Acquisition of Adaptation Knowledge for Breast Cancer Treatment Decision Support

Colloque avec actes et comité de lecture. internationale.International audienceThe elaboration of a treatment in cancerology depends on the particular practice of decision protocols. These protocols are often adapted rather than used straightforwardly. This paper deals with the acquisition of the knowledge exploited during protocol adaptations. It shows that this knowledge acquisition process can be based on similarity paths, that are used for representing the matchings between decision problems (e.g., source and target problems within a case-based reasoning process)

Case-based learning: Predictive features in indexing

Interest in psychological experimentation from the Artificial Intelligence community often takes the form of rigorous post-hoc evaluation of completed computer models. Through an example of our own collaborative research, we advocate a different view of how psychology and AI may be mutually relevant, and propose an integrated approach to the study of learning in humans and machines. We begin with the problem of learning appropriate indices for storing and retrieving information from memory. From a planning task perspective, the most useful indices may be those that predict potential problems and access relevant plans in memory, improving the planner's ability to predict and avoid planning failures. This “predictive features” hypothesis is then supported as a psychological claim, with results showing that such features offer an advantage in terms of the selectivity of reminding because they more distinctively characterize planning situations where differing plans are appropriate.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46928/1/10994_2004_Article_BF00993173.pd

A Data-Driven Case-Based Reasoning Approach to Interactive Storytelling

Abstract. In this paper we describe a data-driven interactive storytelling system similar to previous work by Gordon & Swanson. We addresses some of the problems of their system, by combining information retrieval, machine learning and natural language processing. To evaluate our system, we leverage emerging crowd-sourcing communities to collect orders of magnitude more data and show statistical improvement over their system. The end result is a computer agent capable of contributing to stories that are nearly indistinguishable form entirely human written ones to outside observers

Deleting and building sort out techniques for case-base maintenance

Early work on case based reasoning reported in the literature shows the importance of case base maintenance for successful practical systems. Different criteria to the maintenance task have been used for more than half a century. In this paper we present different sort out techniques for case base maintenance. All the sort out techniques proposed are based on the same principle: a Rough Sets competence model. First of all, we present sort out reduction techniques based on deletion of cases. Next, we present sort out techniques that build new reduced competent case memories based on the original ones. The main purpose of these methods is to maintain the competence and reduce, as much as possible, its size. Experiments using different domains, most of them from the UCI repository, show that the reduction techniques maintain the competence obtained by the original case memory. The results are analysed with those obtained using well-known reduction techniques