Amélie Cordier

Bio: Amélie Cordier is an academic researcher from University of Lyon. The author has contributed to research in topics: Case-based reasoning & Knowledge acquisition. The author has an hindex of 14, co-authored 84 publications receiving 616 citations. Previous affiliations of Amélie Cordier include Claude Bernard University Lyon 1 & Lyon College.

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

Abstract: This paper presents how the Taaable project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful

Extending Case-Based Reasoning with Traces

Abstract: One topical challenge in knowledge engineering is to build systems that can ease the sharing and re-using of experience between a large community of users. This requires a certain plasticity of systems and associated interfaces. Yet, the lack of flexibility is a limitation of current case-based reasoning (CBR) systems. They are often developed in order to solve one particular problem and are not designed to be changed by users depending on the use they want to make of them. In this paper we propose to use interaction traces as a knowledge source for CBR systems and we show how it allows us to drive back the current limits of CBR.

Taaable: A Case-Based System for Personalized Cooking

Abstract: Taaable is a Case-Based Reasoning (CBR) system that uses a recipe book as a case base to answer cooking queries. Taaable participates in the Computer Cooking Contest since 2008. Its success is due, in particular, to a smart combination of various methods and techniques from knowledge-based systems: CBR, knowledge representation, knowledge acquisition and discovery, knowledge management, and natural language processing. In this chapter, we describe Taaable and its modules. We first present the CBR engine and features such as the retrieval process based on minimal generalization of a query and the different adaptation processes available. Next, we focus on the knowledge containers used by the system. We report on our experiences in building and managing these containers. The Taaable system has been operational for several years and is constantly evolving. To conclude, we discuss the future developments: the lessons that we learned and the possible extensions.

Interactive and Opportunistic Knowledge Acquisition in Case-Based Reasoning

Abstract: As a young discipline at the junction of computer science, artificial intelligence and cognitive sciences, knowledge engineering aims at modelling knowledge of a specific domain to operationalise them in a computer system. To this end, it offers theoretical tools, models and empirical methodologies to support knowledge sharing between the user and the system. The work developed here is related to knowledge engineering of a particular type of system: case-based reasoning systems (CBR). A CBR system assists a user in his problem solving task by retrieving a previous successful problem solving experience and by adapting it to the current situation. In this work, we are mainly interested in the interacting system "user - CBR tool". The main research question we address here can be formulated as: what methods and tools have to be developed to support knowledge acquisition in the learning system "user - CBR tool". This issue raises the question of the knowledge of the reasoning process and leads to an analysis at the knowledge level of CBR systems. Another part of the analysis aims at studying the interactions between the user and the CBR tool during the problem solving phases. These aspects are studied at several levels in the different contributions presented in this thesis. Our different experiences and experiments lead us to propose, as a first contribution, a formalisation at general level of interactive knowledge learning in CBR (FIKA). This formalisa- tion relies on the reasoning failures which, as they allow to highlight the gaps in the available knowledge, are used to guide the learning process. Two extensions of this general model have been proposed: IAKA and FRAKAS. IAKA refines the principles proposed in FIKA to permit their immediate implementation in a particular type of system where knowledge can be represented according to a given model (cases and adaptation knowledge in the form of adaptation operators). These principles have been implemented and experimented with in an application developed exclusively for this purpose. FRAKAS proposes similar methods and tools for another type of system where domain knowledge is used to guide adaptation. As for these principles, they have been implemented in a prototype inspired by a real world application. We have conducted a study of strengths and limits of FRAKAS and IAKA and we have investigated possible ways to combine them. A first practical implementation has been made in a CBR application allowing the adaptation of cooking recipes, the project TAAABLE.

Trace-Based Reasoning - Modeling Interaction Traces for Reasoning on Experiences.

Abstract: This paper addresses Trace-Based Reasoning (TBR) by using Case-Based Reasoning (CBR) as a descriptive framework. TBR is a reasoning paradigm in which inferences are made on specific objects called traces. Traces are sequential records of events observed and stored during an interactive process. We report two contributions. First, we propose a review of the current researches related to TBR. Then, we compare CBR and TBR. From this comparison, we show that the exploitation of traces instead of cases as knowledge sources raises very specific challenges. More precisely, new methods for defining similarity measures and for performing adaptation of traces are required. These new methods have to take into account the sequential properties of traces. In the discussion, we emphasis the benefits of using traces as a knowledge container in a reasoning process and we pinpoint promising applications of TBR.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

Abstract: Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

Case-Based Reasoning Systems in the Health Sciences: A Survey of Recent Trends and Developments

Abstract: The health sciences are, nowadays, one of the major application areas for case-based reasoning (CBR). The paper presents a survey of recent medical CBR systems based on a literature review and an e-mail questionnaire sent to the corresponding authors of the papers where these systems are presented. Some clear trends have been identified, such as multipurpose systems: more than half of the current medical CBR systems address more than one task. Research on CBR in the area is growing, but most of the systems are still prototypes and not available in the market as commercial products. However, many of the projects/systems are intended to be commercialized.