The field of adaptive hypermedia is a little over a decade old. It has a rich history in a range of fields such as artificial intelligence, user modelling, intelligent tutoring systems and hypertext. Early adaptive hypermedia work concentrated on application-led research; developing a range of systems for specific purposes. In 1996, Peter Brusilovsky reviewed the state-of-the-art and proposed a taxonomy of adaptive hypermedia techniques, thereby providing the means to categorise adaptive hypermedia systems. Since then, several practical frameworks for adaptive hypermedia applications have been produced, in addition to formal models for formalising adaptive hypermedia applications. This thesis presents a new framework for adaptive hypermedia systems based on agent technology, a field of research largely ignored within the adaptive community. Conceptually, this framework occupies a middle ground between the formal reference models for adaptive hypermedia and application-specific frameworks. This framework provides the means to implement formal models using variety of architectural approaches. Three novel adaptive hypermedia applications have been developed around this agent-based framework. Each system employs different architectural structures, they model the user with a variety of profiling techniques, and each provides a different set of adaptive features. The diversity of these three systems emphasises the flexibility and functionality of this proposed agent-based framework.

Dynamic selection and composition of autonomous and loosely-coupled Web services is increasingly used to automate business processes. The typical long-running characteristic of business processes imposes new management challenges such as dynamic adaptation of running process instances. However, current process orchestration engines provide limited flexibility to dynamically adapt to changing runtime conditions (e.g., presence of faults). Additionally, current process specification languages exhibit some limitations regarding modularity of crosscutting management concerns. In particular, monitoring and adaptation logic is often scattered across several process definitions and intertwined with the business logic. This leads to monolithic and complex processes that are hard to understand, reuse, maintain, and evolve. To address these limitations, we developed a policy-based change management framework, named Manageable and Adaptable Service Compositions (MASC), to declaratively express crosscutting monitoring and process adaptation concerns in a separate and modular way. MASC policies use a set of simple, but flexible and relatively powerful, constructs to declaratively specify policies that govern: (1) discovery and selection of services to be used, (2) monitoring to detect the need for adaptation, (3) reconfiguration and adaptation of the process to handle special cases (e.g., context-dependant behaviour) and recover from typical faults in service-based processes. The identified constructs are executed by a lightweight service-oriented management middleware named MASC middleware. The adaptation is transparent because it preserves the original functional behaviour of the business process and does not tangle the adaptation logic with that of the business process. Additionally, policies do not have to be necessarily defined when designing the process; they can also be introduced later during deployment or at runtime. We implemented a MASC proof-of-concept prototype and evaluated it on Stock Trading case study scenarios. We conducted extensive studies to demonstrate the feasibility of the proposed techniques and illustrate the benefits of our approach in providing adaptive composite services using the policy-based approach. Our performance and scalability studies indicate that MASC middleware is scalable and the introduced overhead are acceptable.

Dynamic selection and composition of autonomous and loosely-coupled Web services is increasingly used to automate business processes. The typical long-running characteristic of business processes imposes new management challenges such as dynamic adaptation of running process instances. However, current process orchestration engines provide limited flexibility to dynamically adapt to changing runtime conditions (e.g., presence of faults). Additionally, current process specification languages exhibit some limitations regarding modularity of crosscutting management concerns. In particular, monitoring and adaptation logic is often scattered across several process definitions and intertwined with the business logic. This leads to monolithic and complex processes that are hard to understand, reuse, maintain, and evolve. To address these limitations, we developed a policy-based change management framework, named Manageable and Adaptable Service Compositions (MASC), to declaratively express crosscutting monitoring and process adaptation concerns in a separate and modular way. MASC policies use a set of simple, but flexible and relatively powerful, constructs to declaratively specify policies that govern: (1) discovery and selection of services to be used, (2) monitoring to detect the need for adaptation, (3) reconfiguration and adaptation of the process to handle special cases (e.g., context-dependant behaviour) and recover from typical faults in service-based processes. The identified constructs are executed by a lightweight service-oriented management middleware named MASC middleware. The adaptation is transparent because it preserves the original functional behaviour of the business process and does not tangle the adaptation logic with that of the business process. Additionally, policies do not have to be necessarily defined when designing the process; they can also be introduced later during deployment or at runtime. We implemented a MASC proof-of-concept prototype and evaluated it on Stock Trading case study scenarios. We conducted extensive studies to demonstrate the feasibility of the proposed techniques and illustrate the benefits of our approach in providing adaptive composite services using the policy-based approach. Our performance and scalability studies indicate that MASC middleware is scalable and the introduced overhead are acceptable.

Recommendations are crucial for the success of large websites. While there are many ways to determine recommendations, the relative quality of these recommenders depends on many factors and is largely unknown. We present the architecture and implementation of AWESOME (Adaptive website recommendations), a data warehouse-based recommendation system. It allows the coordinated use of a large number of recommenders to automatically generate website recommendations. Recommendations are dynamically selected by efficient rule-based approaches utilizing continuously measured user feedback on presented recommendations. AWESOME supports a completely automatic generation and optimization of selection rules to minimize website administration overhead and quickly adapt to changing situations. We propose a classification of recommenders and use AWESOME to comparatively evaluate the relative quality of several recommenders for a sample website. Furthermore, we propose and evaluate several rule-based schemes for dynamically selecting the most promising recommendations. In particular, we investigate two-step selection approaches that first determine the most promising recommenders and then apply their recommendations for the current situation. We also evaluate one-step schemes that try to directly determine the most promising recommendations.

A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. It is argued that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the children to be seriously involved in the test. The first part of this thesis reviews the school readiness approaches applied in various countries. This motivated the development of the sophisticated system described in the thesis. Extensive research was conducted to enrich the system with features that consider machine learning and social network aspects. A modified genetic algorithm was integrated into a web-based stealth assessment tool for school readiness. The research goal is to create a web-based stealth assessment tool that can learn the user's skills and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be presented to the user according to his/her skills and status. The modified GA and the learning procedure were described. A penalizing system and a fitness heuristic for best choice selection were integrated into the GA. Two methods for learning were presented, namely a memory system and a no-memory system. Several methods were presented for the improvement of the speed of learning. In addition, learning mechanisms were introduced in the social network aspect to address further usage of stealth assessment automation. The effect of the relatives and friends on the readiness of the child was studied by investigating the social communities to which the child belongs and how the trend in these communities will reflect on to the child under investigation. The plan is to develop this framework further by incorporating more information related to social network construction and analysis. Also, it is planned to turn the framework into a self adaptive one by utilizing the feedback from the usage patterns to learn and adjust the evaluation process accordingly.

Las Aplicaciones Web Adaptativas son sistemas Web que plantean una solución a esta problemática, mediante la adaptación automática del acceso a ítems de información, servicios e hiperlinks, en base a las características de los usuarios. El desarrollo de estos sistemas exige adoptar una aproximación ingenieril que facilite la especificación de las funcionalidades adaptativas a proveer, junto con las características de los usuarios en las cuales se basan dichas funcionalidades. La presente tesis introduce una aproximación al desarrollo de Aplicaciones Web Adaptativas desde una perspectiva dirigida por modelos. Esta aproximación integra prácticas tradicionales de desarrollo de Aplicaciones Web con conceptos de-nidos y probados por la comunidad de Hipermedia Adaptativa. Tomando como base el proceso de desarrollo de aplicaciones Web OOWS (Object Oriented Web Solutions), se defíne un conjunto de primitivas conceptuales que permiten expresar técnicas adaptativas a un alto nivel de abstracción. La definición de estas primitivas es respaldada por una propuesta de Modelado de Usuarios. Además, un conjunto de estrategias de modelado permite incorporar Métodos Adaptativos a los esquemas navegacionales de OOWS, en base a dichas primitivas. / Rojas Durán, GE. (2008). Modelling adaptive web applications in OOWS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/2000 / Palancia

A major challenge that faces most families is effectively anticipating how ready to start school a given child is. Traditional tests are not very effective as they depend on the skills of the expert conducting the test. It is argued that automated tools are more attractive especially when they are extended with games capabilities that would be the most attractive for the children to be seriously involved in the test. The first part of this thesis reviews the school readiness approaches applied in various countries. This motivated the development of the sophisticated system described in the thesis. Extensive research was conducted to enrich the system with features that consider machine learning and social network aspects. A modified genetic algorithm was integrated into a web-based stealth assessment tool for school readiness. The research goal is to create a web-based stealth assessment tool that can learn the user's skills and adjust the assessment tests accordingly. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be presented to the user according to his/her skills and status. The modified GA and the learning procedure were described. A penalizing system and a fitness heuristic for best choice selection were integrated into the GA. Two methods for learning were presented, namely a memory system and a no-memory system. Several methods were presented for the improvement of the speed of learning. In addition, learning mechanisms were introduced in the social network aspect to address further usage of stealth assessment automation. The effect of the relatives and friends on the readiness of the child was studied by investigating the social communities to which the child belongs and how the trend in these communities will reflect on to the child under investigation. The plan is to develop this framework further by incorporating more information related to social network construction and analysis. Also, it is planned to turn the framework into a self adaptive one by utilizing the feedback from the usage patterns to learn and adjust the evaluation process accordingly.