5821670
Learning in Logic with RichProlog
Deduction and induction are unified on the basis of a generalized notion of logical consequence, having classical first-order logic as a particular case. RichProlog is a natural extension of Prolog rooted in this generalized logic, in the same way as Prolog is rooted in classical logic. Prolog can answer ?1 queries as a side effect of a deductive inference. RichProlog can answer ?1 queries, ?1 queries (as a side effect of an inductive inference), and ?2 queries (as a side effect of an inductive inference followed by a deductive inference). RichProlog can be used to learn: a learning problem is expressed as a usual logic program, supplemented with data, and solved by asking a ?2 query. The output is correct in the limit, i.e., when sufficient data have been provided.
39139980
On Sufficient Conditions for Learnability of Logic Programs from Positive Data
Shinohara, Arimura, and Krishna Rao have shown learnability in the limit of minimal models of classes of logic programs from positive only data. In most cases, these results involve logic programs in which the "size" of the head yields a bound on the size of the body literals. However, when local variables are present, such a bound on body literal size cannot directly be ensured. The above authors achieve such a restriction using technical notions like mode and linear inequalities. The present paper develops a conceptually clean framework where the behavior of local variables is controlled by nonlocal ones. It is shown that for certain classes of logic programs, learnablity from positive data is equivalent to limiting identification of bounds for the number of clauses and the number of local variables. This reduces the learning problem finding two integers. This cleaner framework generalizes all the known results and establishes learnability of new classes.
7592429
On the Convergence of Incremental Knowledge Base Construction
Ripple Down Rules is a practical methodology to build knowledge-based systems, which has proved successful in a wide range of commercial applications. However, little work has been done on its theoretical foundations. In this paper, we formalise the key features of the method. We present the process of building a correct knowledge base as a discovery scenario involving a user, an expert, and a system. The user provides data for classification. The expert helps the system to build its knowledge base incrementally, using the output of the latter in response to the last datum provided by the user. In case the system’s output is not satisfactory, the expert guides the system to improve its future performance while not affecting its ability to properly classify past data. We examine under which conditions the sequence of knowledge bases constructed by the system eventually converges to a knowledge base that faithfully represents the target classification function. Our results are in accordance with the observed behaviour of real-life systems.
2402100
Input-Dependence in Function-Learning
In the standard literature on inductive inference, a learner sees as input the course of values of the function to be learned. In the present work, it is investigated how reasonable this choice is and how sensitive the model is with respect to variations like the overgraph or undergraph of the function. Several implications and separations are shown and for the basic notions, a complete picture is obtained. Furthermore, relations to oracles, additional information and teams are explored.
38643151
Learning from Each Other
Since its inception, the field of machine learning has seen the advent of several learning paradigms, designed to frame the issues central to the learning activity, provide effective learning methods, and investigate the power and limitations inherent to the process of successful learning. In this article, we propose a formalization that underlies the key concepts of many such paradigms and discuss their relevance to scientific discovery, with the aim of assessing what scientists can expect from machines designed to assist them in their quest for the discovery of valid laws. We illustrate the formalization on several variations of a card game, and highlight the differences that paradigms impose on learners, as well as the assumptions they make on the nature of the learning process. We then use the formalization to describe a multi-agent interaction protocol, that has been inspired by these paradigms and that has been validated recently on some groups of agents. Finally, we propose extensions to this protocol.
37212285
Quantification over names and modalities
51977174
A Memory Aware High Level Synthesis Too
We introduce a new approach to take into account the memory architecture and the memory mapping in High- Level Synthesis for data intensive applications. We formalize the memory mapping as a set of constraints for the synthesis, and defined a Memory Constraint Graph and an accessibility criterion to be used in the scheduling step. We use a memory mapping file to include those memory constraints in our HLS tool GAUT. It is possible, with the help of GAUT, to explore a wide range of solutions, and to reach a good tradeoff between time, power-consumption, and area.
28948159
Autonomous capture of a tumbling satellite
In this paper, we describe a framework for the autonomous capture and servicing of satellites. The work is based on laboratory experiments that illustrate the autonomy and remote-operation aspects. The satellite-capture problem is representative of most on-orbit robotic manipulation tasks where the environment is known and structured, but it is dynamic since the satellite to be captured is in free flight. Bandwidth limitations and communication dropouts dominate the quality of the communication link. The satellite-servicing scenario is implemented on a robotic test-bed in laboratory settings. The communication aspects were validated in transatlantic tests. © 2007 Canadian Space Agency
33073441
Synthèse Comportementale Sous Contraintes de Communication et de Placement Mémoire pour les composants du TDSI
The design of complex Digital Signal Processing systems implies to minimize architectural cost and to maximize timing performances while taking into account communication and memory accesses constraints for the integration of dedicated hardware accelerator. Unfortunately, the traditional Matlab/ Simulink design flows gather not very flexible hardware blocs. In this paper, we present a methodology and a tool that permit the High-Level Synthesis of DSP applications, under both I/O timing and memory constraints. Based on formal models and a generic architecture, our tool GAUT helps the designer in finding a reasonable trade-off between the circuit's performance and its architectural complexity. The efficiency of our approach is demonstrated on the case study of a FFT algorithm.
12563790
Méthodologie de modélisation et d'implémentation d'adaptateurs spatio-temporels
The re-use of pre-designed blocks is a well-known concept of the software development. This technique has been applied to System-on-Chip (SoC) design whose complexity and heterogeneity are growing. The re-use is made thanks to high level components, called virtual components (IP), available in more or less flexible forms. These components are dedicated blocks: digital signal processing (DCT, FFT), telecommunications (Viterbi, TurboCodes),... These blocks rest on a model of fixed architecture with very few degrees of personalization. This rigidity is particularly true for the communication interface whose orders of acquisition and production of data, the temporal behavior and protocols of exchanges are fixed. The successful integration of such an IP requires that the designer (1) synchronizes the components (2) converts the protocols between "incompatible" blocks (3) temporizes the data to guarantee the temporal constraints and the order of the data. This phase remains however very manual and source of errors. Our approach proposes a formal modeling, based on an original Ressource Compatibility Graph. The synthesis flow is based on a set of transformations of the initial graph to lead to an interface architecture allowing the space-time adaptation of the data exchanges between several components.
17460903
A memory aware high level synthesis tool
We introduce a new approach to take into account the memory architecture and the memory mapping in high-level synthesis for data intensive applications. We formalize the memory mapping as a set of constraints for the synthesis, and defined a memory constraint graph and an accessibility criterion to be used in the scheduling step. We use a memory mapping file to include those memory constraints in our HLS tool GAUT. It is possible, with the help of GAUT, to explore a wide range of solutions, and to reach a good tradeoff between time, power-consumption, and area.