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.
10962196
Absolute versus probabilistic classification in a logical setting
Suppose we are given a set W of logical structures, or possible worlds, a set of logical formulas called possible data and a logical formula @f. We then consider the classification problem of determining in the limit and almost always correctly whether a possible world M satisfies @f, from a complete enumeration of the possible data that are true in M. One interpretation of almost always correctly is that the classification might be wrong on a set of possible worlds of measure 0, with respect to some natural probability distribution over the set of possible worlds. Another interpretation is that the classifier is only required to classify a set W^' of possible worlds of measure 1, without having to produce any claim in the limit on the truth of @f for the members of the complement of W^' in W. We compare these notions with absolute classification of W with respect to a formula that is almost always equivalent to @f in W, hence we investigate whether the set of possible worlds on which the classification is correct is definable. We mainly work with the probability distribution that corresponds to the standard measure on the Cantor space, but we also consider an alternative probability distribution proposed by Solomonoff and contrast it with the former. Finally, in the spirit of the kind of computations considered in Logic programming, we address the issue of computing almost correctly in the limit witnesses to leading existentially quantified variables in existential formulas.
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.
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.
37212285
Quantification over names and modalities
11623202
A memory aware behavioral synthesis tool for real-time VLSI circuits
We introduce a new approach to take into account the memory architecture and the memory mapping in the Behavioral Synthesis of Real-Time VLSI circuits. We formalize the memory mapping as a set of constraints for the thesis, 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 straints in our HLS tool GAUT. Our scheduling algorithm exhibits a relatively low complexity that permits to tackle complex designs in a reasonable time. Several experiments are performed to demonstrate the efficiency of our method, and to compare GAUT with an industrial behavioral synthesis tool. We finally show how to explore, with the help of GAUT, a wide range of solutions, and to reach a good tradeoffs between time, power-consumption, and area.
30324632
Neural Network Based Algorithm for Radiation Dose Evaluation in Heterogeneous Environments
An efficient and accurate algorithm for radiation dose evaluation is presented in this paper. Such computations are useful in the radiotherapic treatment planning of tumors. The originality of our approach is to use a neural network which has been trained with several homogeneous environments to deduce the doses in any kind of environment (possibly heterogeneous). Our algorithm is compared in several representative contexts to a reference simulation code in the domain.
15088597
G729 Voice Decoder Design
Embedded digital signal processing (DSP) systems are usually associated with real time constraints and/or high data rates such that fully software implementations are often not satisfactory. In that case, mixed hardware/software implementations are to be investigated. This paper presents the design of a HW/SW G.729 voice decoder dedicated to embedded systems. The decoder has been built around, on the one hand a reconfigurable digital circuit (FPGA) to achieve the so called IP hardware part—the autocorrelation computation—using a linear systolic array, and on the other hand a digital signal processor (DSP) for the remainder of the algorithm. Apart such an implementation is typically driven by the use of reusable component (IP) it is of great interest for new G729-based applications such as Voice over IP (VoIP) for example. It results in an overall reduction of the execution time per frame. Another interesting point is the design of a parameterizable autocorrelation block which can be useful for a wide range of applications such as GSM 13 Kbit/s, APC 9.6 Kbit/s and G723 6.3 Kbit/s and 5.3 Kbit/s. In the G729 context and using a V50 Virtex FPGA, the execution time of this function is 10 times faster than a TMS320C6201 DSP implementation.
30216304
Pipelined memory controllers for DSP applications handling unpredictable data accesses
Multimedia applications are often characterized by a large number of data accesses with regular and periodic access patterns. In these cases, optimized pipelined memory access controllers can be generated improving the pipeline access mode to RAM. We focus on the design and the implementation of memory sequencers that can be automatically generated from a behavioral synthesis tool and which can efficiently handle predictable address patterns as well as unpredictable ones (dynamic address computations) in a pipeline way.
16535802
Power Consumption Modeling and Characterization of the TI C6201
This new approach characterizes power dissipation on complex dsps. its processor model relies on an initial functional-level power analysis of the target processor together with a characterization that qualifies the more significant architectural and algorithmic parameters for power dissipation. these parameters come from a simple profiling of the assembly code. This functional model accounts for deeply pipelined, superscalar, and hierarchical memory architectures.