Section: Scientific Foundations

Representation and Reasoning with Imperfect Knowledge

While classical FOL is the kernel of many KRR languages, to solve real-world problems we often need to consider features that cannot be expressed purely (or not naturally) in classical logic. The logic- and graph-based formalisms used for previous points have thus to be extended with such features. The following requirements have been identified from scenarios in decision making in the agronomy domain (see 4.2 ):

1. to cope with vague and uncertain information and preferences in queries;

2. to cope with multi-granularity knowledge;

3. to take into account different and potentially conflicting viewpoints ;

4. to integrate decision notions (priorities, gravity, risk, benefit);

5. to integrate argumentation-based reasoning.

Although the extensions we will develop need to be validated on the applications that motivated them, we also want them to be sufficiently generic to be applied in other contexts. Our approach consists in increasing the expressivity of our core languages, while trying to preserve their essential combinatorial properties, so that algorithmic optimizations can be transferred to these extensions.