Towards Case Completion with inferencing and solution identification using ‘Nested CBR’

Case Based Reasoning (CBR) provides a framework to capture past problems and their solutions to solve future problems. Problem cases are typically complete; however, it is not always possible to have a complete problem case due to complexity, lack of data, or availability of human expertise. The limitations of existing approaches for handling incomplete cases include a reliance upon manual input, such as Conversational CBR (CCBR) and Incremental CBR (ICBR), or a rigid structure of relationships maintained using a semantic ontology, to infer the missing feature values. Using the case base to infer feature values increases the efficiency and likelihood of identifying a relevant solution compared with manual interactions because the case base is based upon proven problem to solution correlation. Therefore, in this work-in-progress paper, we propose \u27Nested CBR\u27 as an approach for the automated completion of partial problem cases, and the subsequent solution identification, thereby avoiding manual input and improving solution efficiency and meaning

Preferences in Case-Based Reasoning

Case-based reasoning (CBR) is a well-established problem solving paradigm that has been used in a wide range of real-world applications. Despite its great practical success, work on the theoretical foundations of CBR is still under way, and a coherent and universally applicable methodological framework is yet missing. The absence of such a framework inspired the motivation for the work developed in this thesis. Drawing on recent research on preference handling in Artificial Intelligence and related fields, the goal of this work is to develop a well theoretically-founded framework on the basis of formal concepts and methods for knowledge representation and reasoning with preferences

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Towards the generalisation of a case-based aiding system to facilitate the understanding of ethical and professional issues in computing

Modern computers endow users of Information and Computer Technology (ICT) with immense power. The speed of the computing revolution has enabled the novel implementation of ICT before consideration of consequent ethical issues can be made. There is now a demand by society that students, ICT novices, and professionals should be aware of the social, legal, and professional issues associated with ubiquitous use of computers. This thesis describes the development of an Internet-based tool that may be used to raise students' awareness of the ethical implications of ICT. It investigates the application, meaning, and scope of computer ethics. Theoretical foundations are developed for the construction of the tool that will classify, store, and retrieve a suitable analogous case from a collection of realworld, ethically analysed ICT case studies. These are used for comparison with ethically dubious events that may be experienced by students. The model draws upon the theoretical aspects of mechanisms for the modification of users' ethical perception. This research is novel in linking these theories to ethical understanding and case retrieval. Little information is available upon the retrieval of documents addressing ethical issues. The classification and retrieval of material using an ethical framework has some commonality with legal retrieval. Similarities are investigated, and concepts are adapted for the retrieval of ethical documents. The differences that arise present challenges for new research. The use of artificial intelligence (AI) retrieval techniques is not acceptable to meet the pedagogic aims of the retrieval tool. A model is developed, avoiding the use of AI in the reasoning process, requiring the student to consider and evaluate the ethical issues raised. The model is tested and evaluated. The research suggests that non-AI paradigms may be used for retrieval of ethical cases, and that areas for future investigation and development exist

Quality of service technologies for multimedia applications in next generation networks

Next Generation Networks are constantly evolving towards solutions that allow the operator to provide advanced multimedia applications with QoS guarantees in heterogeneous, multi-domain and multi-services networks. Other than the unquestionable advantages inherent the ability to simultaneously handle traffic flows at different QoS levels, these architectures require management systems to efficiently perform quality guarantees and network resource utilization. These issues have been addressed in this thesis. DiffServ-aware Traffic Engineering (DS-TE) has been considered as reference architecture for the deployment of the quality management systems. It represents the most advanced technology to accomplish either network scalability and service granularity goals. On the basis of DS-TE features, a methodology for traffic and network resource management has been defined. It provides some rules for QoS service characterization and allows to implement Traffic Engineering policies with a class-based approach. A set of basic parameters for quality evaluation has been defined, that are the Key Performance Indicators; some mathematical model to derive the statistical nature of traffic have been analyzed and an algorithm to improve the fulfillment of quality of service targets and to optimize network resource utilization. It is aimed at reducing the complexity inherent the setting of some of the key parameters in the NGN architectures. Multidomain scenarios with technologies different from DS-TE have been also evaluated, defining some methodologies for network interoperability. Simulations with Opnet Modeler confirmed the efficacy of the proposed system in computing network configurations with QoS targets. With regard to QoS performance at the application level, video streaming applications in wireless domains have been particularly addressed. A rate control algorithm to adjust the rate on a per-window basis has been defined, making use of a short-term prediction of the network delay to keep the probability of playback buffer starvation lower than a desired threshold during each window. Finally, a framework for mutual authentication in web applications has been proposed and evaluated. It integrates an IBA password technique with a challenge-response scheme based on a shared secret key for image scrambling. The wireless environment is mainly addressed by the proposed system, which tries to overcome the severe constraints on security, data transmission capability and user friendliness imposed by such environment

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

User Satisfaction with Personalised Internet Applications

The study focuses on user satisfaction with websites and personalised internet applications in particular. The abundance of information on the web is increasing more and more. Therefore, the significance of websites targeting the users’ preferences, like personalised Internet applications, is rising. The aim of this study was to find out which factors determine user satisfaction with personalised internet applications. Factors like the usefulness of the information or trust towards how personal information is handled were considered. A large-scale user survey evaluating three internet applications (from the travel, e-learning and real estate domains) was conducted. Expert opinions were collected to complement the results and provide insights from users’ and experts’ points of views

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Design and evaluation issues for user-centric online product search

Nowadays more and more people are looking for products online, and a massive amount of products are being sold through e-commerce systems. It is crucial to develop effective online product search tools to assist users to find their desired products and to make sound purchase decisions. Currently, most existing online product search tools are not very effective in helping users because they ignore the fact that users only have limited knowledge and computational capacity to process the product information. For example, a search tool may ask users to fill in a form with too many detailed questions, and the search results may either be too minimal or too vast to consider. Such system-centric designs of online product search tools may cause some serious problems to end-users. Most of the time users are unable to state all their preferences at one time, so the search results may not be very accurate. In addition, users can either be impatient to view too much product information, or feel lost when no product appears in the search results during the interaction process. User-centric online product search tools can be developed to solve these problems and to help users make buying decisions effectively. The search tool should have the ability to recommend suitable products to meet the user's various preferences. In addition, it should help the user navigate the product space and reach the final target product without too much effort. Furthermore, according to behavior decision theory, users are likely to construct their preferences during the decision process, so the tool should be designed in an interactive way to elicit users' preferences gradually. Moreover, it should be decision supportive for users to make accurate purchasing decisions even if they don't have detail domain knowledge of the specific products. To develop effective user-centric online product search tools, one important task is to evaluate their performance so that system designers can obtain prompt feedback. Another crucial task is to design new algorithms and new user interfaces of the tools so that they can help users find the desired products more efficiently. In this thesis, we first consider the evaluation issue by developing a simulation environment to analyze the performance of generic product search tools. Compared to earlier evaluation methods that are mainly based on real-user studies, this simulation environment is faster and less expensive. Then we implement the CritiqueShop system, an online product search tool based on the well-known critiquing technique with two aspects of novelties: a user-centric compound critiquing generation algorithm which generates search results efficiently, and a visual user interface for enhancing user's satisfaction degree. Both the algorithm and the user interface are validated by large-scale comparative real-user studies. Moreover, the collaborative filtering approach is widely used to help people find low-risk products in domains such as movies or books. Here we further propose a recursive collaborative filtering approach that is able to generate search results more accurately without requiring additional effort from the users