Knowledge acquisition and discovery for the textual case-based cooking system WIKITAAABLE

International audienceThe textual case-based cooking systemWIKITAAABLE participates to the second Computer cooking contest (CCC). It is an extension of the TAAABLE system that has participated to the first CCC. WIKITAAABLE's architecture is composed of a semantic wiki used for the collaborative acquisition of knowledge (recipe, ontology, adaptation knowledge) and of a case-based inference engine using this knowledge for retrieving and adapting recipes. This architecture allows various modes of knowledge acquisition for case-based reasoning that are studied within the TAAABLE project. In particular, opportunistic adaptation knowledge discovery is an approach for interactive and semi-automatic learning of adaptation knowledge triggered by a feedback from the user

Découverte opportuniste de connaissances d'adaptation

National audienceL'étape d'adaptation est souvent considérée comme le talon d'Achille du raisonnement à partir de cas car elle nécessite des connaissances spécifiques au domaine d'application qui sont difficiles à acquérir. Dans ce papier, deux stratégies sont combinées pour faciliter la tâche d'acquisition de connaissances d'adaptation : les connaissances d'adaptation sont apprises à partir de la base de cas par des techniques d'extraction de connaissances, et l'acquisition de connaissances d'adaptation est déclenchée de manière opportuniste au cours d'une session particulière de résolution de problèmes

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

Adaptation par révision et adaptation différentielle : comparaison de deux approches de l'adaptation

National audienceL'objectif de cet article est la comparaison de deux approches de l'adaptation. L'adaptation par révision (APR) s'appuie sur un opérateur de révision des connaissances et consiste à appliquer cet opérateur pour réviser le cas source par le cas cible qui représente le problème à résoudre, en prenant en compte les connaissances du domaine. Les connaissances utiles à l'APR sont l'opérateur de révision et les connaissances du domaine. L'adaptation différentielle (AD) s'inspire du calcul différentiel en le généralisant à des espaces de représentation non nécessairement numériques. Les connaissances d'adaptation de l'AD sont les influences de chaque variation de descripteurs de problème sur les variations des descripteurs de solution. Il est montré que l'APR généralise l'AD au sens où on peut construire un opérateur de révision paramétré par les connaissances de l'AD telles que l'APR muni d'un tel opérateur et sans connaissances du domaine coïncide avec l'AD. Cela indique d'une part comment étendre l'AD pour qu'elle tienne compte de connaissances du domaine et, d'autre part, comment choisir un opérateur de révision pertinent pour l'APR en s'appuyant sur les principes de l'AD

Knowledge Continuous Integration Process (K-CIP)

International audienceSocial semantic web creates read/write spaces where users and smart agents collaborate to produce knowledge readable by humans and machines. An important issue concerns the ontology evolution and evaluation in man-machine collaboration. How to perform a change on ontologies in a social semantic space that currently uses these ontologies through requests ? In this paper, we propose to implement a continuous knowledge integration process named K-CIP. We take advantage of man-machine collaboration to transform feedback of people into tests. This paper presents how K-CIP can be deployed to allow fruitful man-machine collaboration in the context of the WikiTaaable system.Le web sémantique social crée des espaces partagés dans lesquels des utilisateurs et des agents logiciels collabore pour produire de la connaissance utilisable par les humains et les machines. Un problème important est celui de l'évolution et l'évaluation des ontologies dans la collaboration : comment réaliser un changement sur une ontologie dans un espace qui utilise cette ontologie. Dans ce papier, nous proposons de réaliser un processus d'intégration continue de la connaissance nommé K-CIP. Nous tirons profit des retours des utilisateurs dans la collaboration pour construire des tests. Cet article montre comment K-CIP peut être mis en oeuvre pour améliorer la collaboration humain-machine dans le contexte du système WikiTaaable

Adaptation knowledge discovery using positive and negative cases

International audienceCase-based reasoning usually exploits positive source cases, each of them consisting in a problem and a correct solution to this problem. Now, the general issue of exploiting also negative cases-i.e., problem-solution pairs where the solution answers incorrectly the problem-can be raised. Indeed, such cases are "naturally" generated by a CBR system as long as it sometimes proposes incorrect solutions. This paper aims at addressing this issue for adaptation knowledge (AK) discovery: how positive and negative cases can be used for this purpose. The idea is that positive cases are used to propose adaptation rules and that negative cases are used to filter out some of these rules. In a preliminary work, this kind of AK discovery has been applied using frequent closed itemset (FCI) extraction on variations within the case base and tested on a toy Boolean use case, with promising first results. This paper resumes this study and evaluates it on 4 benchmarks, which confirms the benefit of exploiting negative cases for AK discovery. This involves some adjustments in the data preparation and in adaptation rule filtering, in particular because FCI extraction works only with Boolean features, hence some methodology lessons learned for AK discovery with positive and negative cases

Le moteur de raisonnement à partir de cas de WikiTaaable

National audienceLe système WikiTaaable est un système de raisonnement à partir de cas dont les cas sont des recettes de cuisine et les problèmes des requêtes données par des ingredients et types de plats requis ou à éviter. Son moteur d'inférences s'appuie sur le modèle des reformulations et des chemins de similarité : la remémoration met en évidence une recette et un chemin de similarité qui réifie le lien recette-requête et sert de base au processus d'adaptation. Si la base de recettes était organisée de manière adéquate, la remémoration pourrait être mise en oeuvre grâce à des modifications à la fois sur la base de recettes (via cette organisation) et sur la requête, dans l'optique d'apparier exactement au moins une recette modifiée et la requête modifiée (le chemin serait alors décrit par la séquence des modifications). Dans la version actuelle, le travail d'organisation de la base de recettes n'ayant pas été fait (est-il seulement faisable ?), la remémoration consiste en la modification seule de la requête. Une fois une recette remémorée, elle peut être adaptée selon cette première version du chemin de similarité mais un nouvel appariement avec modifications de la recette et de la requête peut éventuellement aboutir à un " meilleur " chemin de similarité, i.e., un chemin conduisant à une adaptation moins risquée. Cette notion de risque est mesurée à l'aide d'une fonction de coût associée aux chemins de similarité. La représentation actuelle des connaissances utilisées par le système est limitée ; elle ne permet pas de représenter, par exemple, des quantités. Une réflexion sur ce que devrait être la nouvelle représentation est également menée dans cet article

Actes du 17ème séminaire sur le raisonnement à partir de Cas, Paris (29--30 juin)

Actes du séminaire RàPC 2009

Intelligent monitoring of business processes using case-based reasoning

The work in this thesis presents an approach towards the effective monitoring of business processes using Case-Based Reasoning (CBR). The rationale behind this research was that business processes constitute a fundamental concept of the modern world and there is a constantly emerging need for their efficient control. They can be efficiently represented but not necessarily monitored and diagnosed effectively via an appropriate platform. Motivated by the above observation this research pursued to which extent there can be efficient monitoring, diagnosis and explanation of the workflows. Workflows and their effective representation in terms of CBR were investigated as well as how similarity measures among them could be established appropriately. The monitoring results and their following explanation to users were questioned as well as which should be an appropriate software architecture to allow monitoring of workflow executions. Throughout the progress of this research, several sets of experiments have been conducted using existing enterprise systems which are coordinated via a predefined workflow business process. Past data produced over several years have been used for the needs of the conducted experiments. Based on those the necessary knowledge repositories were built and used afterwards in order to evaluate the suggesting approach towards the effective monitoring and diagnosis of business processes. The produced results show to which extent a business process can be monitored and diagnosed effectively. The results also provide hints on possible changes that would maximize the accuracy of the actual monitoring, diagnosis and explanation. Moreover the presented approach can be generalised and expanded further to enterprise systems that have as common characteristics a possible workflow representation and the presence of uncertainty. Further work motivated by this thesis could investigate how the knowledge acquisition can be transferred over workflow systems and be of benefit to large-scale multidimensional enterprises. Additionally the temporal uncertainty could be investigated further, in an attempt to address it while reasoning. Finally the provenance of cases and their solutions could be explored further, identifying correlations with the process of reasoning

Case based design of knitwear

In the developed world we are surrounded by man-made objects, but most people give little thought to the complex processes needed for their design. The design of hand knitting is complex because much of the domain knowledge is tacit. The objective of this thesis is to devise a methodology to help designers to work within design constraints, whilst facilitating creativity. A hybrid solution including computer aided design (CAD) and case based reasoning (CBR) is proposed. The CAD system creates designs using domain-specific rules and these designs are employed for initial seeding of the case base and the management of constraints. CBR reuses the designer's previous experience. The key aspects in the CBR system are measuring the similarity of cases and adapting past solutions to the current problem. Similarity is measured by asking the user to rank the importance of features; the ranks are then used to calculate weights for an algorithm which compares the specifications of designs. A novel adaptation operator called rule difference replay (RDR) is created. When the specifications to a new design is presented, the CAD program uses it to construct a design constituting an approximate solution. The most similar design from the case-base is then retrieved and RDR replays the changes previously made to the retrieved design on the new solution. A measure of solution similarity that can validate subjective success scores is created. Specification similarity can be used as a guide whether to invoke CBR, in a hybrid CAD-CBR system. If the newly resulted design is suffciently similar to a previous design, then CBR is invoked; otherwise CAD is used. The application of RDR to knitwear design has demonstrated the flexibility to overcome deficiencies in rules that try to automate creativity, and has the potential to be applied to other domains such as interior design