代理者（Agent）是一個自主性的軟體程式，可以幫助代表人類在網際網路上從事各種的電子化服務（E-Service）。由於目前多代理者系統缺少了安全管理的機制，以致於目前為止代理者代表人類在網上從事活動的行為還不被大家接受。因此，我們提出了一套以代理者為導向的公鑰匙架構（Agent-Oriented Public Key Infrastructure, APKI），各式各樣的數位憑證被產生、儲存、註銷及驗證，以滿足不同存取控制的需求。例如，代理者的認證是以代理者身份憑證為基礎，而授權的部分則以授權憑證或屬性憑證來做驗證。透過這些數位憑證，我們可以在虛擬網路上的代理者之間建立一條信任路徑，一個安全的電子化服務的實際應用範例將會以此架構實作及呈現出來，以驗證我們所提架構的可行性。 / Agent is autonomous software that mediates e-service for human on the Internet. The acceptance of agent-mediated e-service (AMES) is very slow for the lacking of security management infrastructure for multi-agent system. Therefore we proposed an agent-oriented public key infrastructure (APKI) for multi-agent e-service. In this APKI, a taxonomy of digital certificates are generated, stored, verified, and revoked to satisfy different access and delegation control purposes. Agent identity certificate was designed for agent’s authentication whereas attributed and agent authorization certificates were proposed for agent’s authorization and delegation. Using these digital certificates, we establish agent trust relationships on the cyberspace. A trusted agent-mediated e-service scenario will be shown to demonstrate the feasibility of our APKI.

Multi-agent Systems (MAS) provide an increasingly relevant field of research due to their many applications to modelling real world situations where the behaviour of many individual, self-motivated, agents must be reasoned about and controlled. The problem of agent social reasoning is central to MAS, where an agent reasons about its actions and interactions with other agents. This is the most important component of MAS, as it is the interactions, cooperation and competition between agents that make MAS a powerful approach suited for tackling many complex problems. Existing work focuses either on specific types of social reasoning or general purpose agent practical reasoning - reasoning directed toward actions. This thesis argues that social reasoning should be considered separately from practical reasoning. There are many possible benefits to this separation compared to existing approaches. Principally, it can allow general algorithms for agent implementation, analysis and bounded reasoning. This viewpoint is motivated by the desire to implement social reasoning agents and allow for a more general theory of social reasoning in agents. This thesis presents the novel Expectation- Strategy-Behaviour (ESB) framework for social reasoning, which provides a generic way to specify and execute agent reasoning approaches. ESB is a powerful tool, allowing an agent designer to write expressive social reasoning specifications and have a computational model generated automatically. Through a formalism and description of an implemented reasoner based on this theory it is shown that it is possible and beneficial to implement a social reasoning engine as a complementary component to practical reasoning. By using ESB to specify, and then implement, existing social reasoning schemes for joint commitment and normative reasoning, the framework is shown to be a suitable general reasoner. Examples are provided of how reasoning can be bounded in an ESB agent and the mechanism to allow analysis of agent designs is discussed. Finally, there is discussion on the merits of the ESB solution and possible future work.

In this thesis an architecture, similar to subsumption architectures, is presented which uses low level behaviour modules, based on combinations of machine learning techniques, to create teams of autonomous agents cooperating via shared plans for interaction. The purpose of this is to perform effective single plan execution within multiple scenarios, using a modern team based first person shooter video game as the domain and visualiser. The main focus is showing that through basic machine learning mechanisms, applied in a multi-agent setting on sparse data, plans can be executed on game levels of varying size and shape without sacrificing team goals. It is also shown how different team members can perform locally sub-optimal operations which contribute to a globally better strategy by adding exploration data to the machine learning mechanisms. This contributes to the reinforcement learning problem of exploration versus exploitation, from a multi-agent perspective.

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Decision making problems are amongst the most common challenges facing managers at different management levels in the organisation: strategic, tactical, and operational. However, prior reaching decisions at the operational level of the management hierarchy, operations management departments frequently have to deal with the optimisation process to evaluate the available decision alternatives. Industries with complex supply chain structures and service organisations that have to optimise the utilisation of their resources are examples. Conventionally, operational decisions used to be taken centrally by a decision making authority located at the top of a hierarchically-structured organisation. In order to take decisions, information related to the managed system and the affecting externalities (e.g. demand) should be globally available to the decision maker. The obtained information is then processed to reach the optimal decision. This approach usually makes extensive use of information systems (IS) containing myriad of optimisation algorithms and meta-heuristics to process the high amount and complex nature of data. The decisions reached are then broadcasted to the passive actuators of the system to put them in execution. On the other hand, recent advancements in information and communication technologies (ICT) made it possible to distribute the decision making rights and proved its applicability in several sectors. The market-based approach is as such a distributed decision making mechanism where passive actuators are delegated the rights of taking individual decisions matching their self-interests. The communication among the market agents is done through market transactions regulated by auctions. The system’s global optimisation, therefore, raise from the aggregated self-oriented market agents. As opposed to the centralised approach, the main characteristics of the market-based approach are the market mechanism and local knowledge of the agents. The existence of both approaches attracted several studies to compare them in different contexts. Recently, some comparisons compared the centralised versus market-based approaches in the context of transportation applications from an algorithm perspective. Transportation applications and routing problems are assumed to be good candidates for this comparison given the distributed nature of the system and due to the presence of several sources of uncertainty. Uncertainty exceptions make decisions highly vulnerable and necessitating frequent corrective interventions to keep an efficient level of service. Motivated by the previous comparison studies, this research aims at further investigating the features of both approaches and to contrast them in the context of a distributed task allocation problem in light of environmental uncertainty. Similar applications are often faced by service industries with mobile workforce. Contrary to the previous comparison studies that sought to compare those approaches at the mechanism level, this research attempts to identify the effect of the most significant characteristics of each approach to face environmental uncertainty, which is reflected in this research by the arrival of dynamic tasks and the occurrence of stochasticity delays. To achieve the aim of this research, a target optimisation problem from the VRP family is proposed and solved with both approaches. Given that this research does not target proposing new algorithms, two basic solution mechanisms are adopted to compare the centralised and the market-based approach. The produced solutions are executed on a dedicated multi-agent simulation system. During execution dynamism and stochasticity are introduced. The research findings suggest that a market-based approach is attractive to implement in highly uncertain environments when the degree of local knowledge and workers’ experience is high and when the system tends to be complex with large dimensions. It is also suggested that a centralised approach fits more in situations where uncertainty is lower and the decision maker is able to make timely decision updates, which is in turn regulated by the size of the system at hand.

This thesis presents a new approach to local decision-making in multi-agent systems with varying amounts of communication. Here, local decision-making refers to action choices which are made in a decentralized fashion by individual agents based on the information which is locally available to them. The work described here is set within the multi-agent decision process framework. Unreliable, faulty or stochastic communication patterns present a challenge to these settings which usually rely on precomputed, centralised solutions to control individual action choices. Various approximate algorithms for local decision-making are developed for scenarios with and without sequentiality. The construction of these techniques is based strongly on methods of Bayesian inference. Their performance is tested on synthetic benchmark scenarios and compared to that of a more conservative approach which guarantees coordinated action choices as well as a completely decentralized solution. In addition, the method is applied to a surveillance task based on real-world data. These simulation results show that the algorithms presented here can outperform more traditional approaches in many settings and provide a means for flexible, scalable decision-making in systems with varying information exchange between agents.

We investigate the properties of a new Multi-Agent Systems (MAS) for computer-aided composition called IPCS (pronounced “ipp-siss”) the Intermediate Performance Composition System which generates expressive performance as part of its compositional process, and produces emergent melodic structures by a novel multi-agent process. IPCS consists of a small-medium size (2 to 16) collection of agents in which each agent can perform monophonic tunes and learn monophonic tunes from other agents. Each agent has an affective state (an “artificial emotional state”) which affects how it performs the music to other agents; e.g. a “happy” agent will perform “happier” music. The agent performance not only involves compositional changes to the music, but also adds smaller changes based on expressive music performance algorithms for humanization. Every agent is initialized with a tune containing the same single note, and over the interaction period longer tunes are built through agent interaction. Agents will only learn tunes performed to them by other agents if the affective content of the tune is similar to their current affective state; learned tunes are concatenated to the end of their current tune. Each agent in the society learns its own growing tune during the interaction process. Agents develop “opinions” of other agents that perform to them, depending on how much the performing agent can help their tunes grow. These opinions affect who they interact with in the future. IPCS is not a mapping from multi-agent interaction onto musical features, but actually utilizes music for the agents to communicate emotions. In spite of the lack of explicit melodic intelligence in IPCS, the system is shown to generate non-trivial melody pitch sequences as a result of emotional communication between agents. The melodies also have a hierarchical structure based on the emergent social structure of the multi-agent system and the hierarchical structure is a result of the emerging agent social interaction structure. The interactive humanizations produce micro-timing and loudness deviations in the melody which are shown to express its hierarchical generative structure without the need for structural analysis software frequently used in computer music humanization.

Essa dissertação apresenta um mecanismo para incorporar autonomia de planejamento em modelos organizacionais multiagentes. Para tanto, propõe-se um modelo formal para representar a presença e ausência de autonomia de planejamento, utilizando dois tipos de objetivos: procedurais e declarativos. O modelo é implementado na plataforma JaCaMo na qual se realiza um estudo de caso de uma organização, onde coexistem agentes que possuem ou não possuem autonomia de planejamento. / This dissertation presents a mechanism to incorporate planning autonomy in multiagent organizational models. Therefore, we propose a formal model to represent presence or absence of planning autonomy, using two types of objectives: procedural and declarative ones. The model is implemented using the JaCaMo platform, in which an organization case study is proposed, where agents who have or do not have planning autonomy co-exist.

Essa dissertação apresenta um mecanismo para incorporar autonomia de planejamento em modelos organizacionais multiagentes. Para tanto, propõe-se um modelo formal para representar a presença e ausência de autonomia de planejamento, utilizando dois tipos de objetivos: procedurais e declarativos. O modelo é implementado na plataforma JaCaMo na qual se realiza um estudo de caso de uma organização, onde coexistem agentes que possuem ou não possuem autonomia de planejamento. / This dissertation presents a mechanism to incorporate planning autonomy in multiagent organizational models. Therefore, we propose a formal model to represent presence or absence of planning autonomy, using two types of objectives: procedural and declarative ones. The model is implemented using the JaCaMo platform, in which an organization case study is proposed, where agents who have or do not have planning autonomy co-exist.

Multi-agent systems (MAS) are emerging as a promising paradigm for conceptualizing, designing and implementing large-scale heterogeneous software systems. The key advantage of looking at components in such systems as autonomous agents is that as agents they are capable of flexible self-organization, instead of being rigidly organized by the system's architect. However, self-organization is like evolution-it takes a lot of time and the results are not guaranteed. More often than not, the system's architect has an idea about how the agents should organize themselves-what types of organizations they should form. In our work, we tried to solve the problem of modelling organizations and their roles in a MAS, independent of the particular agent platform on which the MAS will eventually run. First and foremost, we have proposed a metamodel for expressing platform-independent organization models. Furthermore, we have implemented the proposed metamodel for the Jade agent platform as a module extending this framework. Finally, we have demonstrated the use of our module by modelling three specific organizations: remote function invocation, arithmetic expression evaluation and sealed-bid auction. Our work shows how to separate the behaviour acquired through a role from the behaviour intrinsic to an agent. This...