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When performing quality inspection of recycled paper one phenomenon of concern is the appearance of macroscopic undulations on the paper sheet surface that may emerge shortly or some time after its production. In this paper we explore the detection and measurement of this defect by means of computer vision and statistical pattern recognition techniques that may allow early detection at the production site. We propose features computed from Gabor Filter Banks (GFB) and Discrete Wavelet Transforms (DWT) for the characterization of paper sheet surface bumpiness in recycled paper images. The lack of a precise definition of the defect and the great variability of the sheet deformation shapes and scales, both within each image and between images, introduce additional difficulties to the problem. We obtain, with both proposed modeling approaches (GFB and DWT), classification accuracies are comparable to the agreement between human observers. The best performance is obtained using DWT features. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of a semantic annotation system, named Cerno, to analyze research publications in electronic format. Specifically, we address the problem of providing automatic support for authors who need to deal with large volumes of research documents. To this end, we have developed Biblio, a user-friendly tool based on Cerno. The tool directs the user's attention to the most important elements of the papers and provides assistance by generating automatically a list of references and an annotated bibliography given a collection of published research articles. The tool performance has been evaluated on a set of papers and preliminary evaluation results are promising. The backend of Biblio uses a standard relational database to store the results. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

In this paper we review two well-known citation methods to find relatedness between scientific papers: co-citation and bibliographic coupling. We propose a practical method to estimate the co-citation relatedness using the Google search engine. We call this method Web co-citation. We conducted experiments on a collection of scientific papers to compare the performances of different methods. The experimental results show that our approach, despite its simplicity, is efficient in discovering the relatedness between scientific papers. [ABSTRACT FROM AUTHOR]

In this tutorial paper we summarise the key features of the multi-threaded Qu-Prolog language for implementing multi-threaded communicating agent applications. Internal threads of an agent communicate using the shared dynamic database used as a generalisation of Linda tuple store. Threads in different agents, perhaps on different hosts, communicate using either a thread-to-thread store and forward communication system, or by a publish and subscribe mechanism in which messages are routed to their destinations based on content test subscriptions. We illustrate the features using an auction house application. This is fully distributed with multiple auctioneers and bidders which participate in simultaneous auctions. The application makes essential use of the three forms of inter-thread communication of Qu-Prolog. The agent bidding behaviour is specified graphically as a finite state automaton and its implementation is essentially the execution of its state transition function. The paper assumes familiarity with Prolog and the basic concepts of multi-agent systems. [ABSTRACT FROM AUTHOR]

This paper is a tutorial describing the main features of the KGP (Knowledge-Goals-Plan) model of agency and giving user guidance on how the model can be used to develop applications. The KGP model is based on computational logic. It consists of an abstract component, a computational component and an implementation. This paper concentrates on the abstract component, which consists of formal specifications of a number of different modules, including the knowledge bases, capabilities, transitions and control. For each of these we summarise what is provided by the model, and through the platform implementing the model, and what is left to the users to specify according to the requirements of the applications for which they wish to use the KGP model to design agents. [ABSTRACT FROM AUTHOR]

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBASCuBA: Subspace Clustering Based Analysis.) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers. [ABSTRACT FROM AUTHOR]

Office workers tend to produce paper piles of documents to read or to process sometime later. The information contained in these piles is often lost if it is not transferred to electronic format and connected to knowledge structures. Information that is not part of the knowledge worker's electronic information space is frequently overlooked because it is not proactively provided during actual processes or tasks he is involved in. This paper presents a novel prototype for an intelligent office appliance, which results from an integration of three state-of-the-art office applications/appliances: a workflow system, a document classification system, and a multi-functional peripheral. The resulting system allows for leveraging an office worker's papers to her personal knowledge space in order to realize a pro-active and context-sensitive information support within knowledge-intensive tasks and processes. [ABSTRACT FROM AUTHOR]

To facilitate the transition from paper-based patient record (PPR) to computer-based patient record (CPR), engineers should try to improve the usability of CPR system. From the point of view of a physician, we reviewed PPRs written by 8 Japanese physicians. We revealed the characteristics of PPR to find out about the barrier to transition from PPR to CPR. Our findings may be helpful to the engineers who are aiming to develop a CPR system. [ABSTRACT FROM AUTHOR]

In this paper we describe the design of a hybrid search that combines simple metadata search with a traditional keyword search over unstructured context data. This paradigm provides the inquirer additional options to narrow the search with some semantic aspects through the XML metadata query. We demonstrate a paper search for a case study of the hybrid search, and describe a data integration hub to integrate those data dispersed on the Net. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

Amongst several fundamental aspects in multi-agent systems design, the definition of the agent interaction space is of the utmost importance. The specification of the agent interaction has several facets: syntax, semantics, and compliance verification. In an open society, heterogenous agents can participate without showing any credentials. Accessing their internals or their knowledge bases is typically impossible, thus it is impossible to prove a priori that agents will indeed behave according to the society rules. Within the SOCS (Societies Of ComputeeS) project, a language based on abductive semantics has been proposed as a mean to define interactions in open societies. The proposed language allows the designer to define open, extensible and not over-constrained protocols. Beside the definition language, a software tool has been developed with the purpose of verifying at execution time if the agents behave correctly with respect to the defined protocols. This paper provides a tutorial overview of the theory and of the tools the SOCS project provided to design, define and test agent interaction protocols. [ABSTRACT FROM AUTHOR]

This paper is based on the tutorial given as part of the tutorial programme of CLIMA-VI. The tutorial aimed at giving an overview of the various features available in Jason, a multi-agent systems development platform that is based on an interpreter for an extended version of AgentSpeak. The BDI architecture is the best known and most studied architecture for cognitive agents, and AgentSpeak is an elegant, logic-based programming language inspired by the BDI architecture. [ABSTRACT FROM AUTHOR]

Privacy-preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix this paper proposes a novel privacy-preserving classification mining algorithm which suits all data types, arbitrary probability distribution of original data, and perturbing all attributes (including label attribute). Experimental results demonstrate that decision tree built using this algorithm on perturbed data has comparable classifying accuracy to decision tree built using un-privacy-preserving algorithm on original data. [ABSTRACT FROM AUTHOR]

This paper presents the experimental aspects of a robotic system used for detection, sorting and grading of paper objects from visual features. The experimental system is used as an automated paper recycling system. The system uses simplified stereo vision to compute position estimate of unknown objects. A vector of geometrical and textural features is used for sorting the paper, according to its grade. The grading process uses additional contact sensors for refining grading from visual features. Supervised learning is used for training the decision system to separate paper from other objects. The experimental aspects of the training process are presented in detail. A special vacuum gripper was designed for gripping paper objects detected by the vision system. The experimental results prove the efficiency of the proposed techniques. [ABSTRACT FROM AUTHOR]

SimStudent is a machine-learning agent that learns cognitive skills by demonstration. It was originally developed as a building block of the Cognitive Tutor Authoring Tools (CTAT), so that the authors do not have to build a cognitive model by hand, but instead simply demonstrate solutions for SimStudent to automatically generate a cognitive model. The SimStudent technology could then be used to model human students' performance as well. To evaluate the applicability of SimStudent as a tool for modeling real students, we applied SimStudent to a genuine learning log gathered from classroom experiments with the Algebra I Cognitive Tutor. Such data can be seen as the human students' "demonstrations" of how to solve problems. The results from an empirical study show that SimStudent can indeed model human students' performance. After training on 20 problems solved by a group of human students, a cognitive model generated by SimStudent explained 82% of the problem-solving steps performed correctly by another group of human students. [ABSTRACT FROM AUTHOR]

In this article I will present an overview of a selection of tools for execution and proof based on temporal logic, and outline both the general techniques used and problems encountered in implementing them. This selection is quite subjective, mainly concerning work that has involved researchers I have collaborated with at Liverpool (and, previously, Manchester). The tools considered will mainly be theorem-provers and (logic-based) agent programming languages. Specifically: - clausal temporal resolution [21, 28] and several of its implementations, namely , and , together with its application to verification [35]; - executable temporal logics [24, 4] and its implementation as both MetateM [3] and Concurrent MetateM [22, 49], together with its use as a programming language for both individual agents [23, 26, 29] and multi-agent systems [33, 30, 32]. In addition, I will briefly mention work on induction-based temporal proof [5], temporal logic programming [1], and model checking [7]. Rather than providing detailed algorithms, this presentation will concentrate on general principles, outlining current problems and future possibilities. The aim here is to give the reader an overview of the ways we handle temporal logics. In particular how we use such logics as the basis for both programming and verification. [ABSTRACT FROM AUTHOR]

This talk will consider rigorous mathematics and the nature of proof. It begins with an historical perspective and follows the development of formal mathematics. The talk will conclude with examples demonstrating that understanding the relationship between formal mathematics and rigorous proof can assist with both the discovery and the quality of real proofs of real results. Keywords: rigor, formal mathematics, nature of proof. [ABSTRACT FROM AUTHOR]

Recently, almost uncontrolled technological progress allows so called high-throughput data collection for sophisticated and complex experimental biological systems analysis. Especially it concerns the whole cellular genome. Therefore it becomes more and more vital to suggest and elaborate gene network models, which can be used for more complete interpretation of large and complex data sets. The presented paper concerns modeling of yeast cell cycle. With the reference to the previously published papers concerning the same subject, our paper presents a significant improvement in calculation procedure leading to construction of an efficient algorithm for modeling of the mentioned cell cycle. Presented model concerns 12 selected genes and incorporates all possible types of interactions. [ABSTRACT FROM AUTHOR]

The uniqueness of a fingerprint allows to build systems that can recognize personal fingerprint feature automatically. In practice, fingerprint recognition and classification based on two techniques: the reference point location and the Gabor filtering or the fingerprint ridge characteristics (called minutiae). In this paper a new method of the reference point alignment has been presented. A new approach of reference point localization is based on so-called identification filters, which have been composed on the basis of analysis of biometric characteristic of human fingerprint. In this paper construction of such filters has been precisely explained. Proposed method with other methods was compared. [ABSTRACT FROM AUTHOR]

Paper is related to biometrics image compression (finger print and iris images) and refers to the earlier works related to the use of the interferometry images in non touch measurement techniques. The finger prints fringes have similar periodic nature similar to interfered waves, thus we applied a special kind of base functions to perform compression of exemplary images. The second stage of the paper deals with the iris decomposition i.e. a hybrid of Canny Edge detector stage and a Circular Hough Transform to establish iris boundaries and data encryption-compression by Gabor wavelets functions. To ensure correctness of the investigations - comparison to real criminals fingerprint database - searching and recognition case - has been tested. Eventually the comparison of iris templates on exemplary human eye datasets has been presented too. [ABSTRACT FROM AUTHOR]

This paper presents especially elaborated laboratory setup and computer software for visualizing the tongue surface movement and the changes of its shape using an ultrasonograph (USG). In the paper the acoustic properties of the tongue, the way of examination and the measurement set-up was described, as well. The elaborated setup allows us to computer-visualize and follow in real time the changes of the tongue shape in the process of vowel articulation, thus providing a visual feedback with the speaker. It can be one of the ways of treating speech defects and phoniatric rehabilitation in people with hearing disorders and pronunciation defects. [ABSTRACT FROM AUTHOR]

This paper presents the problem of diagnostic parameters quality control in the adaptive ECG interpretation system. The quality estimation is used for seamless adaptation of processing being a part of the optimization feedback. Therefore, quality control procedures - unlike in conventional systems - are embedded in the software. The proposed methodology is based on two principal concepts: diagnostic results priority depending on the patient status and the data convergence used as description of the adaptive system response to a transient in patient status. The paper includes an example of testing a surveillance network prototype with use of proposed parameters, demonstrating their high usability. [ABSTRACT FROM AUTHOR]

In the paper a new method of handwritten word recognition is described. In this method different probabilistic character language models (PCLMs) are used in order to improve the word recognition accuracy. These models consist of conditional probability distributions of characters given that preceding, succeeding or surrounding characters are known. The application of character succession and precedence in word recognition was explored in many earlier works. The novelty of the method proposed here consists in utilizing also the conditional probabilities based on character appearing of both sides of the letter being recognized and in combining such classifier with other ones based on character precedence and succession. This new classifier that uses two sided character neighborhood is realized as an iterative procedure which starts with character support factors evaluated independently for each character in the word by a soft character classifier. Then in each step of this iterative procedure the support factors are calculated as Bayes-like formula using needed probabilities. Next, the support factors obtained at the character level are used to calculate the support factors for complete words. Finally, the soft word recognition results obtained for classifiers based on three different PCLMs are combined. Experiments described in the paper show the superiority of the combined method over all its simpler components. [ABSTRACT FROM AUTHOR]

In syntactic pattern recognition a pattern is represented by abstract data, for instance a graph. The problem of recognition is to determine if a pattern, represented by the describing graph, belongs to a language L(G), generated by a graph grammar G. The so-called IE graphs can be used for a pattern description. They are generated by so-called ETPL(k) graph grammars. The purpose of this paper is to present an idea of a new approach to syntactic recognition of fuzzy patterns represented by fuzzy IE graphs, followed the example of random IE graphs. This methodology can be used for analysis of wider class of patterns and scenes than the one described by the classical syntactic methods. In this paper, apart from the presentation of the fuzzy-aided approach, it is also shown that the probabilistic-syntactic approach is a special case of the presented one. [ABSTRACT FROM AUTHOR]

In the paper we present the method of the generalisation of a language sample for grammatical inference of quasi context-sensitive GDPLL(k) grammars. GDPLL(k) grammars and parsers have been developed as an efficient tool for syntactic pattern recognition: the grammars are characterised by very good discriminative properties and the parser for the grammars is of the linear computational complexity. Nevertheless, one of the main problems of practical application of GDPLL(k) grammars in syntactic pattern recognition systems consists in difficulties in defining the grammar from the sample of a pattern language. The method which we describe in the paper is an important element of the solution of this problem. [ABSTRACT FROM AUTHOR]

Mathematical morphology is a widely known image processing technique. Morphology-based processing techniques can be easily implemented, thanks to conceptual simplicity of basic morphological operators. However, most morphological algorithms that are run on sequential processors are unable to meet real-time requirements. A way to solve this problem are the implementations using dedicated hardware, e.g. the FPGAs. This paper describes a complete FPGA architecture for real-time edge detection. The architecture consists of prefiltering, gradient image computation and non-maximum suppression blocks, allowing the extraction of a single-pixel-thin binary edge image of good quality. The described solution, although simple and compact, is capable of processing image data stream with the speed of hundreds of frames per second for a 512×512, 8-bit gray-scale image, while keeping the costs, size and power consumption at a low level. Description of implemented algorithm, resource usage summary, output images, as well as block diagrams of the architecture are included in the paper. [ABSTRACT FROM AUTHOR]

Colorization is a term used to describe a computerized process for adding color to black and white pictures, movies or TV programs by replacing a scalar value stored at each pixel of the gray scale image by a vector in a three dimensional color space with luminance, saturation and hue or simply RGB. Since different colors may carry the same luminance value but vary in hue and/or saturation, the problem of colorization has no inherently "correct" solution. Due to these ambiguities, human interaction usually plays a large role. In this paper we present a novel colorization method that utilizes the morphological distance transformation and changes of neighboring pixel intensities to propagate the color within the gray scale image. The proposed method frees the user of segmenting the image, as color is provided simply by scribbles which are next automatically propagated within the image. The effectiveness of the algorithm allows the user to work interactively and to obtain the desired results promptly after providing the color scribbles. In the paper we show that the proposed method allows for high quality colorization results for still images. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss a fusion of the two most popular image features - color and shape - in the aspect of content-based image retrieval. It is clear that these representations have their own advantages and drawbacks. Our suggestion is to combine them to achieve better results in various areas, e.g. pattern recognition, object representation, image retrieval, by using optimal variants of particular descriptors (both, color and shape) and utilize them in the same time. To achieve such goal we propose two general strategies (sequential and parallel) for joining elementary queries. They are used to construct a system, where each image is being decomposed into regions, basing on shapes with some characteristic properties - color and its distribution. In the paper we provide an analysis of this proposition as well as the initial results of application in Content Based Image Retrieval problem. The original contribution of the presented work is related to the fusion of several shape and color descriptors and joining them into parallel or sequential structures giving considerable improvements in content-based image retrieval. The novelty is based on the fact that many existing methods (even complex ones) work in the same domain (shape or color), while the proposed approach joins features from different areas. [ABSTRACT FROM AUTHOR]

This paper shows that little has been written on the roles that people actually perform in the course of mechatronics engineering work. The paper reports empirical results of interviews with several engineers working in mechatronics: this is part of a larger study of engineering work in several disciplines. The paper argues that coordinating the work of other people is the most significant working role, both in mechatronic engineering and also other engineering disciplines. This role has not been explicitly identified before. While coordination appears to be a generic nontechnical role, close examination reveals that technical knowledge is very important for effective coordination in engineering work. The coordination role is not mentioned in engineering course accreditation criteria. The absence of explicit references to this role in previous literature suggests that the research methods used for this study could provide better guidance for engineering educators on course requirements. [ABSTRACT FROM AUTHOR]

Uncertainty is the main obstacle in predicting crude oil price. Although there are various models and computational methods on crude oil price forecasting in literature, most of them do not effectively predict the variability of crude oil price due to uncertainty. Very recently, Hu and He [2] reported of using ILS (Interval Least Square) approach to forecast the stock market and obtained much better results than that obtained with traditional point methods, In this paper, we investigate if the ILS approach can forecast the relationship between commodity inventory levels and crude oil spot prices effectively. Our empirical study suggests that both the ILS method and the confidence interval method can produce comparable quality forecasts. While the computational result produced by the ILS method seems slightly worse than the 95% confidence intervals in two quality measurements, the differences are negligible. On a new forecasting quality measurement proposed in this paper, the ILS method produces results better than the 95% confidence intervals. Hence, interval method is a feasible alternative in crude oil price forecasting. [ABSTRACT FROM AUTHOR]

This paper proposes a common framework for various probabilistic logics. It consists of a set of uncertain premises with probabilities attached to them. This raises the question of the strength of a conclusion, but without imposing a particular semantics, no general solution is possible. The paper discusses several possible semantics by looking at it from the perspective of probabilistic argumentation. [ABSTRACT FROM AUTHOR]

Many types of fuzzy truth values have been proposed, such as numerical truth values, interval truth values, triangular truth values and trapezoid truth values and so on. Recently, different type of fuzzy truth values (which we call multi-interval truth values) have been proposed and discussed. A characteristic feature of multi-interval truth values is that some of the truth values are not convex. This fact makes us difficult to understand algebraic properties on multi-interval truth values. This paper first shows that a set of multi-interval truth values is de Morgan bi-lattices when the conventional logical operations are introduced. Next, this paper focuses on functions over the set of the simplest multi-interval truth values, i.e., we define a multi-interval truth value as a non-empty subset of {0, 1, 2}. Then, this paper discusses mathematical properties of functions over multi-interval truth values. [ABSTRACT FROM AUTHOR]

Among modern applications of document retrieval, a practical system for retrieving scientific publications has recently been attracting much attention from research community. In a scientific document, there are many types of uncertainty information occurring, such as research areas of the documents or authors. Thus, a method for efficiently handling uncertainty information when retrieving scientific information, as well as other kinds of uncertainty information, is currently desirable. In our paper, we propose a novel fuzzy retrieval framework based on approximating reasoning for document retrieval. We also discuss using approximating reasoning to discover additional relations in the database to support more advanced search functions in an intelligent information retrieval system. This paper also introduces an experimental system implementing our proposed technique. The performance of the system is then evaluated and analyzed. [ABSTRACT FROM AUTHOR]

Although fuzzy set and probability theories are complementary for dealing with pervasive imprecision and uncertainty in real world problems, object-oriented database models combining the relevance and strength of both the theories appear to be sporadic. This paper introduces our extension of Eiter et al.'s probabilistic object base model with two key features: (1) uncertain and imprecise attribute values are represented as probability distributions on a set of fuzzy set values; and (2) class methods with uncertain and imprecise input and output arguments are formally integrated into the new model. A probabilistic interpretation of relations on fuzzy set values is proposed for their combination with probability degrees. Then the syntax and semantics of fuzzy-probabilistic object base schemas, instances, and selection operation are defined. Furthermore, the soft computing paradigm needs to have real systems implemented to be useful in practice. This paper also presents our development of FPDB4O as a management system for fuzzy and probabilistic object bases of the proposed model. [ABSTRACT FROM AUTHOR]

In many practical situations, we are not satisfied with the accuracy of the existing measurements. There are two possible ways to improve the measurement accuracy: first, instead of a single measurement, we can make repeated measurements; the additional information coming from these additional measurements can improve the accuracy of the result of this series of measurements;second, we can replace the current measuring instrument with a more accurate one; correspondingly, we can use a more accurate (and more expensive) measurement procedure provided by a measuring lab - e.g., a procedure that includes the use of a higher quality reagent. In general, we can combine these two ways, and make repeated measurements with a more accurate measuring instrument. What is the appropriate trade-off between sample size and accuracy? In our previous paper, we solved this problem for the case of static measurements. In this paper, we extend the results to the case of dynamic measurements. [ABSTRACT FROM AUTHOR]

This paper investigates the use of the method of dimensional reduction Cascaded Nonlinear Components Analysis (C-NLPCA) in the protein secondary structure prediction problem. The use of the C-NLPCA is justified by the fact that this method manage to obtain a dimensional reduction that considers the nonlinearity of the data. In order to prove the effectiveness of the C-NLPCA, this paper presents comparisons of methods of components extraction, as well as, of existing predictors. The C-NLPCA revealed to be efficient, propelling a new field of research. [ABSTRACT FROM AUTHOR]

This paper presents an autonomous intelligent agent developed for health care in geriatric residences. The paper focuses on the construction of an autonomous agent which incorporates a model of human thinking, such as reasoning based on past experiences. The work here presented focuses in the development of the CBP internal structure. The planning mechanism has been implemented by means of a novel FYDPS neural network. The system has been tested and this paper presents the results obtained. [ABSTRACT FROM AUTHOR]

A multiagent system that incorporates an Artificial Neural Networks based Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. The proposed system facilitates the intrusion detection in dynamic networks. This paper presents the structure of the Mobile Visualization Connectionist Agent-Based IDS, more flexible and adaptable. The proposed improvement of the system in this paper includes deliberative agents that use the artificial neural network to identify intrusions in computer networks. The agent based system has been probed through anomalous situations related to the Simple Network Management Protocol. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid deliberative architecture based on the concept of CBPBDI agent. A CBP-BDI agent is a BDI agent that incorporates a CBP reasoning engine. The work here presented focuses in the development of the CBP internal structure. The planning mechanism has been implemented by means of a novel FYDPS neural network. The system has been tested and this paper presents the results obtained. [ABSTRACT FROM AUTHOR]

In present, data processing is an important process in many organizations. Classification trees are used to assign a classification to unknown data and can be also used for data partitioning (data clustering). The classification tree must be able to cope with outliers and have acceptably simple structure. An important advantage is the white-box structure. This paper presents a novel method called ACO-DTree for classification tree generation and their evolution inspired by natural processes. It uses a hybrid metaheuristics combining evolutionary strategies and ant colony optimization. Proposed method benefits from the stochastic process and population approach, which allows the algorithm to evolve more efficiently than the methods alone. The paper also consults the parameter estimation for the method. Tests on real data (UCI and MIT-BIH database) have been performed and evaluated. [ABSTRACT FROM AUTHOR]

In this paper we present a hybrid heuristic for the capacitated p-median problem (CPMP). This problem considers a set of n points, each of them with a known demand, the objective consists of finding p medians and assign each point to exactly one median such that the total distance of assigned points to their corresponding medians is minimized, and the a capacity limit on the medians may not be exceeded. The purpose of this paper is to present a new hybrid heuristic to solve the CPMP, called Clustering Search (CS), which consists in detecting promising search areas based on clustering. Computational results show that the CS found the best known solutions in all most instances. [ABSTRACT FROM AUTHOR]

One objective in Precision Agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. With such purpose we have designed a hybrid decision making system based on the Bayesian and Fuzzy k-Means (FkM) classifiers, where the a priori probability required by the Bayes framework is supplied by the FkM. This makes the main finding of this paper. The method performance is compared against other available strategies. [ABSTRACT FROM AUTHOR]

This paper presents a multiagent framework designed to animate groups of synthetic humans that properly balance task oriented and social behaviors. The work presented in this paper focuses on the BDI agents and the social model integrated to provide socially acceptable decisions. The social model provides rationality, to control the global coordination of the group, and sociability, to simulate relations (e.g. friends) and reciprocity between members. The multiagent based framework has been tested successfully in dynamic environments while simulating a virtual university bar, where several types of agents (groups of waiters and customers) can interact and finally display complex social behaviors (e.g. task passing, reciprocity, planned meetings). [ABSTRACT FROM AUTHOR]

This chapter examines the possibilities and challenges that lie ahead for evolutionary music and art. Evolutionary computing methods have enabled new modes of creative expression in the art made by humans. One day, it may be possible for computers to make art autonomously. The idea of machines making art leads to the question: what do we mean by ‘making art' and how do we recognise and acknowledge artistic creativity in general? Two broad categories of human-machine creativity are defined: firstly, machines that make art like, and for, humans; and secondly, machines that make ‘art' that is recognised as creative and novel by other machines or agents. Both these categories are examined from an evolutionary computing perspective. Finding ‘good' art involves searching a phase-space of possibilities beyond astronomical proportions, which makes evolutionary algorithms potentially suitable candidates. However, the problem of developing artistically creative programs is not simply a search problem. The multiple roles of interaction, environment, physics and physicality are examined in the context of generating aesthetic output. A number of ‘open problems' are proposed as grand challenges of investigation for evolutionary music and art. For each problem, the impetus and background are discussed. The paper also looks at theoretical issues that might limit prospects for art made by machines, in particular the role of embodiment, physicality and morphological computation in agent-based and evolutionary models. Finally, the paper looks at artistic challenges for evolutionary music and art systems. [ABSTRACT FROM AUTHOR]

This paper explores the application of ecosystem simulation to the production of works of generative electronic art. The aim is to demonstrate that virtual ecosystems are capable of producing outcomes that are rich, complex and interesting aesthetically. A number of artworks that employ virtual ecosystems are surveyed. The author argues that the most interesting works of generative art exhibit four basic properties: coherence and unity; multi-scaled temporal complexity; autonomous production of novelty; responsiveness to perturbation. The virtual ecosystem is assessed for its suitability as a medium for constructing generative art in light of these desirable properties. It is concluded that the ecosystem's strengths lie in its exhibition of multi-scaled complexity and its autonomous production of novelty. Whilst an artist may manipulate a simulation to retain visual and sonic coherence, the software also possesses an implicit coherence inherent in its ability to self-organize. Under some circumstances it appears that the weakness of the virtual ecosystem as an artistic medium lies in its unpredictable response to perturbation. Consequently, the paper also explores virtual ecosystems' susceptibility to external control and describes methods that have been employed to adjust the responsiveness of art works that employ them. [ABSTRACT FROM AUTHOR]

The paper proposes an agent-based algorithm for data reduction, implemented using JABAT (JADE Based A-Team) environment designed for solving a variety of computationally hard optimization problems. The approach aims at reducing the original dataset in two dimensions including selection of reference instances and removal of irrelevant attributes. Several agents representing different local-search based strategies are employed in parallel to achieve a synergetic effect. The paper includes also computational experiment results. [ABSTRACT FROM AUTHOR]

The paper discusses a distributed approach to multi-agent plan execution and control, where a team of agents may perform actions concurrently in a partially observable environment. Each agent is able to supervise the execution of the actions it is responsible for by means of an on-line monitoring step and to perform agent diagnosis when a failure in the execution of an action has been detected. The emphasis of the paper is on the mechanism for synthesizing a recovery plan in presence of an action failure. One contribution of the paper concerns an in depth analysis of the characteristics the recovery plan has to satisfy. The most stringent requirement regards that the recovery plan has to be conformant, as the partial observability of the system allows just to estimate the status of the agent and the action effects may be non deterministic. The paper proposes an approach for synthesizing such a conformant recovery plan based on the adoption of symbolic methods. In particular, the recovery planning is implemented in terms of operations on Ordered Binary Decision Diagrams used for encoding both the belief states and the action models. [ABSTRACT FROM AUTHOR]

Mining subgraphs is an area of research where we have a given set of graphs, and we search for (connected) subgraphs contained in these graphs. In this paper we focus on the analysis of graph patterns where the graphs are molecules and the subgraphs are patterns. In the analysis of fragments one is interested in the molecules in which the patterns occur. This data can be very extensive and in this paper we introduce a technique of making it better available using visualization. The user does not have to browse all the occurrences in search of patterns occurring in the same molecules; instead the user can directly see which subgraphs are of interest. [ABSTRACT FROM AUTHOR]

This paper proposes a situation-based conflict estimation method that efficiently generates quality plans for multi-agent systems (MAS) by appropriately selecting abstract plans in hierarchical planning (HP). In HP, selecting a plan at an abstract level affects planning performance because an abstract plan restricts the scope of concrete-level (or primitive) plans and thus can reduce the planning cost. However, if all primitive plans under the selected abstract plan have serious and difficult-to-resolve conflicts with the plans of other agents, the final plan after conflict resolution will be inefficient or of low quality. This issue originates in the uncertainty of MAS, where other agents also have individual plans for their own goals and it is difficult to clearly anticipate which abstract plan will cause fewer conflicts with other agents' plans. In the proposed method, by introducing conflict patterns that express the situations of conflicts among agents' plans, agents learn and estimate which abstract plans are less likely to cause conflicts or which conflicts will be easy to resolve; thus, after conflict resolution, they can induce probabilistically higher-utility primitive plans. This paper also describes an experiment to evaluate our method. The results indicate that our method can improve the efficiency of plan execution. [ABSTRACT FROM AUTHOR]