


Source: Aamodt, Agnar - Department of Computer and Information Science, Norwegian University of Science and Technology


Collections: Computer Technologies and Information Sciences