Constraints on predicate invention

This chapter describes an inductive learning method that derives logic programs and invents predicates when needed. The basic idea is to form the least common anti-instance (LCA) of selected seed examples. If the LCA is too general it forms the starting poínt of a gneral-to-specific search which is guided by various constraints on argument dependencies and critical terms. A distinguishing feature of the method is its ability to introduce new predicates. Predicate invention involves three steps. First, the need for a new predicate is discovered and the arguments of the new predicate are determíned using the same constraints that guide the search. In the second step, instances of the new predicate are abductively inferred. These instances form the input for the last step where the definition of the new predicate is induced by recursively applying the method again. We also outline how such a system could be more tightly integrated with an abductive learning system

La notion de « concept » dans les textes spécialisés : une étude comparative entre la progression thématique et la texture des concepts

Cet article met en évidence deux types de structures textuelles identifiables dans les articles de recherche écrits en anglais. La première, la progression thématique, est analysée dans un cadre de linguistique textuelle. Ensuite nous comparons ces résultats avec ceux d’un deuxième type de texture : celle des liens entre concepts identifiés par une méthode de « fouille de textes ». Nos conclusions soulignent la nature des difficultés textuelles qu’éprouvent les auteurs n’ayant pas l’anglais comme langue maternelle. Ce travail propose aussi un travail futur en vue de créer un outil d’aide à la rédaction pour les non anglophones s'exprimant en langue anglaise.This paper explores two types of text structure in scientific research articles written by native and non-native writers of English. We use a text linguistics analysis to study thematic progression in these texts. We then compare these results to the study of a new type of texture found in texts: that is, the texture formed by concept identification using a method from Text Mining. Our conclusions point out some of the specific difficulties that non-native writers face in managing the structure of their texts. We also look toward developing a semi-automatic aide for non-native writers

Human Heuristics for a Team of Mobile Robots

International audienceThis paper is at the crossroad of Cognitive Psychology and AI Robotics. It reports a cross-disciplinary project concerned about implementing human heuristics within autonomous mobile robots. In the following, we address the problem of relying on human-based heuristics to endow a group of mobile robots with the ability to solve problems such as target finding in a labyrinth. Such heuristics may provide an efficient way to explore the environment and to decompose a complex problem into subtasks for which specific heuristics are efficient. We first present a set of experiments conducted with group of humans looking for a target with limited sensing capabilities solving. Then we describe the heuristics extracted from the observation and analysis of their behavior. Finally we implemented these heuristics within khepera-like autonomous mobile robots facing the same tasks. We show that the control architecture can be experimentally validated to some extent thanks to this approach. Index Terms-- Cognition, Autonomous Robotics, Human-centered approach, Heuristics, Multi-agents Problem Solving

On the Notion of Interestingness in Automated Mathematical Discovery

Deciding whether something is interesting or not is of central importance in automated mathematical discovery, as it helps determine both the search space and search strategy for finding and evaluating concepts and conjectures

On the use of Process Mining and Machine Learning to support decision making in systems design

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.FUI GONTRAN

Encoding Conceptual Graphs by Labeling RAAM

The meaning of medical texts... In this paper we discuss the possibility to memorize and retrieve natural language sentences and especially medical language sentences given in this kind of formalism with the use of the LRAAM model [Spe93b, Spe93a]. In Section 2 we explain the idea underlying conceptual graphs. In Section 3 we briefly expose the access by content capabilities of the LRAAM and suggest a generalization of the access by content procedures introducing the concept of Generalized Hopfield Network. A discussion on the impact of this generalization on knowledge extraction from a database of conceptual graphs is given in the conclusion

Apprenticeship Learning in Imperfect Domain Theories

This chapter presents DISCIPLE, a multi-strategy integrated learning system illustrating a theory and a methodology for learning expert knowledge in the context of an imperfect domain theory. DISCIPLE integrates a learning system and an empty expert system, both using the same knowledge base. It is initially provided with an imperfect (nonhomogeneous) domain theory and learns problem solving rules from the problem solving steps received from its expert user, during interactive problem solving sessions. In this way, DISCIPLE evolves from a helpful assistant in problem solving to a genuine expert. The problem solving method of DISCIPLE combines problem reduction, problem solving by constraints, and problem solving by analogy. The learning method of DISCIPLE depends of its knowledge about the problem solving step (the example) from which it learns. In the context of a complete theory about the example, DISCIPLE uses explanation-based learning to improve its performance. In the..