Representing Temporal Knowledge
for Case-Based Prediction

Martha Drum Jre1, Agnar Aamodt2, and Pl Skalle3

1Borak S.L., Ronda de Poniente N4, Tres Cantos, CP 28760, Madrid, Spain
2Artificial Intelligence Research Institute, IIIA, Spanish Council for Scientific Research, CSIC,
08193 Bellaterra, Barcelona, Spain.
3Department 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
problems for avoiding faulty situations. Based on a well-established theory of
temporal intervals, we have developed a method for representing temporal
cases inside the knowledge-intensive CBR system Creek. The paper presents
the theoretical foundation of the method, the representation formalism and basic
reasoning algorithms, and an example applied to the prediction of unwanted
events in oil well drilling.
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