Emergent Narratives (EN) are a popular approach in game design where stories naturally emerge from players' interactions with the game world.Story sifting is an EN technique where the player's attention is drawn to particularly interesting narrative patterns, but these story sifting patterns can be complex and difficult to create.To address this, we have developed an event-based rules system that supports composable patterns: building blocks that allow higher-level stories to be constructed from lower level ones.We demonstrate our approach through a simulation (of an alien invasion scenario) alongside a powerful incremental story sifter that curates compound events.We conduct agent ratio and stability testing to demonstrate the system's robustness, and a complexity test to show how the performance of the sifter scales with agent numbers and map size.Our findings show that the system remains stable throughout.Our work demonstrates that an event-based rules approach can effectively support composite patterns, ultimately enabling the curation of more complex EN stories.

As mobile devices are getting more powerful and more affordable the use of online educational multimedia is also getting very prevalent. Limited battery power is nevertheless a major restricting factor as streaming multimedia drains battery power quickly. Many battery efficient multimedia adaptation techniques have been proposed that achieve battery efficiency by lowering presentation quality of entire multimedia. Adaptation is usually done without considering any impact on the information contents of multimedia. In this paper, based on the results of an experimental study, we argue that without considering any negative impact on information contents of multimedia the adaptation may negatively impact the learning process. Some portions of the multimedia that require a higher visual quality for conveying learning information may lose their learning effectiveness in the adapted lowered quality. We report results of our experimental study that indicate that different parts of the same learning multimedia do not have same minimum acceptable quality. This strengthens the position that power-saving adaptation techniques for educational multimedia must be developed that lower the quality of multimedia based on the needs of its individual fragments for successfully conveying learning information.

Using online multimedia content on mobile devices is a power hungry activity and drains battery power very quickly. This poses a big challenge in using mobile devices with limited battery power for learning purposes using online educational multimedia. Multimedia adaptation techniques have been developed that preserve battery power by lowering multimedia quality. These adaptation techniques do not provide users with any power-saving options and the adaptation is done automatically without involvement of users. In this paper, we propose a Learner-Battery Interaction model that suggests involving learners in the adaptation process. The idea is to provide learners with power-saving options and relevant feedback about the form of adapted multimedia in advance. This will help leaners in making informed power-saving decisions for adaptation. We implemented the model in a prototype system and conducted an evaluation in the form of a user study.

As a result of tremendous enhancements in the capabilities of mobile devices and availability of higher data rate mobile internet, the use of online multimedia learning resources on mobile devices is increasingly becoming popular. Limited Battery Power of mobile devices, however, is still one big challenge in Mobile Learning. High Quality multimedia learning resources are power hungry and if used on mobile devices drain battery power rapidly limiting learning opportunities on the move. Lack of significant improvements in battery capacities has resulted in significant interest in battery power saving techniques. Existing power-saving streaming multimedia adaptation techniques tend to extend battery life by reducing quality of multimedia making them susceptible to information loss. This loss may affect the learning content efficacy and jeopardizes the learning process. To the best of our knowledge, no previous work has considered the learning content efficacy in multimedia streaming adaptation mechanism. In this paper, we present MoBELearn system, which is a prototype implementation of our proposed Content Aware Power Saving Educational Multimedia Adaptation (CAPS-EMA) approach. We demonstrate battery efficiency in educational multimedia streaming while keeping the adapted resource suitable for learning. We also describe our semantic metamodel for educational multimedia resource that support our energy efficient adaptation technique.

Self-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem solving agent organizations. Based on self-organization principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static, and dynamic scenarios, the performance of organizations using our method is close (70–90%) to that of an idealized centralized allocation method and is considerably better (10–60%) than the current state-of-the-art decentralized approaches.

Ontologies that evolve through use to support new domain tasks can grow extremely large. Moreover, large ontologies require more resources to use and have slower response times than small ones. To help address this problem, we present an on-line se-mantic forgetting algorithm that removes ontology fragments containing infrequently used or cheap to relearn concepts. We situate our algorithm in an extension of the widely used RoboCup Rescue plat-form, which provides simulated tasks to agents. We show that our agents send fewer messages and com-plete more tasks, and thus achieve a greater degree of success, than other state-of-the-art approaches. 1

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high‐quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph‐based algorithm, SPEAR (spamming‐resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext‐induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

This paper describes an Open Linked Data backlinking service, a generic architecture component to support the discovery of useful links between items across highly connected data sets. Using Public Sector Information (PSI) currently available as Linked Data, we demonstrate that contemporary publishing practices do not adequately support the ability to navigate or automatically traverse between resources published by different vendors, or the capacity to discover information relevant to a particular URL Although some useful services in this area have been developed, such as large triple indexes of published data, and the collection of sameAs relationships between individuals, we believe that an important component is missing: a mechanism to discover the backlinks to relevant resources that cannot be found by direct URI resolution. We present the implementation of such a component, integrating data from various PSI sources.

Collaborative tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in collaborative tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents.

With a suitable algorithm for ranking the expertise of a user in a collaborative tagging system, we will be able to identify experts and discover useful and relevant resources through them. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. Firstly, an expert should possess a high quality collection of resources, while the quality of a Web resource depends on the expertise of the users who have assigned tags to it. Secondly, an expert should be one who tends to identify interesting or useful resources before other users do. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements these ideas for ranking users in a folksonomy. We evaluate our method with experiments on data sets collected from Delicious.com comprising over 71,000 Web documents, 0.5 million users and 2 million shared bookmarks. We also show that the algorithm is more resistant to spammers than other methods such as the original HITS algorithm and simple statistical measures.

Semantic Web services have been one of the most significant research areas within the semantic Web vision, and have been recognized as a promising technology that exhibits huge commercial potential. Current semantic Web service research focuses on defining models and languages for the semantic markup of all relevant aspects of services, which are accessible through a Web service interface. The Web service modelling ontology (WSMO) is one of the most significant semantic Web service framework proposed to date. To support the standardization and tool support of WSMO, a formal semantics of the language is highly desirable. As there are a few variants of WSMO and it is still under development, the semantics of WSMO needs to be formally defined to facilitate easy reuse and future development. In this paper, we present a formal object-Z semantics of WSMO. Different aspects of the language have been precisely defined within one unified framework. This model provides a formal unambiguous specification, which can be used to develop tools and facilitate future development.

Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.

Semantic Web services, one of the most significant research areas within the semantic Web vision, has attracted increasing attention from both the research community and industry. The Web service modelling ontology (WSMO) has recently been proposed as an enabling framework for the total/partial automation of the tasks (e.g., discovery, selection, composition, mediation, execution, monitoring, etc.) involved in both intra- and inter-enterprise integration of Web services. To support the standardization and tool support of WSMO, a formal semantics of the language is highly desirable. As there are a few variants of WSMO and it is still under development, the semantics of WSMO needs to be formally defined to facilitate easy reuse and future development. In this paper, we present a formal object-Z semantics of WSMO. Different aspects of the language have been precisely defined within one unified framework. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in this paper, can be used to identify and eliminate errors presented in existing documentation.

To support standardization and tool support of OWL-S, a formal semantics of the language is highly desirable. In this paper, we present a formal Object-Z semantics of OWL-S. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in the paper, can be used to identify and eliminate errors in the current documentation.

We present a Semantic Web application that we callCS AKTive Space. The application exploits a wide range of semantically heterogeneousand distributed content relating to Computer Science research in theUK. This content is gathered on a continuous basis using a variety of methods including harvesting and scraping as well as adopting a range models for content acquisition. The content currently comprises aroundten million RDF triples and we have developed storage, retrieval andmaintenance methods to support its management. The content is mediated through an ontology constructed for the application domainand incorporates components from other published ontologies. CS AKTive Spacesupports the exploration of patterns and implications inherent in the content and exploits a variety of visualisations and multi dimensional representations. Knowledge services supported in the applicationinclude investigating communities of practice: who is working, researching or publishing with whom. This work illustrates a number ofsubstantial challenges for the Semantic Web. These include problems of referential integrity, tractable inference and interaction support. Wereview our approaches to these issues and discuss relevant related work.

The Web Services world consists of loosely-coupled distributed systems which adapt to ad-hoc changes by the use of service descriptions that enable opportunistic service discovery. At present, these service descriptions are semantically impoverished, being concerned with describing the functional signature of the services rather than characterising their meaning. In the Semantic Web community, the DAML Services effort attempts to rectify this by providing a more expressive way of describing Web services using ontologies. However, this approach does not separate the domain-neutral communicative intent of a message (considered in terms of speech acts) from its domain-specific content, unlike similar developments from the multi-agent systems community.In this paper, we describe our experiences of designing and building an ontologically motivated Web Services system for situational awareness and information triage in a simulated humanitarian aid scenario. In particular, we discuss the merits of using techniques from the multi-agent systems community for separating the intentional force of messages from their content, and the implementation of these techniques within the DAML Services model.