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This paper describes two approaches that allow decomposition of structural design problems that enable structural design optimization to be performed on systems that are often seen as too large or complex to address in a single optimization. The COMPOSE method is shown to enable optimization of many tightly coupled subsystems despite the usual problem of non-convergence when a subsystem is repeatedly removed and optimized under the previously prevailing boundary conditions, then reinserted into the entire system model, which subsequently causes the boundary conditions experienced by the subsystem to change. Use of COMPOSE may allow reducing the number of whole-system evaluations necessary for component design from hundreds or thousands to fewer than ten, while still exploring the component design space extensively.Asecond approach, collaborative independent agents, is shown to address problems that have both large design spaces and time-intensive analyses, rendering them intractable to traditional methods. In an example problem, a set of loosely coupled optimization agents is shown to reduce dramatically the computing time needed to find good solutions to such problems. The savings result from the continuing transfer of results from rapid, low-refinement, less accurate search to agents that search at the full level of refinement and accuracy demanded of a solution of the problem, essentially providing them guidance as to what portions of the design space are likely to be worth searching in greater detail. [ABSTRACT FROM AUTHOR]

Well log data are routinely used for stratigraphic interpretation of the earth's subsurface. This paper investigates using a co-evolutionary fuzzy system to generate a well log interpreter that can automatically process well log data and interpret reservoir permeability. The methodology consists of 3 steps: 1) transform well log data into fuzzy symbols which maintain the character of the original log curves; 2) apply a co-evolutionary fuzzy system to generate a fuzzy rule set that classifies permeability ranges; 3) use the fuzzy rule set to interpret well logs and infer the permeability ranges. We present the developed techniques and test them on well log data collected from oil fields in offshore West Africa. The generated fuzzy rules give sensible interpretation. This result is encouraging in two respects. It indicates that the developed well log transformation method preserves the information required for reservoir properties interpretation. It also suggests that the developed co-evolutionary fuzzy system can be applied to generate well log interpreters for other reservoir properties, such as lithology. [ABSTRACT FROM AUTHOR]

We propose, theorize and implement the Recursive Pattern-based Hybrid Supervised (RPHS) learning algorithm. The algorithm makes use of the concept of pseudo global optimal solutions to evolve a set of neural networks, each of which can solve correctly a subset of patterns. The pattern-based algorithm uses the topology of training and validation data patterns to find a set of pseudo-optima, each learning a subset of patterns. It is therefore well adapted to the pattern set provided. We begin by showing that finding a set of local optimal solutions is theoretically equivalent, and more efficient, to finding a single global optimum in terms of generalization accuracy and training time. We also highlight that, as each local optimum is found by using a decreasing number of samples, the efficiency of the training algorithm is increased. We then compare our algorithm, both theoretically and empirically, with different recursive and subset based algorithms. On average, the RPHS algorithm shows better generalization accuracy, with improvement of up to 60% when compared to traditional methods. Moreover, certain versions of the RPHS algorithm also exhibit shorter training time when compared to other recent algorithms in the same domain. In order to increase the relevance of this paper to practitioners, we have added pseudo code, remarks, parameter and algorithmic considerations where appropriate. [ABSTRACT FROM AUTHOR]

Cognitive psychology works have shown that the cognitive representation of categories is based on a typicality notion: all objects of a category do not have the same representativeness, some are more characteristic or more typical than others, and better exemplify their category. Categories are then defined in terms of prototypes, i.e. in terms of their most typical elements. Furthermore, these works showed that an object is all the more typical of its category as it shares many features with the other members of the category and few features with the members of other categories. In this paper, we propose to profit from these principles in a machine learning framework: a formalization of the previous cognitive notions is presented, leading to a prototype building method that makes it possible to characterize data sets taking into account both common and discriminative features. Algorithms exploiting these prototypes to perform tasks such as classification or clustering are then presented. The formalization is based on the computation of typicality degrees that measure the representativeness of each data point. These typicality degrees are then exploited to define fuzzy prototypes: in adequacy with human-like description of categories, we consider a prototype as an intrinsically imprecise notion. The fuzzy logic framework makes it possible to model sets with unsharp boundaries or vague and approximate concepts, and appears most appropriate to model prototypes. We then exploit the computed typicality degrees and the built fuzzy prototypes to perform machine learning tasks such as classification and clustering. We present several algorithms, justifying in each case the chosen parameters. We illustrate the results obtained on several data sets corresponding both to crisp and fuzzy data. [ABSTRACT FROM AUTHOR]

In this paper we are proposing a novel fuzzy method that can handle imperfect knowledge in a broader way than Intuitionistic (in the sense of Atanassov) fuzzy logic does (IFL). This fuzzy method can manage non-contradictory, doubtful, and contradictory information provided by experts, providing a mediated solution, so we called it Mediative Fuzzy Logic (MFL). We are comparing results of MFL, with IFL and traditional Fuzzy logic (FL). [ABSTRACT FROM AUTHOR]

Copyright of Forging New Frontiers: Fuzzy Pioneers II is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract An event is a spatio-temporally localizable occurrence. Each event in our universe can be defined within a two-dimensional space in which one dimension is causality and the other is serendipity. Unfortunately, the majority of scientists in the Modern Era in their fascination with rules of causality and wanting to believe in a complete deterministic expression of the universe have banished all notions of serendipity to the realm of fiction, religion and/or, the occult. But the hegemony of Newtonian causality finally crumbled under the gravity of Heisenberg's Uncertainty Principle which demonstrated that an external observer can never acquire enough information to fully express the state of a system. This was a quantum physical expression of what was later eloquently put by Heidegger in his philosophical definition of Ereignis, to designate an unpredictable and often uncontrollable disruption of the spatio-temporal causal continuity. In the Postmodern Era, when events such as 9/11 occur beyond any assessable realm of causal relationships, we can no longer afford to discard the serendipity component of events if we wish to understand with clarity.Instead we must devise rules of conformity between the causal and non-causal fields of reality. Fuzzy Logic provides such a vigorous system of thinking that can lead us to this accord. This paper uses the tools of Fuzzy Logic to find pathways for events taking place within a causal-serendipity space. As a first approach, an event is defined on a hyperbolic path in which the degree of serendipity multiplied by the degree of causality is constant. This allows for the diminution of serendipity as scientific knowledge about a subject increases and the enhancement of serendipity to become dominant when data are scarce or measurements uncertain. The technique is applied to several different types of causality - direct, chain-like, parallel, and accumulation. [ABSTRACT FROM AUTHOR]

In this paper, we present an application for helping users to find new query terms in web retrieval via fuzzy association rules. Once the user has made an initial query, a set of documents is retrieved from the web. Representing these documents as text transactions, each item in the transaction means the presence of the term in the document. From the set of transactions, fuzzy association rules are extracted. Based on the thresholds of support and certainty factor, a selection of rules is carried out and the terms in those rules are offered to the user to be added to the query and to improve the retrieval. [ABSTRACT FROM AUTHOR]

Morphic Computing was first introduced by Resconi and Nikravesh [2006, 2007a, and 2007b] which makes use of Morphic and Morphogenetic Fields. Morphic Fields and its subset Morphogenetic Fields have been at the center of controversy for many years in mainstream science and the hypothesis is not accepted by some scientists who consider it a pseudoscience. Resconi and Nikravesh [2006, 2007a, and 2007b] claim that Morphic Computing is a natural extension of Holographic Computation, Quantum Computation, Soft Computing, and DNA Computing. In this paper, we introduce extensions of the Morphic Computing such as Quantum Logic and Entanglement in Morphic Computing, Morphic Systems and Morphic System of Systems (M-SOS). Its applications to the field of computation by words as an example of the Morphic Computing, Morphogenetic Fields in neural network and Morphic Computing, Morphic Fields - concepts and Web search, and agents and fuzzy in Morphic Computing will also be discussed. [ABSTRACT FROM AUTHOR]

Searching database records, ranking the results and making decision based on multi-criteria queries is central for many database applications and information systems used within organizations in finance, business, industrial and other fields. Most of the available systems are modeled using crisp logic and queries, which result in rigid systems with imprecise results. In this paper, we introduce an intelligent decision and information support based on evolutionary-based optimization as a flexible tool allowing approximation where the selected objects do not need to match exactly the decision criteria resembling natural human behavior. [ABSTRACT FROM AUTHOR]

Neural networks and traditional classifiers work well for optical character recognition; however, it is advantageous to combine the results of several algorithms to improve classification accuracies. This paper presents a combination method based on the Dempster-Shafer theory of evidence, which uses statistical information about the relative classification strengths of several classifiers. Numerous experiments show the effectiveness of this approach. The method allows 15—30% reduction of misclassification error compared to the best individual classifier. [ABSTRACT FROM AUTHOR]

In this paper, the problem of classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of Dempster-Shafer theory. Each neighbor of a sample to be classified is considered as an item of evidence that supports certain hypotheses regarding the class membership of that pattern. The degree of support is defined as a function of the distance between the two vectors. The evidence of the k nearest neighbors is then pooled by means of Dempster's rule of combination. This approach provides a global treatment of such issues as ambiguity and distance rejection, and imperfect knowledge regarding the class membership of training patterns. The effectiveness of this classification scheme as compared to the voting and distance-weighted k-NN procedures is demonstrated using several sets of simulated and real-world data [ABSTRACT FROM AUTHOR]

In this paper, we describe an abstract framework and axioms under which exact local computation of marginals is possible. The primitive objects of the framework are variables and valuations. The primitive operators of the framework are combination and marginalization. These operate on valuations. We state three axioms for these operators and we derive the possibility of local computation from the axioms. Next, we describe a propagation scheme for computing marginals of a valuation when we have a factorization of the valuation on a hypertree. Finally we show how the problem of computing marginals of joint probability distributions and joint belief functions fits the general framework. [ABSTRACT FROM AUTHOR]

This paper studies belief functions, set functions which are normalized and monotone of order 8. The concepts of continuity and condensability are defined for belief functions, and it is shown how to extend continuous or condensable belief functions from an algebra of subsets to the corresponding power set. The main tool used in this extension is the theorem that every belief function can be represented by an allocation of probability i.e., by a n-homomorphism into a positive and completely additive probability algebra. This representation can be deduced either from an integral representation due to Choquet or from more elementary work by Revuz and Honeycutt. [ABSTRACT FROM AUTHOR]

Many knowledge-based expert systems employ numerical schemes to represent evidence, rate competing hypotheses, and guide search through the domain's problem space. This paper has two objectives: first, to introduce one such scheme, developed by Arthur Dempster and Glen Shafer, to a wider audience; second, to present results that can reduce the computation-time complexity from exponential to linear, allowing this scheme to be implemented in many more systems. In order to enjoy this reduction, some assumptions about the structure of the type of evidence represented and combined must be made. The assumption made here is that each piece of the evidence either confirms or denies a single proposition rather than a disjunction. For any domain in which the assumption is justified, the savings are available. [ABSTRACT FROM AUTHOR]

This paper, presents, results, of, the, application, to epistemic, logic structures of the method proposed by Carnap for the development of logical foundations of probability theory. These results, which provide firm conceptual bases for the Dempster-Shafer calculus of evidence, are derived by exclusively using basic concepts from probability and modal logic theories, without resorting to any other theoretical notions or structures. A form of epistemic logic (equivalent in power to the modal system S5), is used to define a space of possible worlds or states of affairs. This space, called the epistemic universe, consists of all possible combined descriptions of the state of the real world and of the state of knowledge that certain rational agents have about it. These representations generalize those derived by Carnap, which were confined exclusively to descriptions of possible states of the real world. Probabilities defined on certain classes of sets of this universe, representing different states of knowledge about the world, have the properties of the major functions of the Dempster-Shafer calculus of evidence: belief functions and mass assignments. The importance of these epistemic probabilities lies in their ability to represent the effect of uncertain evidence in the states of knowledge of rational agents. Furthermore, if an epistemic probability is extended to a probability function defined over subsets of the epistemic universe that represent true states of the real world, then any such extension must satisfy the well-known interval bounds derived from the Dempster-Shafer theory. Application of this logic-based approach to problems of knowledge integration results in a general expression, called the additive combination formula, which can be applied to a wide variety of problems of integration of dependent and independent knowledge. Under assumptions of probabilistic independence this formula is equivalent to Dempster's rule of combination. [ABSTRACT FROM AUTHOR]

Copyright of Classic Works of the Dempster-Shafer Theory of Belief Functions is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although informal models of evidential reasoning have been successfully applied in automated reasoning systems, it is generally difficult to define the range of their applicability. In addition, they have not provided a basis for consistent management of evidence bearing on hypotheses that are related hierarchically. The Dempster-Shafer (D-S) theory of evidence is appealing because it does suggest a coherent approach for dealing with such relationships. However, the theory's complexity and potential for computational inefficiency have tended to discourage its use in reasoning systems. In this paper we describe the central elements of the D-S theory, basing our exposition on simple examples drawn from the field of medicine. We then demonstrate the relevance of the D-S theory to a familiar expert-system domain, namely the bacterial-organism identification problem that lies at the heart of the mycin system. Finally, we present a new adaptation of the D-S approach that achieves computational efficiency while permitting the management of evidential reasoning within an abstraction hierarchy [ABSTRACT FROM AUTHOR]

This paper redefines the concept of sampling from a population with a given parametric form, and thus leads up to some proposed alternatives to the existing Bayesian and fiducial arguments for deriving posterior distributions. Section 2 spells out the basic assumptions of the suggested class of sampling models, and Sect. 3 suggests a mode of inference appropriate to the sampling models adopted. A novel property of these inferences is that they generally assign upper and lower probabilities to events concerning unknowns rather than precise probabilities as given by Bayesian or fiducial arguments. Sections 4 and 5 present details of the new arguments for binomial sampling with a continuous parameter p and for general multinomial sampling with a finite number of contemplated hypotheses. Among the concluding remarks, it is pointed out that the methods of Sect. 5 include as limiting cases situations with discrete or continuous observables and continuously ranging parameters. [ABSTRACT FROM AUTHOR]

A limitation of future development is the lack of ability to use technical equipment. To avoid this problem it is necessary to concentrate on pedagogy of technology - how to teach and learn technology? The paper also tries to break down some attitudes in both sides of teaching technology. The problem is mainly based on the lack of conversation and understanding between two sciences: Technology and Pedagogy. The aim of this paper is to help this conversation to begin and continue - this acts as a transmitter between these sciences. Paper explains basic ideas of both sciences with such language that the other is able to understand. [ABSTRACT FROM AUTHOR]

A simple technique is proposed to predict the variation of the bearing capacity of an unsaturated soil with respect to matric suction. This technique is based on extending conventional bearing capacity theory proposed by Terzaghi. The proposed equation in this paper is presented as a functional relationship such that the variation of the bearing capacity of an unsaturated soil with respect to matric suction can be predicted. This technique is developed extending the concepts for predicting the shear strength of unsaturated soils proposed by Vanapalli et al. (1996). Using the approach presented in this paper, the bearing capacity of an unsaturated soil can be predicted using the saturated shear strength parameters, c' and π' and the soil-water retention curve (SWRC). [ABSTRACT FROM AUTHOR]

In order to obtain the retention curve of unsaturated soil which is defined as the relationship between water content and suction, numerous laboratory tests have been done for many soils with different clay contents. The laboratory tests concern different types of soils; the swelling and not swelling clays (natural clay with high silt content and the bentonite clay). Two complementary experimental ways are conducted. The first which leads to measure suction is based on the filter paper method. The second with the dessicator allows to impose a high suction values. The paper discusses the theoretical approaches for the grain-size distribution and water retention curve's relation. It discusses also their correlations according to each type of tested soil. The experimental data are used to validate the theoretical model selected in the study. The influence of the different parameters used by the model is also studied. All the parameters required to run the model can be obtained by performing independent, common laboratory tests and are related to the physical parameters like the initial dry density and the initial void ratio. After some numerical tests, the direct procedure to obtain the soil-water characteristic curve is improved. [ABSTRACT FROM AUTHOR]

In the following paper an influence of several state parameters on one dimensional time-dependent material behaviour of an unsaturated soil is presented. For identification of parameter values, laboratory experiments were performed. Standard oedometric compression tests were carried out to investigate stress-strain-time relationships. Apart from the degree of saturation the initial void ratio vary as parameters. For measuring the matrix suction a tensiometer is mounted inside the specimen. A detailed description of the equipment used and the test procedures are given in the paper. The tests reported here were carried out with a kaolinite to determine parameter values, depending on different state variables. The characteristic behaviour of unsaturated samples observed during consolidation and creeping is shown in a series of figures. Parameters like Cc, Cs and Cα are used in constitutive model that can simulate both consolidation processes as well as creep phenomenon for partially saturated clay in a single consistent analysis. [ABSTRACT FROM AUTHOR]

This paper presents results of an experimental study on shear strength behaviour of an unsaturated silty soil. A comprehensive set of laboratory experiments have been undertaken in a double-walled triaxial cell (and a conventional cell for saturated samples) on samples of a compacted silty soil. In the experiments the soil samples were subjected to isotropic consolidation followed by unloading and subsequent anisotropic reloading (shearing) under constant suctions. Volume change and shear strength data for the samples were monitored continuously during the experiments. In this paper, the results of the experimental study will be presented and the effects of suction on the shear strength and volume change of unsaturated soil will be discussed in detail. [ABSTRACT FROM AUTHOR]

Coarse granular materials such as rockfill have been described in recent years by means of strain and work hardening plasticity models Oldecop and Alonso (2001), Chàvez and Alonso (2003), Alonso et al. (2005) which require a proper definition of the flow rule. The paper presents experimental information on dilatancy rates obtained in suction controlled triaxial tests performed on large diameter (25 cm) specimens of compacted coarse gravels of hard limestone. Tests involved confining stresses in the range 0.3-1 MPa and Relative Humidity in the range 10-100%. Unloading-reloading during the tests provided information on the elastic response. Volumetric and deviatoric plastic strains rates were determined from records of total vertical and volumetric deformations. In general, a steady (critical) state was not found. Plots have been prepared showing the variation of the plastic flow direction, d, with a number of variables: vertical deformation, stress ratio, η, confining stress, p, plastic work, Wp, suction, s, and some derived normalized variables. It was found that the common framework of dilatancy being a function of the current stress ratio and the limiting stress ratio is far from being appropriate to describe the behaviour of the tested aggregates. It was found that relationships d=f

This paper presents results when using a tensiometer designed in the Soil Mechanics Laboratory at COPPE/UFRJ, and several other instruments. This new instrument measures suction values up to 1500 kPa. In more typical tensiometers, the cavitation of the system hinders the measurement of suction with values over 100 kPa. Ridley and Burland (1993) designed a new model of tensiometer to measure suction of more than 100 kPa. Mahler et al. (2002) presented a new tensiometer with which suction values up to 350 kPa were measured. This paper introduces an updated model of a tensiometer, using a variation of the equipment proposed by Mahler et al. (2002). The updated model worked extremely well and cost little to build. The equipment used allows suction to be measured relatively quickly and, as previously stated, is economical to produce (about US$300). To date, the range of suction levels that can be measured reaches 1500 kPa with no difficulty. The tests were carried out in two boxes of 160× 50× 60 cm under laboratory conditions. The equipment used included a 15 m.c.a. ceramic block pressure sensor, de-aerated water and a special acrylic tube specifically designed for this instrument. The results were compared to two simple automated tensiometers and equivalent tensiometers. In general very good results were obtained. The main final remarks so far are as follows: • the high bubble air entry of the ceramic block inhibits the presence of air bubbles, but the response time is slower for suction values of more than 200 kPa; • the saturation process used for the ceramic stone worked very well; • as expected, the position of the equivalence tensiometer influences the value measured; • the mini-lysimeter system proved to be a very good alternative for laboratory tests and for the development of instruments that measure suction; • the new instrument presented herein proved to be a good and an economical alternative for measuring matrix suction in the soil. [ABSTRACT FROM AUTHOR]

This chapter focuses on associativememories in recurrent artificial neural networks using the same kind of very simple neurons usually found in neural nets. The past 25 years have dedicated much of the research endeavor on coding the information in fixed point attractors. From a cognitive or neurophysiological point of view, this choice is rather arbitrary. This paper justifies the need to switch to another encoding mechanism exploiting limit cycles and complex dynamics in the background rather than fixed points. It is shown how these attractors encompass in many aspects the limitations of fixed points: better correspondence with neurophysiological facts, increase of the encoding capacity, improved robustness during the retrieval phase, decrease in the number of spurious attractors. However, how to exploit and learn these cycles for encoding the relevant information is still an open issue. In this paper two learning strategies are proposed, tested and compared, one rather classical, very reminiscent of the usual supervised hebbian learning, the other one, rather original since allowing the coding attractor to be chosen by the network itself. Computer experiments of these two learning strategies will be presented and explained. The second learning mechanism will be advocated both for its highly cognitive relevance and on account of its much better performance in encoding and retrieving the information. Since the kind of dynamics observed in our experiments (cyclic attractor when a stimulus is presented and a weak background chaos in the absence of such stimulation) faithfully reminds neurophysiological data and although no straightforward applications have been found so far, we limit our justification to this qualitative mapping with brain observations and the need to better explore how a physical device such as a brain can store and retrieve in a robust way a huge quantity of information. [ABSTRACT FROM AUTHOR]

An approach to intelligence and reasoning is developed for the brain. The need for such an approach to Computational Intelligence is argued for on ethical grounds. The paper then considers various components of information processing in the brain, choosing attention, memory and reward as key (language cannot be handled in the space available). How these could then be used to achieve cognitive faculties and ultimately reasoning are then discussed, and the paper concludes with a brief analysis of reasoning tasks, including the amazing powers of Betty the Crow. [ABSTRACT FROM AUTHOR]

Many machine learning methods just consider the quality of prediction results as their final purpose. To make the prediction process transparent (reversible), spline kernel based methods were proposed by Gunn. However, the original solution method, termed SUpport vector Parsimonious ANOVA (SUPANOVA) was computationally very complex and demanding. In this paper, we propose a new heuristic to compute the optimal sparse vector in SUPANOVA that replaces the original solver for the convex quadratic problem of very high dimensionality. The resulting system is much faster without the loss of precision, as demonstrated in this paper on two benchmarks: the iris data set and the Boston housing market data benchmark. [ABSTRACT FROM AUTHOR]

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a Hierarchical Genetic Algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Clustering is a widely applied tool of data mining to detect the hidden structure of complex multivariate datasets. Hence, clustering solves two kinds of problems simultaneously, it partitions the datasets into cluster of objects that are similar to each other and describes the clusters by cluster prototypes to provide some information about the distribution of the data. In most of the cases these cluster prototypes describe the clusters as simple geometrical objects, like spheres, ellipsoids, lines, linear subspaces etc., and the cluster prototype defines a special distance function. Unfortunately in most of the cases the user does not have prior knowledge about the number of clusters and not even about the proper shape of prototypes. The real distribution of data is generally much more complex than these simple geometrical objects, and the number of clusters depends much more on how well the chosen cluster prototypes fit the distribution of data than on the real groups within the data. This is especially true when the clusters are used for local linear modeling purposes. The aim of this paper is not to define a new distance norm based on a problem dependent cluster prototype but to show how the so called geodesic distance that is based on the exploration of the manifold the data lie on, can be used in the clustering instead of the classical Euclidean distance. The paper presents how this distance measure can be integrated within fuzzy clustering and some examples are presented to demonstrate the advantages of the proposed new methods. [ABSTRACT FROM AUTHOR]

The paper describes the application of an artificial neural network in natural language text reasoning. The task of knowledge discovery in text from a database, represented with a database file consisting of sentences with similar meanings but different lexico-grammatical patterns, was solved with the application of neural networks which recognize the meaning of the text using designed training files. We propose a new method for natural language text reasoning that utilizes three-layer neural networks. The paper deals with recognition algorithms of text meaning from a selected source using an artificial neural network. In this paper we present that new method for natural language text reasoning and also describe our research and tests performed on the neural network. [ABSTRACT FROM AUTHOR]

Probability and possibility are both convenient scales of uncertainty, because they are defined by a distribution function. They also have complementary properties in the sense that probability is a quantitative and objective ratio scale, while possibility is a qualitative and subjective ordinal scale. The paper discusses probabilistic and possibilistic causal models with a time-series effect from the viewpoint of Evidence theory, and shows that they can be defined by a single equation with different conditions of focal elements using the basic probability assignments. The equation could be recognized as a causal model with a general representation of uncertainty in the form of Evidence theory. The paper finalizes the discussion with the properties of the generalized uncertain causal model. [ABSTRACT FROM AUTHOR]

This paper is a result of the application of soft computing technologies to solve the pick up and delivery problem (PDP). In this paper, we consider a practical PDP that is frequently encountered in the real-world logistics operations, such as Helicopter Offshore Crew Transportation of Oil & Gas Company. We consider a typical scenario of relatively large number of participants, about 70 persons and 5 helicopters. Logistics planning turns to be a combinatorial problem, and that makes it very difficult to find reasonable solutions within a short computational time. We present an algorithm based on two optimization techniques, genetic algorithms and heuristic optimization. Our solution is tested on an example with a known optimal solution, and on actual data provided by PEMEX, Mexican Oil Company. Currently, the algorithm is implemented as part of the system for simulation and optimization of offshore logistics called SMART-Logistics and it is at a field-testing phase. [ABSTRACT FROM AUTHOR]

This paper describes results concerning the capability of supervised machine learning techniques to predict production potential for a single formation, prior to drilling, over a 16,000 square mile area of SE New Mexico. In this paper a neural network is used to predict production potential for a single formation of SE New Mexico region. The process involved gathering data for use as potential inputs, collecting production data at known wells, selecting optimal inputs, developing and testing various network architectures, making predictions, analyzing and applying the results. This predicted production was further refined by excluding production at locations where the Woodford shale was not present. Results were evaluated by inspecting a map of predicted production and performing statistical testing, including a correlation of predicted and actual production, which produced a correlation coefficient of 0.79. The results were then used by the Devonian FEE Tool, an expert system designed to reduce exploration risk. [ABSTRACT FROM AUTHOR]

As data is getting more complex and complicated, it is increasingly difficult to construct an effective complex decision model. Two very obvious examples where such a need emerges are in the economic and the medical fields. This paper presents the fuzzy signature and cognitive modeling approach which could improve such decision models. Fuzzy signatures are introduced to handle complex structured data and problems with interdependent features. A fuzzy signature can also be used in cases where data is missing. The proposed fuzzy signature structure will be used in problems that fall into this category. This paper also investigates a novel cognitive model to extend the usage of fuzzy signatures. This Fuzzy Cognitive Signature Modelling will enhance the usability of fuzzy theory in modelling complex systems as well as facilitating complex decision making process based on ill structured information or data. [ABSTRACT FROM AUTHOR]

In this paper, we propose the solution method about the multiobjective portfolio selection problem, particularly the scenario model to include the ambiguous factors and chance constraints. Generally, mathematical programming problems with probabilities and possibilities are called to stochastic programming problem and fuzzy programming problem, and it is difficult to find its global optimal solution. In this paper, we manage to develop the efficient solution method to find its global optimal solution of such a problem introducing the some subproblems. [ABSTRACT FROM AUTHOR]

This paper presents a monitoring methodology to identify complex systems faults. This methodology combines the production of meaningful error signals (residuals) obtained by comparison between the model outputs and the system outputs, with a posterior fuzzy classification. In a first off-line phase (learning) the classification method characterises each fault. In the recognition phase, the classification method identifies the faults. The chose classification method permits to characterize faults non included in the learning data. This monitoring process avoids the problem of defining thresholds for faults isolation. The residuals analysis and not the system variables themselves, permit us to separate fault recognition from system operation point influence. The paper describes the proposed methodology using a benchmark of a two interconnected tanks system. [ABSTRACT FROM AUTHOR]

Morphic Computing is based on Field Theory and more specifically Morphic Fields. In this paper, we introduce extensions of Morphic Computing to non classical fields and logic.Morphic Computing change or compute non physical conceptual fields. One example is in representing the semantics of words. In this paper, application to the field of computation by words as an example of the Morphic Computing, Morphic Fields - concepts and Web search, and agents and fuzzy in Morphic Computing will be discussed. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new type of computation called "Morphic Computing". Morphic Computing is based on Field Theory and more specifically Morphic Fields. Morphic Fields were first introduced by Rupert Sheldrake [1981] from his hypothesis of formative causation that made use of the older notion of Morphogenetic Fields. In this paper, we introduce the basis for this new computing paradigm. [ABSTRACT FROM AUTHOR]

In this paper, we go beyond the traditional web search engines that are based on keyword search and the Semantic Web which provides a common framework that allows data to be shared and reused across application,. For this reason, our view is that "Before one can use the power of web search the relevant information has to be mined through the concept-based search mechanism and logical reasoning with capability to Q&A representation rather than simple keyword search". In this paper, we will focus on development of a framework for reasoning and deduction in the web. A new web search model will be presented. One of the main core ideas that we will use to extend our technique is to change terms-documents-concepts (TDC) matrix into a rule-based and graph-based representation. This will allow us to evolve the traditional search engine (keyword-based search) into a concept-based search and then into Q&A model. Given TDC, we will transform each document into a rule-based model including it's equivalent graph model. Once the TDC matrix has been transformed into maximally compact concept based on graph representation and rules based on possibilistic relational universal fuzzy-type II (pertaining to composition), one can use Z(n)-compact algorithm and transform the TDC into a decision-tree and hierarchical graph that will represents a Q&A model. Finally, the concept of semantic equivalence and semantic entailment based on possibilistic relational universal fuzzy will be used as a basis for question-answering (Q&A) and inference from fuzzy premises. This will provide a foundation for approximate reasoning, language for representation of imprecise knowledge, a meaning representation language for natural languages, precisiation of fuzzy propositions expressed in a natural language, and as a tool for Precisiated Natural Language (PNL) and precisation of meaning. The maximally compact documents based on Z(n)-compact algorithm and possibilistic relational universal fuzzy-type II will be used to cluster the documents based on concept-based query-based search criteria. [ABSTRACT FROM AUTHOR]

Search keywords can be considered as decision criteria (goals), whereas the documents contained in the original result list of a search engine can be considered as decision alternatives in the sense of adequate search results. The paper shows how a decision making model based on interactions between decision goals can be used for re-ranking of the search results obtained by classical search engines. The decision making model was previously applied to real world problems in production optimization and business process management. In contrast to other approaches, the interactive structure of decision goals for each decision situation is calculated explicitly based on fuzzy types of interaction. In this paper, after a brief description of the model an application to the calculation of context depending re-rankings of search results of internet search engines is presented. It is shown that keywords of search queries can be understood as decision goals and that the decision making model can be successfully applied for context dependent re-ranking of the search results. Using the model, popularity based rankings can be re-ranked making use of context dependent information derived from the query keywords. [ABSTRACT FROM AUTHOR]

This paper is devoted to the evaluation of quantified statements in the context of flexible querying of relational databases. Two types of quantified statements can be distinguished and the most sophisticated one (of type "QB X are A") is considered in this paper. The contribution is to propose a new theoretical background for their evaluation (using an arithmetic on gradual numbers (ℕf, ℤf, ℚf)). [ABSTRACT FROM AUTHOR]

With more Web services emerging on Internet, it becomes a challenge for Web Service community to integrate existing Web services to meet the complex requirement of users with little intervention of human. Although semantics added into Web service promises us more powerful measures to automate the service discovering, matching, and integrating, how to realize the process of automatic Service Composition with semantics is still an urgent problem. In virtue of the fruits of the study of behaviorism, this paper analyzes and models the complex services. And through the behavior analysis and behavior partition, this paper reaches a better combination of services, reasoning algorithms with architecture-Service Agent Community(SAC), and makes Service Composition more automatically and intelligently. [ABSTRACT FROM AUTHOR]

Recently, mobile RFID (Radio Frequency Identification) services such as in smart poster or supply chain are rapidly growing up as a newly generating industry. Here, mobile RFID service is defined as a special type of mobile service using RFID tag packaging object and RFID reader attached mobile RFID terminal. This paper is to provide an approach for ensuring RFID net-work security based on security engineering method. To ensure secure mobile RFID service network, we describes an approach which includes security state and security flow. We propose an efficient phone-based middleware platform architecture which is constructed by mobile RFID security mechanism based on WIPI (Wireless Internet Platform for Interoperability). WIPI-based light-weight mobile RFID security platform can be applied to various mobile RFID services that need secure business in mobile environment. So, we will propose the way to protect the personal privacy effectively using privacy preference for secure mobile RFID systems in ubiquitous network on this paper. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Web Decision Support System for Impacts Assessment of Urban Mobility (WDSS4IA) which is based on a new approach. The latter uses fuzzy set theory for modeling criteria, belief theory for evaluations fusion from various information sources and it is able to handle uncertainty and vagueness. In this paper, the WDSS4IA is presented, focusing on its conception, modules and functionalities. [ABSTRACT FROM AUTHOR]

The problem of knowledge sharing is eminent in the P2P area. In this paper, we propose a general framework, called semantic community, which is conceived for supporting dynamic ontology-based knowledge sharing and evolution in P2P systems. The knowledge sharing and evolution processes in semantic community are based on peer ontologies which are describing the knowledge of each peer, and on interactions among peers. This paper exploits an ontology matching algorithm to identify semantic affinity of two concepts. According to the semantic affinity threshold, the founder has to evaluate which peers are admitted in the community. [ABSTRACT FROM AUTHOR]

An important challenge regarding resource selection in Grid is how to cope with the wide range of selection and the high degree of strangeness. In this paper, reputation evaluation mechanism, as a fundamental building block technique, is introduced to leverage the guarantee of trustworthiness and reliability. Guided by the reputation evaluation ideas and fuzzy logic, the proposed approach can better handle uncertainty, fuzziness, and incomplete information, so the security of information service is highly strengthened. By applying the approach using eBay transaction statistical data, the paper demonstrate the final integrative decision order in various conditions. Compared with other methods, this approach has better overall consideration, accord with human selection psychology naturally. [ABSTRACT FROM AUTHOR]

Making search engines responsive to human needs requires understanding user intention when submitting a query. Intention, context and situation are intimately connected [8]. Thus context modelling is paramount when mining search engine logs but the process should be sistematized and standarized for the future generation of autonomous data mining components. In this paper, we propose to integrate context as metadata represented in PMML. The complete knowledge discovery process would benefit from the metadata and in particular final and intermediate evaluations can use this information for interpretation. The paper also presents results of integration of GUMO ontology to conceptualize user context to improve interpretation of query mining on the weblogs of the site search engine. [ABSTRACT FROM AUTHOR]

This paper describes the part of a recommendation system designed for the recognition of film reviews (RRSS). Such a system allows the automatic collection, evaluation and rating of reviews and opinions of the movies. First the system searches and retrieves texts supposed to be movie reviews from the Internet. Subsequently the system carries out an evaluation and rating of the movie reviews. Finally, the system automatically associates a digital assessment with each review. The goal of the system is to give the score of reviews associated with the user who wrote them. All of this data is the input to the cognitive engine. Data from our base allows the making of correspondences, which are required for cognitive algorithms to improve, advanced recommending functionalities for e-business and e-purchase websites. In this paper we will describe the different methods on automatically identifying opinions using natural language knowledge and techniques of classification. [ABSTRACT FROM AUTHOR]