substitutions) appear to be very limited and the user has no means to give some feedback on the quality of the proposed adaptation.

For example, the substitutionΣ1 = ∅ ¬ peanut oil suggests to remove the ingredient peanut oil in the retrieved recipe, but as the oil is used in this recipe to saute the bok choy, the adapted recipe turns out to be practically unfeasible. A better adaptation would suggest to replace peanut oil by e.g., sesame oil, which can be modeled by the substitution Σ1 = peanut oil sesame oil. To generate this substitution automatically, the system could for example exploit the fact that the concepts peanut oil and sesame oil are both sub-concepts of the concept oil in O. But still, some additional knowledge would be needed to express the fact that peanut oil should be replaced by sesame oil, and not by olive oil or hot chili oil, as olive oil and hot chili oil are also sub-concepts of oil in O. Besides, the system should be aware that this substitution is recommended only in Asian cuisine, which can be modeled by the more precise substitution Σ1 = asian ∧ peanut oil asian ∧ sesame oil.

Furthermore, the second substitution Σ2 = green onions leek suggests to solely replace sliced green onions by uncooked leek. But the green onion was used in the original *Wonton Soup* for garniture, so the user might consider that raw leek added as garniture alters too much the taste of a soup. A better adaptation would consist in frying leek with e.g., tempeh and red bell pepper to prepare the garniture. Such an adaptation can be modeled by the substitution Σ2 = green onions leek ∧ tempeh ∧ red bell pepper. This substitution, which reflects a cooking know-how, can hardly be generated automatically from the ontology.

These examples show that in order to improve its adaptation capabilities, the system would greatly benefit from the availability of a set of adaptation rules that would capture more complex adaptation strategies. These adaptation rules cannot be generated automatically from the ontology and need to be acquired from other knowledge sources. These examples also show that the human expert plays a major role in adaptation knowledge acquisition and that in the cooking domain, adaptation rules are often highly contextual.

## 4 Opportunistic Adaptation Knowledge Discovery

The presented AK acquisition method combines two previous approaches of AK acquisition. The first one was implemented in the CabamakA system [5]
and learns AK from differences between cases by the means of knowledge discovery techniques (section 4.1). The second one was implemented in the IakA system [8] and acquires adaptation knowledge at problem-solving time through interactions with the user (section 4.2).

## 4.1 Adaptation Knowledge Discovery From The Case Base

Machine learning algorithms aim at extracting some regularities from a set of observations. Knowledge discovery techniques combine efficient machine