Schwa-Deletion in Hindi Text-to-Speech Synthesis

We describe the phenomenon of schwa-deletion in Hindi and how it is handled in the pronunciation component of a multilingual concatenative text-to-speech system. Each of the consonants in written Hindi is associated with an inherent schwa vowel which is not represented in the orthography. For instance, the Hindi word pronounced as [namak] (salt) is represented in the orthography using the consonantal characters for [n], [m], and [k]. Two main factors complicate the issue of schwa pronunciation in Hindi. First, not every schwa following a consonant is pronounced within the word. Second, in multimorphemic words, the presence of a morpheme boundary can block schwa deletion where it might otherwise occur. We propose a model for schwa-deletion which combines a general purpose schwa-deletion rule proposed in the linguistics literature (Ohala, 1983), with additional morphological analysis necessitated by the high frequency of compounds in our database. The system is implemented in the framework of finite-state transducer technology.     

Enhancing the performance of an agent-based manufacturing system through learning and forecasting

Agent-based technology has been identified as an important approach for developing next generation manufacturing systems. One of the key techniques needed for implementing such advanced systems will be learning. This paper first discusses learning issues in agent-based manufacturing systems and reviews related approaches, then describes how to enhance the performance of an agent-based manufacturing system through learning from history (based on distributed case-based learning and reasoning) and learning from the future (through system forecasting simulation). Learning from history is used to enhance coordination capabilities by minimizing communication and processing overheads. Learning from the future is used to adjust promissory schedules through forecasting simulation, by taking into account the shop floor interactions, production and transportation time. Detailed learning and reasoning mechanisms are described and partial experimental results are presented.     

Informatik als Grundbildung

Im Teil II Bildung von Elementarsätzen wird der Teil Namengebung und Kennzeichnung vorbereitet. Seine Überschrift könnte auch lauten: Was sind Nominatoren und wie kommen sie zustande? Teil V ist wie seine Vorgänger Teil III Gleichheit und Abstraktion und Teil IV Objektsprache/Metasprache als eine Erweiterung einer rationalen Grammatik aufzufassen. Der noch folgende Teil VI Logik und Geltungsicherung von Behauptungen schließt die Serie Informatik als Grundbildung ab.     

Modelling structures in generic space, a condition for adaptiveness of monitoring cognitive agent

The adaptiveness of agents is one of the basic conditions for the autonomy. This paper describes an approach of adaptiveness forMonitoring Cognitive Agents based on the notion of generic spaces. This notion allows the definition of virtual generic processes so that any particular actual process is then a simple configuration of the generic process, that is to say a set of values of parameters. Consequently, generic domain ontology containing the generic knowledge for solving problems concerning the generic process can be developed. This lead to the design of Generic Monitoring Cognitive Agent, a class of agent in which the whole knowledge corpus is generic. In other words, modeling a process within a generic space becomes configuring a generic process and adaptiveness becomes genericity, that is to say independence regarding technology. In this paper, we present an application of this approach on Sachem, a Generic Monitoring Cognitive Agent designed in order to help the operators in operating a blast furnace. Specifically, the NeuroGaz module of Sachem will be used to present the notion of a generic blast furnace. The adaptiveness of Sachem can then be noted through the low cost of the deployment of a Sachem instance on different blast furnaces and the ability of NeuroGaz in solving problem and learning from various top gas instrumentation.     

Toward Third Generation Threaded Discussions for Mobile Learning: Opportunities and Challenges for Ubiquitous Collaborative Environments

The mobile communication revolution has led to pervasive connectedness—as evidenced by the explosive growth of instant messaging in the home, and more recently, the enterprise–and, together with the convergence of mobile computing, provides a basis for extending collaborative environments toward truly ubiquitous immersion. Leveraging the true anytime/anywhere access afforded by mobile computing, it becomes possible to develop applications that not only are capable of responding to users whenever/wherever, on demand, but that also may actively seek out and engage users when the need arises. Thus, immersive environments need no longer be thought of strictly in terms of physical immersion with clearly discernable enter and exit events, but rather they may be extended, through mobile-enabled computing, toward ubiquity in terms of both time and space. Based on Media Synchronicity Theory, potential benefits are envisioned, particularly in the case of collaborative learning environments, from shortened response cycles and increased real time interaction opportunities. At the same time, a number of challenging issues must be addressed in designing such an environment to ensure user acceptance and to maximize realization of the potential. Third Generation (3G) Threaded Discussion has been conceptualized as an environment, well suited to mobile learning (m-learning) that could leverage mobile-enabled ubiquity to achieve a degree of extended immersion and thereby accrue the associated collaboration benefits. Exploring this conceptualization serves to help surface both the opportunities and the challenges associated with such environments and to identify promising design approaches, such as the use of intelligent agents.This revised version was published online in March 2005 with corrections to the cover date     

Rapid Concept Learning for Mobile Robots

Concept learning in robotics is an extremely challenging problem: sensory data is often high dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.     

Low-Rank Approximation of Integral Operators by Interpolation

A central component of the analysis of panel clustering techniques for the approximation of integral operators is the so-called -admissibility condition min {diam(),diam()} 2dist(,) that ensures that the kernel function is approximated only on those parts of the domain that are far from the singularity. Typical techniques based on a Taylor expansion of the kernel function require a subdomain to be far enough from the singularity such that the parameter has to be smaller than a given constant depending on properties of the kernel function. In this paper, we demonstrate that any is sufficient if interpolation instead of Taylor expansionisused for the kernel approximation, which paves the way for grey-box panel clustering algorithms.     

Rapid Concept Learning for Mobile Robots

Concept learning in robotics is an extremely challenging problem: sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as opening or wall. The second task is recycling, where the robot has to learn to recognize objects, such as a trash can. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.     

The role of “craft language” in learning “Waza”

The role of craft language in the process of teaching (learning) Waza (skill) will be discussed from the perspective of human intelligence.It may be said that the ultimate goal of learning Waza in any Japanese traditional performance is not the perfect reproduction of the teaching (learning) process of Waza. In fact, a special metaphorical language (craft language) is used, which has the effect of encouraging the learner to activate his creative imagination. It is through this activity that the he learns his own habitus (Kata).It is suggested that, in considering the difference of function between natural human intelligence and artificial intelligence, attention should be paid to the imaginative activity of the learner as being an essential factor for mastering Kata.This article is a modified English version of Chapter 5 of my bookWaza kara shiru (Learning from Skill), Tokyo University Press, 1987, pp. 93–105.     

Incremental Multi-Step Q-Learning

This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic-programming based reinforcement learning method, with the TD() return estimation process, which is typically used in actor-critic learning, another well-known dynamic-programming based reinforcement learning method. The parameter is used to distribute credit throughout sequences of actions, leading to faster learning and also helping to alleviate the non-Markovian effect of coarse state-space quantization. The resulting algorithm, Q()-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. The behavior of this algorithm has been demonstrated through computer simulations.     

Utilizing deontic operators in information systems specification

One major task in requirements specification is to capture the rules relevant to the problem at hand. Declarative, rule-based approaches have been suggested by many researchers in the field. However, when it comes to modeling large systems of rules, not only for the behavior of the computer system but also for the organizational environment surrounding it, current approaches have problems with limited expressiveness, flexibility, and poor comprehensibility. Hence, rule-based approaches may benefit from improvements in two directions: (1) improvement of the rule languages themselves and (2) better integration with other, complementary modeling approaches.In this article, both issues are addressed in an integrated manner. The proposal is presented in the context of the Tempora project on rule-based information systems development, but has also been integrated with PPP. Tempora has provided a rule language based on an executable temporal logic working on top of a temporal database. The rule language is integrated with static (ER-like) and dynamic (SA/RT-like) modeling approaches. In the current proposal, the integration with complementary modeling approaches is extended by including organization modeling (actors, roles), and the expressiveness of the rule language is increased by introducing deontic operators and rule hierarchies. The main contribution of the article is not seen as any one of the above-mentioned extensions, but as the resulting comprehensive modeling support. The approach is illustrated by examples taken from an industrial case study done in connection with Tempora.C. List of Symbols Subset of set - Not subset of set - Element of set - Not element of set - Equivalent to - Not equivalent to - ¬ Negation - Logical and - Logical or - Implication - Sometime in past - Sometime in future - Always in past - Always in future - Just before - Just after - u Until - s Since - Trigger - Condition - _s State condition - Consequence - _a Action - _s State - Role - Actor - ¬ - General deontic operator - O Obligatory - R Recommended - P Permitted - D Discouraged - F Forbidden - (/–) General rule - t _R Real time - t _M Model time     

Linear Interval Tolerance Problem and Linear Programming Techniques

In this paper, we consider the linear interval tolerance problem, which consists of finding the largest interval vector included in ([A], [b]) = {x R ⁿ | A [A], b [b], Ax = b}. We describe two different polyhedrons that represent subsets of all possible interval vectors in ([A], [b]), and we provide a new definition of the optimality of an interval vector included in ([A], [b]). Finally, we show how the Simplex algorithm can be applied to find an optimal interval vector in ([A], [b]).     

Noninvasive data analysis: a web of information granules

The study is concerned with a development of information granules and their use in data analysis. The design of information granules exploits a concept of overshadowing meaning that, we retain a given level of membership to a given concept unless faced with a contrary evidence. A detailed algorithm is provided and illustrated through a number of numerical studies. The idea of noninvasive data analysis is then introduced and discussed from a standpoint of a minimal level of structural dependencies to be used in the model.     

From Conditional Events to Conditional Measures: A New Axiomatic Approach

Our starting point is a definition of conditional event EH which differs from many seemingly similar ones adopted in the relevant literature since 1935, starting with de Finetti. In fact, if we do not assign the same third value u (undetermined) to all conditional events, but make it depend on EH, it turns out that this function t(EH) can be taken as a general conditional uncertainty measure, and we get (through a suitable – in a sense, compulsory – choice of the relevant operations among conditional events) the natural axioms for many different (besides probability) conditional measures.     

Deterministic sequential functions

The simple rational partial functions accepted by generalized sequential machines are shown to coincide with the compositions _P ^–1 , where _P consists of the prefix codings. The rational functions accepted by generalized sequential machines are proved to coincide with the compositions _P ^–1 , where is the family of endmarkers and is the family of removals of endmarkers. (The compositions are read from left to right). We also show that _P ^–1 is the family of the subsequential functions.This work was partially supported by the Esprit Basic Research Action Working Group No. 3166 ASMICS, the CNRS and the Academy of Finland     

A Note on the Ohya-Masuda Quantum Algorithm

We analyze the Ohya-Masuda quantum algorithm that solves the so-called satisfiability problem, which is an NP-complete problem of the complexity theory. We distinguish three steps in the algorithm, and analyze the second step, in which a coherent superposition of states (a pure state) transforms into an incoherent mixture presented by a density matrix. We show that, if nonideal (in analogy with nonideal quantum measurement), this transformation can make the algorithm to fail in some cases. On this basis we give some general notions on the physical implementation of the Ohya-Masuda algorithm.     

Efficient Construction of Regression Trees with Range and Region Splitting

We propose a method for constructing regression trees with range and region splitting. We present an efficient algorithm for computing the optimal two-dimensional region that minimizes the mean squared error of an objective numeric attribute in a given database. As two-dimensional regions, we consider a class R of grid-regions, such as x-monotone, rectilinear-convex, and rectangular, in the plane associated with two numeric attributes. We compute the optimal region R. We propose to use a test that splits data into those that lie inside the region R and those that lie outside the region in the construction of regression trees. Experiments confirm that the use of region splitting gives compact and accurate regression trees in many domains.     

Fodorian semantics,pathologies, and “Block's Problem”

In two recent books, Jerry Fodor has developed a set of sufficient conditions for an object X to non-naturally and non-derivatively mean X. In an earlier paper we presented three reasons for thinking Fodor's theory to be inadequate. One of these problems we have dubbed the Pathologies Problem. In response to queries concerning the relationship between the Pathologies Problem and what Fodor calls Block's Problem, we argue that, while Block's Problem does not threatenFodor's view, the Pathologies Problem does.We would like to thank Ray Elugardo, Pat Manfredi, and Donna Summerfield for helpful comments on an earlier paper on Fodorian Semantics, X means X: Semantics Fodor-Style. We would especially like to thank Ned Block for extended e-mail conversations about Block's Problem. Block agrees that his problem is not the same as our pathologies problem. Contrary to what we say here, he still maintains that his objection can ultimately be made to work to defeat Fodor's theory of meaning. His elaboration of Block's Problem is different than the one we present here. Versions of a related paper were presented at the 1991 Annual Meeting of the Southern Society for Philosophy and Psychology as well as the Canadian Society for History and Philosophy of Science.     

The dynamic clusters method in nonhierarchical clustering

Given a finite setE R ⁿ, the problem is to find clusters (or subsets of similar points inE) and at the same time to find the most typical elements of this set. An original mathematical formulation is given to the problem. The proposed algorithm operates on groups of points, called samplings (samplings may be called multiple centers or cores); these samplings adapt and evolve into interesting clusters. Compared with other clustering algorithms, this algorithm requires less machine time and storage. We provide some propositions about nonprobabilistic convergence and a sufficient condition which ensures the decrease of the criterion. Some computational experiments are presented.