Improving Adaptation Knowledge Discovery by Exploiting Negative Cases: First Experiment in a Boolean Setting

International audienceCase-based reasoning usually exploits positive source cases consisting in a source problem and its solution that is known to be a correct for the problem. The work presented in this paper addresses in addition of positive case exploitation, the exploitation of negative cases, i.e. problem-solution pairs where the solution is an incorrect answer to the problem, which can be acquired when the case-based reasoning (CBR) process fails. An originality of this work is that positive and negative cases are used both for adaptation knowledge (AK) discovery using closed itemsets built on variations between cases. Experiments show that exploiting negative cases in addition to positive ones improves the quality of the AK being extracted and, so, improves the results of the CBR system

Case-Based Cooking with Generic Computer Utensils: Taaable Next Generation

International audienceThis paper presents the participation of the Taaable team in the 2014 Computer Cooking Contest. The three challenges proposed this year are addressed. The basic challenge is addressed with a new version of the Taaable system, built on Tuuurbine, a generic case-basedreasoning system over RDFS. The mixology challenge which requires building recipes only by using a set of available foods is also directly addressed using Tuuurbine conjointly with Revisor, an adaptation engine implementing various revision operators. For the mixology challenge,Revisor is used to compute ingredient substitutions and to adjust the ingredient quantities. The taste score is evaluated as the probability that the adapted cocktail is tasty, depending on the probabilities that the retrieved recipe and the adaptation performed are good. The text of the preparation is adapted using textual substitutions. Finally, the originality challenge addresses reasoning on knowledge built collaboratively by an e-community, taking into account the reliability of knowledge units

Improving Ingredient Substitution using Formal Concept Analysis and Adaptation of Ingredient Quantities with Mixed Linear Optimization

International audienceThis paper presents the participation of the Taaable team to the 2015 Computer Cooking Contest. The Taaable system addresses the mixology and the sandwich challenges. For the mixology challenge, the 2014 Taaable system was extended in two ways. First, a formal concept analysis approach is used to improve the ingredient substitution, which must take into account a limited set of available foods. Second, the adaptation of the ingredient quantities has also been improved in order to be more realistic with a real cooking setting. The adaptation of the ingredient quantities is based on a mixed linear optimization. The team also applied Taaable to the sandwich challenge

How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability

International audienceThis paper shows that performing case-based reasoning (CBR) on knowledge coming from an e-community is improved by taking into account knowledge reliability. MKM (meta-knowledge model) is a model for managing reliability of the knowledge units that are used in the reasoning process. For this, MKM uses meta-knowledge such as belief, trust and reputation, about knowledge units and users. MKM is used both to select relevant knowledge to conduct the reasoning process, and to rank results provided by the CBR engine according to the knowledge reliability. An experiment in which users perform a blind evaluation of results provided by two systems (with and without taking into account reliability, i.e. with and without MKM) shows that users are more satisfied with results provided by the system implementing MKM

Adaptation knowledge discovery for cooking using closed itemset extraction

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