Musical Agents are an emerging technology, designed to provide a range of new musical opportunities to human musicians and composers. Current systems in this area lack certain features which are necessary for a high quality musician; in particular, they lack the ability to structure their output in terms of a communicative dialogue, and reason about the responses of their partners. In order to address these issues, this thesis develops Musical Act Theory (MAT). This is a novel theory, which models musical interactions between agents, allowing a dialogue oriented analysis of music, and an exploration of intention and communication in the context of musical performance. The work here can be separated into four main contributions: a specification for a Musical Middleware system, which can be implemented computationally, and allows distributed agents to collaborate on music in real-time; a computational model of musical interaction, which allows musical agents to analyse the playing of others as part of a communicative process, and formalises the workings of the Musical Middleware system; MAMA, a musical agent system which embodies this theory, and which can function in a variety of Musical Middleware applications; a pilot experiment which explores the use of MAMA and the utility of MAT under controlled conditions. It is found that the Musical Middleware architecture is computationally implementable, and allows for a system which can respond to both direct musical communi- cation and extramusical inputs, including the use of a custom-built tangible interface. MAT is found to capture certain aspects of music which are of interest — an intuitive notion of performative actions in music, and an existing model of musical interaction. Finally, the fact that a number of different levels — theory, architecture and implementation — are tied together gives a coherent model which can be applied to many computational musical situations.

Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web. / Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web.

The adaptiveness provides uniquely identifying and monitoring the learner&rsquo / s learning activities according to his/her respective profile. The adaptive intelligent learning management systems (AILMS) help a wide range of students to achieve their learning goals effectively by delivering knowledge in an adaptive or individualized style through online learning settings. This study presents a multi-agent system, called MODA, developed to provide adaptiveness in learning management systems (LMS). A conceptual framework for adaptive learning systems is proposed for this purpose. The framework is based on the idea that adaptiveness is the best matching between the learner profile and the course content profile. The learning styles of learners and the content type of learning material are used to match the learner to the most suitable content. The thesis covers the pedagogical framework applied in MODA, the technical and multi-agent architectures of MODA, the TCP-IP based protocol providing communication between MODA and LMS, and a sample application of the system to an open source learning management system, OLAT. The study also discusses the possibilities of future interests.

This work focuses on the need for new knowledge to allow hierarchical multi-project management to be conducted in the construction industry, which is characterised by high uncertainty, fragmentation, complex decisions, dynamic changes and long-distance communication. A dynamic integrated project management approach is required at strategic, tactical and operational levels in order to achieve adaptability. The work sees the multi-project planning and control problem in the context of supply chain management at main contractor companies. A portfolio manager must select and prioritise the projects, bid and negotiate with a wide range of clients, while project managers are dealing with subcontractors, suppliers, etc whose relationships and collaborations are critical to the optimisation of schedules in which time, cost and safety (etc) criteria must be achieved. Literature review and case studies were used to investigate existing approaches to hierarchical multi-project management, to identify the relationships and interactions between the parties concerned, and to investigate the possibilities for integration. A system framework was developed using a multi-agent-system architecture and utilising procedures adapted from literature to deal with short, medium and long-term planning. The framework is based on in-depth case study and integrates time-cost trade-off for project optimisation with multi-attribute utility theory to facilitate project scheduling, subcontractor selection and bid negotiation at the single project level. In addition, at the enterprise level, key performance indicator rule models are devised to align enterprise supply chain configuration (strategic decision) with bid selection and bid preparation/negotiation (tactical decision) and project supply chain selection (operational decision). Across the hierarchical framework the required quantitative and qualitative methods are integrated for project scheduling, risk assessment and subcontractor evaluation. Thus, experience sharing and knowledge management facilitate project planning across the scattered construction sites. The mathematical aspects were verified using real data from in-depth case study and a test case. The correctness, usefulness and applicability of the framework for users was assessed by creating a prototype Multi Agent System-Decision Support System (MAS-DSS) which was evaluated empirically with four case studies in national, international, large and small companies. The positive feedback from these cases indicates strong acceptance of the framework by experienced practitioners. It provides an original contribution to the literature on planning and supply chain management by integrating a practical solution for the dynamic and uncertain complex multi-project environment of the construction industry.

Over the past few years, mining data located in heterogeneous and geographically distributed sites have been designated as one of the key important issues. Loading distributed data into centralized location for mining interesting rules is not a good approach. This is because it violates common issues such as data privacy and it imposes network overheads. The situation becomes worse when the network has limited bandwidth which is the case in most of the real time systems. This has prompted the need for intelligent data analysis to discover the hidden information in these huge amounts of distributed databases. In this research, we present an incremental approach for building an efficient Multi-Agent based algorithm for mining real world databases in geographically distributed sites. First, we propose the Distributed Multi-Agent Association Rules algorithm (DMAAR) to minimize the all-to-all broadcasting between distributed sites. Analytical calculations show that DMAAR reduces the algorithm complexity and minimizes the message communication cost. The proposed Multi-Agent based algorithm complies with the Foundation for Intelligent Physical Agents (FIPA), which is considered as the global standards in communication between agents, thus, enabling the proposed algorithm agents to cooperate with other standard agents. Second, the BitTable Multi-Agent Association Rules algorithm (BMAAR) is proposed. BMAAR includes an efficient BitTable data structure which helps in compressing the database thus can easily fit into the memory of the local sites. It also includes two BitWise AND/OR operations for quick candidate itemsets generation and support counting. Moreover, the algorithm includes three transaction trimming techniques to reduce the size of the mined data. Third, we propose the Pruning Multi-Agent Association Rules algorithm (PMAAR) which includes three candidate itemsets pruning techniques for reducing the large number of generated candidate itemsets, consequently, reducing the total time for the mining process. The proposed PMAAR algorithm has been compared with existing Association Rules algorithms against different benchmark datasets and has proved to have better performance and execution time. Moreover, PMAAR has been implemented on real world distributed medical databases obtained from more than one hospital in Egypt to discover the hidden Association Rules in patients' records to demonstrate the merits and capabilities of the proposed model further. Medical data was anonymously obtained without the patients' personal details. The analysis helped to identify the existence or the absence of the disease based on minimum number of effective examinations and tests. Thus, the proposed algorithm can help in providing accurate medical decisions based on cost effective treatments, improving the medical service for the patients, reducing the real time response for the health system and improving the quality of clinical decision making.