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This is a summary of the papers presented at the Active Conceptual Modeling of Learning (ACM-L) Workshop, November 8, 2006, Tucson, Arizona, USA, and several invited papers. [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]

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]

The Lost Cosmonaut is an interactive narrative based on digitally enhanced paper. This technology uses an electronic pen to mediate between paper and computer. Thus any actions of the pen on the paper can be captured and manipulated by a computer as well as we can map digitally controlled events onto paper. The story in this narrative environment reveals itself partially through written text and images on the paper surface just as any other printed story. However, additional information in form of digitally controlled outputs such as sound, light and projections can be accessed through interaction with pen and paper. Furthermore the audience is not only supposed to read and otherwise perceive information, we also want them to actively produce content for this environment by writing onto the paper. By doing so they also add content to the database containing the digital output at the same time. Hence we produce a complex multimedia environment that works on three levels: On paper, in a digitally controlled visual and acoustic environment and in the combination of both worlds. Last but not least this environment is an open system, which grows as a collaborative effort over time as each user adds his own entries to paper and database. We argue that using paper as an integrated part of a digital environment is a best-of-both-world approach that opens up new possibilities for producing and perceiving narrative. [ABSTRACT FROM AUTHOR]

We present a framework for generating paper-cutting effects on mesh models. Our system involves the construction of model defined paper-cutting patterns, extraction of the medial axis (MA) from the mesh models and position location for patterns on the mesh surface based on MA. Patch sets for placing patterns on the mesh surface are determined by the region growing algorithm. After patch sets parameterization, patterns are drawn on the parameterized domain and mapped onto the mesh surface by inverse sampling. [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]

Active Learning in the classroom domain presents an interesting case for integrating physical and digital affordances. Traditional physical handouts and transparencies are giving way to new digital slides and PCs, but the fully digital systems still lag behind the physical artifacts in many aspects such as readability and tangibility. To better understand the interplay between physical and digital affordances in this domain, we developed PaperCP, a paper-based interface for a Tablet PC-based classroom interaction system (Classroom Presenter), and deployed it in an actual university course. This paper reports on an exploratory experiment studying the use of the system in a real-world scenario. The experiment confirms the feasibility of the paper interface in supporting student-instructor communication for Active Learning. We also discuss the challenges associated with creating a physical interface such as print layout, the use of pen gestures, and logistical issues. [ABSTRACT FROM AUTHOR]

In this paper we present our work about personalized search in digital libraries. Unlike other researches which use content-based methods, we focus on citation-based methods for this purpose. We propose a practical approach to estimate the co-citation relatedness between scientific papers using the Google search engine. We conducted some experiments to evaluate performance of different citation-based methods. The experimental results show that our approach is promising and applicable for personalized search in digital libraries. [ABSTRACT FROM AUTHOR]

This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime. [ABSTRACT FROM AUTHOR]

Finding the right material on the Web could be a worthwhile result. Users waste too much time to discover the useful information. Recommender system can provide some shortcuts to the user, but if the recommendation is based on people's opinion, one question remains — how relevant is a user's opinion? This paper presents a model to define the user's relevance opinion in a recommender system. This metric aims to help the target user to decide in what recommendation he should focus his attention. Beyond the model, we present a real experiment using an e-government database. Keywords: User modeling, Authority, Recommender System. [ABSTRACT FROM AUTHOR]

In this chapter, we proudly introduce eight awarded papers, selected from the papers presented in the 20th annual conference of Japan Society for Artificial Intelligence (JSAI2006). The conference was held at Tower Hall Funabori located in the east part of Tokyo from June 7 until June 9, 2006. 276 papers were presented in about 60 sessions and over 500 people participated in the conference. Sessions vary from the fundamental issues to the state-of-the-art applications. Session for regular papers are as follows; Logic and learning, Reinforcement learning and agent learning, Agent theory, Auction/Game/Economics, Agent learning, Agent planning, Agent simulation and interaction, Genetic algorithm, Image processing, Information extraction and classification, Clustering/self-organization, Classifi- cation learning, Text mining, Graph mining, Mining algorithm, Web mining, Pre- and post-processing for data mining, Practices of data mining, Cognitive modeling, Language processing and dialogue, Robot/sensor network, Web information system, Semantic Web, Knowledge modeling and knowledge sharing, Support of knowledge management, knowledge modeling/ontology,Web service, Human interface and communication support, Education support, Learning support environment, and Musical and auditory information processing. [ABSTRACT FROM AUTHOR]

Typically, personalized information recommendation services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. We present an approach based on Word Sense Disambiguation (WSD) for the extraction of user profiles from documents. This approach relies on a knowledge-based WSD algorithm, called JIGSAW, for the semantic indexing of documents: JIGSAW exploits the WordNet lexical database to select, among all the possible meanings (senses) of a polysemous word, the correct one. Semantically indexed documents are used to train a naïve Bayes learner that infers "semantic", sense-based user profiles as binary text classifiers (user-likes and user-dislikes). Two empirical evaluations are described in the paper. In the first experimental session, JIGSAW has been evaluated according to the parameters of the Senseval-3 initiative, that provides a forum where the WSD systems are assessed against disambiguated datasets. The goal of the second empirical evaluation has been to measure the accuracy of the user profiles in selecting relevant documents to be recommended. Performance of classical keyword-based profiles has been compared to that of sense-based profiles in the task of recommending scientific papers. The results show that sense-based profiles outperform keyword-based ones. [ABSTRACT FROM AUTHOR]

Vertical Search Engines (VSEs), which usually work on specific domains, are designed to answer complex queries of professional users. VSEs usually have large repositories of structured instances. Traditional instance ranking methods do not consider the categories that instances belong to. However, users of different interests usually care only the ranking list in their own communities. In this paper we design a ranking algorithm -ZRank, to rank the classified instances according to their importances in specific categories. To test our idea, we develop a scientific paper search engine-CPaper. By employing instance classifying and ranking algorithms, we discover some helpful facts to users of different interests. [ABSTRACT FROM AUTHOR]

Collaborative tagging has grown on the Internet as a new paradigm for web information discovering, filtering and retrieval. In the physical world, we use visual tags: labels readable by smartphones with cameras. While visual tags are usually related to a web site address, collaborative tagging, instead, provides updated, recommended information contributed and shared by users. In this paper we investigate the combination of collaborative tagging systems with visual tags. We present a prototype of a semiautomatic system generating visual tags which gather information from collaborative tagging. The user can interact with a list of relevant tags (built by clustering closely related tags) that can be further encoded in a visual tag, according to user's preferences. The user experience is enriched by retrieving multimedia content linked to the selected tags, present on the web. We finally show a case study illustrating our approach. [ABSTRACT FROM AUTHOR]

Organic computing is an upcoming research area with strong relationships to the ideas and concepts of agent-based systems. In this paper, we therefore will have a closer look at agent systems, organic computing systems (as well as autonomic computing systems) and state commonalities and divergences between them. We then propose a common view on these technologies and show, how they can benefit from each other with regard to software engineering. [ABSTRACT FROM AUTHOR]

In this paper, we discuss importance and usefulness of interpersonal network in a community support system. We built a scheduling support system for an academic conference. Our system supports information exchange among participants and information discovery with generating participants' interpersonal network. This system was used in an academic conference called JSAI2003 involving 276 active users. The analysis of the networks reveals that interpersonal networks can promote information exchange among people by indicating existence of people to the others, and that it can also support information discovery by recommendation. [ABSTRACT FROM AUTHOR]

In this chapter, we introduce five awarded papers, selected from the 19th annual conference of JSAI (Japan Society for Artificial Intelligence) 2005. These five papers are truly excellent, for they were selected out of over 290 papers, the rate of which was less than 2%, voted by the total number of approximately seventy reviewers. As has been often mentioned, the research of artificial intelligence branches to many fields; among them, we could observe such tendency that empirical methods were rather prevalent compared with theoretical issues. However, we contend that the following five papers were both sound in theory and practical in feasibility. We show the synopses of the five papers, adding short comments for the award for each. [ABSTRACT FROM AUTHOR]

On behalf of the program committee (PC) of JSAI 2003, I would like to thank all the chair persons, discussants, and attentive audience who contributed to select these awarded papers. [ABSTRACT FROM AUTHOR]

In spite of the importance of natural drawing with input device, the interface between the device and virtual brush's status has been dealt less in literatures. Through this paper, we present a natural interface between pen-tablet device and virtual brush. Proposed system uses pressure instead of z-coordinate of pen to allow drawing with the similar feeling to the real brush. The contribution of this paper is as follows: first, a brush model interacting with pressure is generated. Second, 3D brush model is allowed to be folded back decreasing droplet size intensively according to pen direction changes. [ABSTRACT FROM AUTHOR]

The research community plays a very important role in helping researchers undertake new research topics. The authors propose a community mining system that helps to find communities of researchers by using bibliography data. The basic concept of this system is to provide interactive visualization of communities both local and global communities. We implemented this concept using actual bibliography data and present a case study using the proposed system. [ABSTRACT FROM AUTHOR]

Capturing and understanding mathematics from print form is an important task in translating written mathematical knowledge into electronic form. While the problem of syntactically recognising mathematical formulas from scanned images has received attention, very little work has been done on semantic validation and correction of recognised formulas. We present a first step towards such an integrated system by combining the Infty system with a semantic analyser for matrix expressions. We applied the combined system in experiments on the semantic analysis of matrix images scanned from textbooks. While the first results are encouraging, they also demonstrate many ambiguities one has to deal with when analysing matrix expressions in different contexts. We give a detailed overview of the problems we encountered that motivate further research into semantic validation of mathematical formula recognition. [ABSTRACT FROM AUTHOR]

Today the proliferation of the availability of the information of scientific events on the Web has created the necessity to offer a quickly access to up-to-date information about the quality of these events. This requirement demands for (semi) automatic tools to speedily provide this information. The human-performed activity of the information quality evaluation is extremely time consuming and easily leads to failures. The application OntoQualis here described was motivated to support the quality evaluation of Scientific Conferences, in the Computer Science area, based on the graduated programs evaluation protocol of the Brazilian agency CAPES. The evaluation mechanism is specified in the QUALIS document specifically designed to assess journals and conferences ranking. This paper presents a brief vision of the ongoing process of domain analysis and ontology prototyping aiming to classify Scientific Conferences: the OntoQualis project. Some results of OntoQualis preliminary evaluation have shown a satisfactory classification level in comparison with CAPES-QUALIS ranking. [ABSTRACT FROM AUTHOR]

Although ontologies as an important component are widely used for different purpose in different communities and a number of approaches have been reported for developing ontologies, few works have been done to clarify the concept of knowledge science as far as we know. This paper presents a novel attempt to create an ontology characterizing a research program "Technology Creation Based on Knowledge Science" from a Knowledge Science perspective. We address a combination of bottom-up and top-down approaches to ontology creation, which is a first time to put forward a perspective of combining explicit knowledge with tacit, intuitive and experiential knowledge for constructing an ontology. An example of application of this ontology, related to a software tool named adaptive hermeneutic agent (AHA), is also given in the paper. [ABSTRACT FROM AUTHOR]

We develop a novel user-centric modeling technology, which can dynamically describe and update a person's expertise profile. In an enterprise environment, the technology can enhance employees' collaboration and productivity by assisting in finding experts, training employees, etc. Instead of using the traditional search methods, such as the keyword match, we propose to use relational and evolutionary graph models, which we call ExpertiseNet, to describe and find experts. These ExpertiseNets are used for mining, retrieval, and visualization. We conduct experiments by building ExpertiseNets for researchers from a research paper collection. The experiments demonstrate that expertise mining and matching are more efficiently achieved based on the proposed relational and evolutionary graph models. [ABSTRACT FROM AUTHOR]

We have developed AirTransNote, a student notes sharing system to facilitate collaborative and interactive learning in a regular lecture at conventional classrooms. Our former student system employed ultrasonic digital pens to capture student notes on a usual paper sheet. Although the paper-based approach is intuitive for learners, the ultrasonic pen with a PDA created some difficulties when used by senior high school students. In order to eliminate those difficulties, we have introduced anoto-based pens and a data gathering system called "digital pen gateway." Owing to the improved student system, students could easily submit their answers with handling of multiple paper sheets. The teacher could refer the answers to choose students who need assistance during the lecture. Questionnaire results showed that the simpler student interface is quite acceptable, and the system can realize our augmented classroom concept in a practical way. [ABSTRACT FROM AUTHOR]

Knowledge visualization is a creative process, but difficult to formalize. This paper presents a system that is capable of analyzing voluminous citation data and visualizing the result. The system offers visualizations of trends by clustering scientific papers taken from the web (CiteSeer papers). Two methods are implemented: factor analysis and PFNET. An experiment has been carried out with the literature in knowledge management. A deep analysis of current trends in KM is then performed to check the relevance of these results. While the topical content is specific to knowledge engineering, semantic web, and related sub-areas, the approach could be applied to any general topic area in AI. [ABSTRACT FROM AUTHOR]

This paper provides a survey and analysis of applications of Agent Based Simulation (ABS). A framework for describing and assessing the applications is presented and systematically applied. A general conclusion from the study is that even if ABS seems a promising approach to many problems involving simulation of complex systems of interacting entities, it seems as the full potential of the agent concept and previous research and development within ABS often is not utilized. We illustrate this by providing some concrete examples. Another conclusion is that important information of the applications, in particular concerning the implementation of the simulator, was missing in many papers. As an attempt to encourage improvements we provide some guidelines for writing ABS application papers. [ABSTRACT FROM AUTHOR]

The aim of this paper is to give theoretical and experimental tools for measuring the driving force in evolving complex networks. First a discrete-time stochastic model framework is introduced to state the question of how the dynamics of these networks depend on the properties of the parts of the system. Then a method is presented to determine this dependence in the possession of the required data about the system. This measurement method is applied to the citation network of high energy physics papers to extract the in-degree and age dependence of the dynamics. It is shown that the method yields close to "optimal" results. [ABSTRACT FROM AUTHOR]

Spatial structure supporting applications offer an abstract level of what can be found in the real world. However, in many systems, objects are aligned straight, rotation is not possible, they can be resized easily and can hold more text than is visible on the screen. Paper and structures created with paper seem to be more limited: Straight alignment is not possible without spending much time; paper can hardly be resized without damaging it; and piles may fall down if they become too tall. However, a closer look shows that paper structures offer much more attributes and dependencies than any current spatial structure supporting application. In this article, we compare paper structures to a selection of computer applications. We argue that the observed small additions with paper carry information which improves finding and organizing. [ABSTRACT FROM AUTHOR]

Semantic Web conferences such as ESWC and ISWC offer prime opportunities to test and showcase semantic technologies. Conference metadata about people, papers and talks is diverse in nature and neither too small to be uninteresting or too big to be unmanageable. Many metadata-related challenges that may arise in the Semantic Web at large are also present here. Metadata must be generated from sources which are often unstructured and hard to process, and may originate from many different players, therefore suitable workflows must be established. Moreover, the generated metadata must use appropriate formats and vocabularies, and be served in a way that is consistent with the principles of linked data. This paper reports on the metadata efforts from ESWC and ISWC, identifies specific issues and barriers encountered during the projects, and discusses how these were approached. Recommendations are made as to how these may be addressed in the future, and we discuss how these solutions may generalize to metadata production for the Semantic Web at large. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a postal RFID application model that can be used for real time trace and tracking system implementation of parcel processing and pallet management. This paper also shows the tag recognition performance of parcels by speed and mounting tag material such as thin cans, water bottles, and paper using an implemented postal RFID system to find the best solution for RFID adaptation in postal logistics. [ABSTRACT FROM AUTHOR]

In order to discover services in business-driven Web service composition conveniently, accurately, and efficiently, this paper proposes a business process-oriented service discovery approach. In order to link business property with services, this paper proposes a business related description model of operations. Based on the business related relation model proposed in this paper, a relation-driven discovering algorithm for business process-oriented service discovery is presented. Compared with other discovering approach, the proposed discovering approach considers the business relation between operations and can solve the problem of discovering services with meaningful business relation. The experimentation shows the better performance of this algorithm. [ABSTRACT FROM AUTHOR]

Request Processing Systems should exhibit predictable behavior by guaranteeing Quality-of-Service parameters. One of the important requirements of predictability is that requests should have maximum acceptable response time thresholds, denoted as deadlines in this paper. In order to provide QoS, the system should try to guarantee these deadlines. This way, it is desirable to use a load-balancing algorithm that tries to both maximize the throughput and minimize the missed deadlines. Assuming that all requests durations are known a priori, this paper shows, with the help of a simulator, how the control over the big tasks plays a crucial role in the pursuit of the objective. Finally, this paper presents a derived form of the traditional Least-Work-Remaining algorithm, named Task Assignment by Isolating Big Tasks, that proved to be a better alternative in such scenarios. [ABSTRACT FROM AUTHOR]

UML is being increasing used for the analysis and design of Web Information Systems. At the same time, many XML-based languages are cornerstones in the development of this kind of system. As a consequence of the predominance of these languages, there are many works in the literature devoted to exploring the relationships between UML and XML. In this paper we present a survey of current approaches to the transformation of UML models into XML schemas. The study is focused on the case of transformation of UML class diagrams to XML schemas, since we have not found any proposal regarding other kinds of UML diagrams. [ABSTRACT FROM AUTHOR]

Object-level vertical search engine has been the research focus recently where the resource collecting problem is still an open area. It is difficult to adapt the traditional link-based web crawler for this task because of the sparse linkage and data-centered webpage of the relevant resources. In this paper, we propose a meta-search based method enhanced with auxiliary crawling to address the problem caused by sparse linkage of the relevant resources. And to retrieve the data-centered webpages efficiently, domain schema is defined to describe the target resource, and representative data instances are selected for meta-search query composing. Moreover, evaluation criteria for the domain resource survey are also proposed as the guideline for query composing and auxiliary crawling, which enable the resource discovery to be automatically performed by computers. Experiment results on real-world data show that our method is effective and efficient. [ABSTRACT FROM AUTHOR]

This paper gives a survey of the current state of ARTIC - the modern Czech concatenative corpus-based text-to-speech system. All stages of the system design are described in the paper, including the acoustic unit inventory building process, text processing and speech production issues. Two versions of the system are presented: the single unit instance system with the moderate output speech quality, suitable for low-resource devices, and the multiple unit instance system with a dynamic unit instance selection scheme, yielding the output speech of a high quality. Both versions make use of the automatically designed acoustic unit inventories. In order to assure the desired prosodic characteristics of the output speech, system-version-specific prosody generation issues are discussed here too. Although the system was primarily designed for synthesis of Czech speech, ARTIC can now speak three languages: Czech (both female and male voices are available), Slovak and German. [ABSTRACT FROM AUTHOR]

Performance on a statistical language processing task relies upon accurate information being found in a corpus. However, it is known (and this paper will confirm) that many perfectly valid word sequences do not appear in training corpora. The percentage of n-grams in a test document which are seen in a training corpus is defined as n-gram coverage, and work in the speech processing community [7] has shown that there is a correlation between n-gram coverage and word error rate (WER) on a speech recognition task. Other work (e.g. [1]) has shown that increasing training data consistently improves performance of a language processing task. This paper extends that work by examining n-gram coverage for far larger corpora, considering a range of document types which vary in their similarity to the training corpora, and experimenting with a broader range of pruning techniques. The paper shows that large portions of language will not be represented within even very large corpora. It confirms that more data is always better, but how much better is dependent upon a range of factors: the source of that additional data, the source of the test documents, and how the language model is pruned to account for sampling errors and make computation reasonable. [ABSTRACT FROM AUTHOR]

It is not easy to develop a game that applies physical laws. In this paper we proposed a physics editor to make it easy to produce a physics game. The physics editor uses XML for processing attribute values of each object. This paper presents the architecture of the physics editor, and describes its detailed components. The physics editor provides an efficient method that is easily applied to physical attributes of all objects in the game. In this paper, we showed the process of a car's creation, using the physics editor. The physics editor that is presented in this paper automatically creates physical objects, but it is limited to the rigid body for car racing. [ABSTRACT FROM AUTHOR]

TSP(Traveling Salesman Problem) used widely for solving the optimization is the problem to find out the shortest distance out of possible courses where one starts a certain city, visits every city among N cities and turns back to a staring city. At this time, the condition is to visit N cities exactly only once. TSP is defined easily, but as the number of visiting cities increases, the calculation rate increases geometrically. This is why TSP is classified into NP-Hard Problem. Genetic Algorithm is used representatively to solve the TSP. Various operators have been developed and studied until now for solving the TSP more effectively. This paper applied the new Population Initialization Method (using the Random Initialization method and Induced Initialization method simultaneously), solved TSP more effectively, and proved the improvement of capability by comparing this new method with existing methods. Keywords: Genetic Algorithm, GA, Optimization, Initialization. [ABSTRACT FROM AUTHOR]

This paper introduces the invited session of "Intelligent Agents and their Applications" being presented at the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. This session concentrates on discussing Intelligent Agents and uses some applications to demonstrate the theories reported. An update on last years session [1] is included to report on several key advances in technology that further enable the ongoing development of multi-agent systems. A brief summary of the impediments presented to researchers is also provided to highlight why innovation must be used to sustain the evolution of Artificial Intelligence. This year a variety of challenges and concepts are presented, together with a collection of thoughts about the direction and vision of Intelligent Agents ands their Applciations. [ABSTRACT FROM AUTHOR]

Adaptive Hypermedia builds upon the annotation and adaptation of content. As manual annotation has proven to be the main bottleneck, all means for supporting it by reusing automatically generated metadata are helpful. In this paper we discuss two issues. The first is the integration of a generic AH authoring environment MOT into a semantic desktop environment. In this setup, the semantic desktop environment provides a rich source of automatically generated meta-data, whilst MOT provides a convenient way to enhance this meta-data manually, as needed for an adaptive course environment. Secondly, we also consider the issue of source schema heterogeneity, especially during the automatic metadata generation process, as semantic desktop metadata are generated through a lot of different tools and at different times, so that schemas are overlapping and evolving. Explicitly taking into account all versions of these schemas would require a combinatorial explosion of generation rules. This paper investigates a solution to this problem based on malleable schemas, which allow metadata generation rules to flexibly match different versions of schemas, and can thus cope with the heterogeneous and evolving desktop environment. [ABSTRACT FROM AUTHOR]

Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user's desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged. [ABSTRACT FROM AUTHOR]

The paper reports on the development of the formal framework to design strategies for multi-issue non-symmetric meaning negotiations among software agents in a distributed information retrieval system. The advancements of the framework are the following. A resulting strategy compares the contexts of two background domain theories not concept by concept, but the whole context to the other context by accounting the relationships among concepts, the properties, the constraints over properties, and the available instances. It contains the mechanisms for measuring contextual similarity through assessing propositional substitutions and to provide argumentation through generating extra contexts. It uses presuppositions for choosing the best similarity hypotheses and to make the mutual concession to the common sense monotonic. It provides the means to evaluate the possible eagerness to concede through semantic commitments and related notions of knowledgeability and degree of reputation. [ABSTRACT FROM AUTHOR]

Accurate cancer classification is very important to cancer diagnosis and treatment. As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles. In this paper, we propose a method based on speciated evolution for the cancer classification. The optimal combination among several feature-classifier pairs from the various features and classifiers is evolutionarily searched using the deterministic crowding genetic algorithm. Experimental results demonstrate that the proposed method is more effective than the standard genetic algorithm and the fitness sharing genetic algorithm as well as the best single classifier to search the optimal ensembles for the cancer classification. [ABSTRACT FROM AUTHOR]

This paper describes a system capable of semi-automatically filling an XML template from free texts in the clinical domain (practice guidelines). The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and actions/recommendations). Therefore, there is a need to compute the exact scope of conditions over text sequences expressing the required actions. We present in this paper the rules developed for this task. We show that the system yields good performance when applied to the analysis of French practice guidelines. [ABSTRACT FROM AUTHOR]

A software product-line is a set of products built from a core set of software components. Although software engineers develop software product-lines for various application types, they are most commonly used for embedded systems development, where the variability of hardware features requires variability in the supporting firmware. Feature models are used to represent the variability in these software product-lines. Various feature modeling approaches have been proposed, including feature diagrams, domain specific languages, constraint languages, and the semantic web language OWL. This paper explores a conceptual graph approach to feature modeling in an effort to produce feature models that have a more natural, and more easily expressed mapping to the problem domain. It demonstrates the approach using a standard Graph Product-line problem that has been discussed in various software product-line papers. A conceptual graph feature model is developed for the graph product-line and it is compared to other feature models for this product-line. [ABSTRACT FROM AUTHOR]

Titled Documents (TD) are short text documents that are segmented into two parts: Heading Part and Excerpt Part. With the development of the Internet, TDs are widely used as papers, news, messages, etc. In this paper we discuss the problem of automatic TDs categorization. Unlike traditional text documents, TDs have short headings which have less useless words comparing to their excerpts. Though headings are usually short, their words are more important than other words. Based on this observation we propose a titled document classification framework using the widely used MNB classifier. This framework puts higher weight on the heading words at the cost of some excerpt words. By this means heading words play more important roles in classification than the traditional method. According to our experiments on four datasets that cover three types of documents, the performance of the classifier is improved by our approach. [ABSTRACT FROM AUTHOR]