Many applications developed in very different areas such as Planning, Natural Language Processing, Hardware Design and Bioinformatics, need to manage critical aspects involving time. The models investigated in Artificial Intelligence literature for Temporal Reasoning are mainly based on the Constraint Satisfaction Problem (CSP) paradigm. Some of them deal with the representation of quantitative aspects of temporal data in terms of metric temporal statements about points. Other approaches, such as Interval Algebra, deal with a qualitative representation of temporal information in terms of qualitative relationships between intervals.
In everyday life these two aspects (either quantitative or qualitative) of temporal knowledge are not distinguished and real data are often affected by imprecision and uncertainty. Therefore, it is important to establish expressive frameworks for representing complex temporal relationships in a unified way.
In this talk the problem of representing and reasoning with fuzzy temporal knowledge in a general and flexible manner is introduced and a model for integrating qualitative and quantitative fuzzy temporal constraints is discussed.