Crypt-equivalent algebraic specifications

Summary Equivalence is a fundamental notion for the semantic analysis of algebraic specifications. In this paper the notion of crypt-equivalence is introduced and studied w.r.t. two loose approaches to the semantics of an algebraic specification T: the class of all first-order models of T and the class of all term-generated models of T. Two specifications are called crypt-equivalent if for one specification there exists a predicate logic formula which implicitly defines an expansion (by new functions) of every model of that specification in such a way that the expansion (after forgetting unnecessary functions) is homologous to a model of the other specification, and if vice versa there exists another predicate logic formula with the same properties for the other specification. We speak of first-order crypt-equivalence if this holds for all first-order models, and of inductive crypt-equivalence if this holds for all term-generated models. Characterizations and structural properties of these notions are studied. In particular, it is shown that first order crypt-equivalence is equivalent to the existence of explicit definitions and that in case of positive definability two first-order crypt-equivalent specifications admit the same categories of models and homomorphisms. Similarly, two specifications which are inductively crypt-equivalent via sufficiently complete implicit definitions determine the same associated categories. Moreover, crypt-equivalence is compared with other notions of equivalence for algebraic specifications: in particular, it is shown that first-order cryptequivalence is strictly coarser than abstract semantic equivalence and that inductive crypt-equivalence is strictly finer than inductive simulation equivalence and implementation equivalence.     

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.     

A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions

This paper proposes a study of Reinforcement Learning (RL) for continuous state-space and time control problems, based on the theoretical framework of viscosity solutions (VSs). We use the method of dynamic programming (DP) which introduces the value function (VF), expectation of the best future cumulative reinforcement. In the continuous case, the value function satisfies a non-linear first (or second) order (depending on the deterministic or stochastic aspect of the process) differential equation called the Hamilton-Jacobi-Bellman (HJB) equation. It is well known that there exists an infinity of generalized solutions (differentiable almost everywhere) to this equation, other than the VF. We show that gradient-descent methods may converge to one of these generalized solutions, thus failing to find the optimal control.In order to solve the HJB equation, we use the powerful framework of viscosity solutions and state that there exists a unique viscosity solution to the HJB equation, which is the value function. Then, we use another main result of VSs (their stability when passing to the limit) to prove the convergence of numerical approximations schemes based on finite difference (FD) and finite element (FE) methods. These methods discretize, at some resolution, the HJB equation into a DP equation of a Markov Decision Process (MDP), which can be solved by DP methods (thanks to a strong contraction property) if all the initial data (the state dynamics and the reinforcement function) were perfectly known. However, in the RL approach, as we consider a system in interaction with some a priori (at least partially) unknown environment, which learns from experience, the initial data are not perfectly known but have to be approximated during learning. The main contribution of this work is to derive a general convergence theorem for RL algorithms when one uses only approximations (in a sense of satisfying some weak contraction property) of the initial data. This result can be used for model-based or model-free RL algorithms, with off-line or on-line updating methods, for deterministic or stochastic state dynamics (though this latter case is not described here), and based on FE or FD discretization methods. It is illustrated with several RL algorithms and one numerical simulation for the Car on the Hill problem.     

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.     

Maximum entropy and the G/G/1/N queue

Summary A new hybrid analytic framework, based on the principle of maximum entropy, is used to derive a closed form expression for the queue length distribution of a G/G/1 finite capacity queue. It is shown that Birth-Death homogeneous recursions for a single resource queue are special cases of maximum entropy one-step transitions which can be applied either in an operational or stochastic context. Furthermore, an equivalence relationship is used to analyse two-stage cyclic queueing networks with general service times, and favourable comparisons are made with global balance and approximate results. Numerical examples provide useful information on how critically system behaviour is affected by the distributional form of interarrival and service patterns. Comments on the implication of the work to the performance analysis and aggregation of computer systems are included.Some of the material included in this paper has been presented to the Performance '86 and ACM Sigmetrics 1986 Joint Conference on Computer Modelling, Measurement and Evaluation, May 28–30, 1986, University of North Carolina, USA     

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.     

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.     

Choosing reference classes and building provisional models

A well-known problem in default logic is the ability of naive reasoners to explain bothg and ¬g from a set of observations. This problem is treated in at least two different ways within that camp.One approach is examination of the various explanations and choosing among them on the basis of various explanation comparators. A typical comparator is choosing the explanation that depends on the most specific observation, similar to the notion of narrowest reference class.Others examine default extensions of the observations and choose whatever is true in any extension, or what is true in all extensions or what is true in preferred extensions. Default extensions are sometimes thought of as acceptable models of the world that are discarded as more knowledge becomes available.We argue that the notions of specificity and extension lack clear semantics. Furthermore, we show that the problems these ideas were supposed to solve can be handled easily within a probabilistic framework.     

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.     

Sometime = always + recursion ≡ always on the equivalence of the intermittent and invariant assertions methods for proving inevitability properties of programs

Summary We propose and compare two induction principles called always and sometime for proving inevitability properties of programs. They are respective formalizations and generalizations of Floyd invariant assertions and Burstall intermittent assertions methods for proving total correctness of sequential programs whose methodological advantages or disadvantages have been discussed in a number of previous papers. Both principles are formalized in the abstract setting of arbitrary nondeterministic transition systems and illustrated by appropriate examples. The sometime method is interpreted as a recursive application of the always method. Hence always can be considered as a special case of sometime. These proof methods are strongly equivalent in the sense that a proof by one induction principle can be rewritten into a proof by the other one. The first two theorems of the paper show that an invariant for the always method can be translated into an invariant for the sometime method even if every recursive application of the later is required to be of finite length. The third and main theorem of the paper shows how to translate an invariant for the sometime method into an invariant for the always method. It is emphasized that this translation technique follows the idea of transforming recursive programs into iterative ones. Of course, a general translation technique does not imply that the original sometime invariant and the resulting always invariant are equally understandable. This is illustrated by an example.     

Theory and benefits of recursive certificate structures

Processing of a set of multi-level digital certificates, particularly path construction and validation, can be excessively resource consuming, and even impractical in some cases. This article introduces classifications of certificate sets as minimal, surplus, and deficient and explains the new paradigm of a recursive certificate structure designed to provide the equivalent of a minimal set of conventional certificates containing only the necessary and sufficient information to minimize the effort to validate a certificate sequence, with a potential avoidance of duplication of validation previously handled by related Certification Authorities.     

A Stylometric Analysis of Ya?ar Kemal’s ? nce Memed Tetralogy

We analyze four nce Memed novels of Yaar Kemal using six style markers: most frequent words, syllable counts, word type – or part of speech – information, sentence length in terms of words, word length in text, and word length in vocabulary. For analysis we divide each novel into five thousand word text blocks and count the frequencies of each style marker in these blocks. The style markers showing the best separation are most frequent words and sentence lengths. We use stepwise discriminant analysis to determine the best discriminators of each style marker. We then use these markers in cross validation based discriminant analysis. Further investigation based on multiple analysis of variance (MANOVA) reveals how the attributes of each style marker group distinguish among the volumes.     

On characterizing the “knee” of the Pareto curve based on Normal-Boundary Intersection

This paper deals with the issue of generating one Pareto optimal point that is guaranteed to be in a desirable part of the Pareto set in a given multicriteria optimization problem. A parameterization of the Pareto set based on the recently developed normal-boundary intersection technique is used to formulate a subproblem, the solution of which yields the point of maximum bulge, often referred to as the knee of the Pareto curve. This enables the identification of the good region of the Pareto set by solving one nonlinear programming problem, thereby bypassing the need to generate many Pareto points. Further, this representation extends the concept of the knee for problems with more than two objectives. It is further proved that this knee is invariant with respect to the scales of the multiple objective functions.The generation of this knee however requires the value of each objective function at the minimizer of every objective function (the pay-off matrix). The paper characterizes situations when approximations to the function values comprising the pay-off matrix would suffice in generating a good approximation to the knee. Numerical results are provided to illustrate this point. Further, a weighted sum minimization problem is developed based on the information in the pay-off matrix, by solving which the knee can be obtained.     

A Philosophical Approach to the Concept of Data Model: Is a Data Model, in Fact, a Model?

The design of the database is crucial to the process of designing almost any Information System (IS) and involves two clearly identifiable key concepts: schema and data model, the latter allowing us to define the former. Nevertheless, the term model is commonly applied indistinctly to both, the confusion arising from the fact that in Software Engineering (SE), unlike in formal or empirical sciences, the notion of model has a double meaning of which we are not always aware. If we take our idea of model directly from empirical sciences, then the schema of a database would actually be a model, whereas the data model would be a set of tools allowing us to define such a schema.The present paper discusses the meaning of model in the area of Software Engineering from a philosophical point of view, an important topic for the confusion arising directly affects other debates where model is a key concept. We would also suggest that the need for a philosophical discussion on the concept of data model is a further argument in favour of institutionalizing a new area of knowledge, which could be called: Philosophy of Engineering.     

Effects of computers on Japanese schools

In this paper I consider how the computer can or should be accepted in Japanese schools. The concept of teaching in Japan stresses learning from a long-term perspective. Whereas in the instructional technology, on which the CAI or the Tutoring System depends, step-by-step attainments in relatively short time are emphasized. The former is reluctant in using the computer, but both share the Platonic perspective which are goal-oriented. However, The Socratic teacher, who intends to activate students' innate disposition to be better, would find another way of teaching and use of the computer.     

Recognition of handwritten musical notes by a modified Neocognitron

A neural network for recognition of handwritten musical notes, based on the well-known Neocognitron model, is described. The Neocognitron has been used for the what pathway (symbol recognition), while contextual knowledge has been applied for the where (symbol placement). This way, we benefit from dividing the process for dealing with this complicated recognition task. Also, different degrees of intrusiveness in learning have been incorporated in the same network: More intrusive supervised learning has been implemented in the lower neuron layers and less intrusive in the upper one. This way, the network adapts itself to the handwriting of the user. The network consists of a 13×49 input layer and three pairs of simple and complex neuron layers. It has been trained to recognize 20 symbols of unconnected notes on a musical staff and was tested with a set of unlearned input notes. Its recognition rate for the individual unseen notes was up to 93%, averaging 80% for all categories. These preliminary results indicate that a modified Neocognitron could be a good candidate for identification of handwritten musical notes.     

Unitary immersions of nonlinear systems

In this paper, we define what we call a unitary immersion of a nonlinear system. We observe that, for classical Hamiltonian systems, this notion contains, in some sense, the concept of quantization. We restrict our attention to degree-zero unitary immersions, where all observation functions must be represented by operators of the type multiplication by a function. We show that the problem of classifying such degree-zero unitary immersions of a given nonlinear system is not obvious. In some cases, we solve this problem.Chargé de Recherche au CNRS.Maître de Conférences.     

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.     

Über die Konvergenz des Relaxationsverfahrens bei nicht-negativen und diagonaldominanten Matrizen

Zusammenfassung In der folgenden Arbeit werden zunächst die Begriffe Gesamtschrittverfahren, Einzelschrittverfahren und Relaxationsverfahren allgemein formuliert und dann auf allgemeine lineare Gleichungssysteme angewandt. Im Spezialfall einer Matrix mit verschwindender Hauptdiagonale erhält man so die bekanntenJacobi-, Gauss-Seidel- und Relaxationsverfahren. Satz 1 macht eine Aussage über die Konvergenz des Einzelschrittverfahrens bei allgemeinen, nicht-negativen Matrizen. Der Beweis verläuft ähnlich wie in einem bereits 1948 vonStein undRosenberg [2] behandelten Spezialfall. Als Korollar ergibt sich eine Aussage über die Konvergenz des Relaxationsverfahrens bei nicht-negativen Matrizen. Es wird ferner der Satz 2 über die Konvergenz des Relaxationsverfahrens bei diagonaldominanten Matrizen beweisen.

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Summary In this paper we give a general definition what is meant by total-step-, single-step- and successive relaxation iterative method and we apply these concepts on systems of linear equations. In the special case of a matrix with zero diagonal entries we obtain the well knownJacobi-, Gauss-Seidel- and Relaxation iterative method. Theorem 1 gives conditions for the convergence of the singlestep-iterative method for general, non-negative matrices. The proof is similar to that given byStein andRosenberg in [2] (1948) for a special case. A corollary gives conditions for the convergence of the relaxation-iterative method for non-negative matrices. Further on we prove theorem 2 about the convergence of the relaxation-iterative method with diagonally dominant matrices.

     

The Use of Certain Equations of Mathematical Physics in Optimization of a Function on a Set. I

The results of application of potential theory to optimization are used to extend the use of (Helmholtz) diffusion and diffraction equations for optimization of their solutions (x, ) with respect to both x, and . If the aim function is modified such that the optimal point does not change, then the function (x, ) is convex in (x, for small . The possibility of using heat conductivity equation with a simple boundary layer for global optimization is investigated. A method is designed for making the solution U(x,t) of such equations to have a positive-definite matrix of second mixed derivatives with respect to x for any x in the optimization domain and any small t < 0 (the point is remote from the extremum) or a negative-definite matrix in x (the point is close to the extremum). For the functions (x, ) and U(x,t) having these properties, the gradient and the Newton–Kantorovich methods are used in the first and second stages of optimization, respectively.