Multiagent simulation is gaining popularity due to its intuitiveness and ability in coping with domain complexity. HLA, being a distributed simulation architecture standard, is a good candidate for implementing a multiagent simulation infrastructure on, provided that agent communication can be implemented. HLA, being a standard designed towards a wide coverage of simulation system architectures and styles, is not an easy system to master. In this thesis, an abstraction layer called the Federate Abstraction Layer (FAL) is described for better engineering of software systems participating in an HLA simulation, providing lower project risks for the project manager and ease of use for the C++ programmers. The FAL is in use in project SAVMOS in Modelling and Simulation Laboratory. Discussion of FAL is followed by discussion of the study for realizing KQML for use in multiagent architectures to be built on top of HLA as the data transfer medium. The results are demonstrated with 10 federates implemented using the FAL.

The few past years have witnessed a huge leap in the field of the smart grid communication networks in which many theories are being developed, and many applications are being evolved to accommodate the implementation of the smart grid concepts. Distribution power systems are considered to be one of the first leading fields having the strong desire of applying the smart grid concepts; resulting in the emersion of the smart distribution power systems, which are the future visualization of the distribution systems having both the ability of smart acting, and the capabilities of automation, self-healing, and decentralized control. For the sake of the real implementation of the smart distribution power systems, the main functions performed by the traditional systems have to be performed by the new smart systems as well, taking into account the new features and properties of those smart systems. One of those main functions is the ability of power networks optimal reconfiguration to minimize the system’s power loss while preserving the system radial topology. The proposed reconfiguration methodology targets the utilization of a hybrid genetic algorithm with two fuzzy controllers that could converge to the global optimal network configuration with the fastest convergence rate consuming the least computational time. The first fuzzy controller is designed to reject any infeasible system configurations that might show up in the population of the genetic algorithm and violate the system radial topology, while the second fuzzy controller is designed to adapt the mutation rate of the genetic algorithm. Consequently, a novel multi-agent system is proposed and designed to perform the reconfiguration application in smart distribution power systems employing the concepts of distributed processing and decentralized control demanded by those systems. A multi-agent system employs a group of intelligent agents that have the capabilities of autonomy, reactivity, pro-activity, and sociality. Those agents cooperate with each other in order to perform a certain function through their powerful abilities to communicate, socialize, and make a common decision in a decentralized fashion based on the information retrieved from the surrounding environment and compiles with their ultimate objective.

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Software systems involving autonomous interacting software entities (or agents) present new challenges in computer science and software engineering. A particularly challenging problem is the engineering of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is concerned with negotiation: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, I focus on settings where agents have limited or uncertain information, precluding them from making optimal individual decisions. I demonstrate that this form of bounded-rationality may lead agents to sub-optimal negotiation agreements. I argue that rational dialogue based on the exchange of arguments can enable agents to overcome this problem. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs and planning knowledge, then rational dialogue over these reasons can enable agents to refine their individual decisions and consequently reach better agreements. I refer to this form of interaction as “interested-based negotiation.” (For complete abstract open document)

A multi-agent system consists of a collection of agents that interact with each other to fulfil their tasks. Individual agents can have different motivations for engaging in interactions. Also, agents can possibly recognise the goals of the other participants in the interaction. To successfully interact, an agent should exhibit the ability to balance reactivity, pro-activeness (autonomy) and sociability. That is, individual agents should deliberate not only on what they themselves know about the working environment and their desires, but also on what they know about the beliefs and desires of the other agents in their group. Multi-agent systems have proven to be a useful tool for modelling and solving problems that exhibit complex and distributed structures. Examples include real-time traffic control and monitoring, work-flow management and information retrieval in computer networks. There are two broad challenges that the agent community is currently investigating. One is the development of the formalisms for representing the knowledge the agents have about their actions, goals, plans for achieving their goals and other agents. The second challenge is the development of the reasoning mechanisms agents use to achieve autonomy during the course of their interactions. Our research interests lie in a model for the interactions among the agents, whereby the behaviour of the individual agents can be specified in a declarative manner and these specifications can be made executable. Therefore, we investigate the methods that effectively represent the agents' knowledge about their working environment (which includes other agents), to derive unrealised information from the agents' knowledge by considering that the agents can obtain only a partial image of their working environment. The research also deals with the logical reasoning about the knowledge of the other agents to achieve a better interaction. Our approach is to apply the notions of modality and non-monotonic reasoning to formalise and to confront the problem of incomplete and conflicting information when modelling multi-agent systems. The approach maintains the richness in the description of the logical method while providing an efficient and easy-to-implement reasoning mechanism. In addition to the theoretical analysis, we investigate n-person argumentation as an application that benefits from the efficiency of our approach.

Doctor of Philosophy (PhD) / This research aims at developing a different kind of virtual world that is dynamically designed and implemented as needed. Currently, most virtual world designs are considered static. Similar to the physical world, these worlds are pre-defined prior to their use. The resultant environments serve certain purposes but do not take into consideration possible changes to the purposes during their use, changes which often occur when the occupants interact with the environments and with each other. Virtual worlds as networked environments can be flexibly configured and programmed. This flexibility makes it possible to consider virtual world designs in terms of dynamics and autonomy, reflecting the changing needs of different moments. To achieve dynamic designs of virtual worlds, this study applies a computational approach using rational design agents. A Generative Design Agent (GDA) model is developed that specifies computational processes for reasoning and designing in virtual worlds. The GDAs serve as personal design agents to the virtual world occupants. Design formalisms for virtual worlds are also addressed. The design component of a GDA is supported by the application of a generative design grammar. On one hand, generative design grammars serve as the generative force to be applied by the GDAs for virtual world design automation. On the other hand, each grammar defines coherent stylistic characterisations shared by the virtual world designs it generates. The technical outcomes of the research consist of the GDA model and a generative design grammar framework. The framework provides guidelines and strategies to designers for developing generative design grammars that produce different design languages for virtual worlds, rather than predefine every detail of all possible virtual world designs. GDAs monitor the virtual worlds and the various activities that occur in the worlds, interpret the occupants’ needs in the virtual worlds and the state of the worlds based on these observations, hypothesise design goals in order to satisfy these needs, and finally apply generative design grammars to provide virtual world designs for the moment, or initiate other actions in the worlds, according to the current design goals, on behalf of the occupants. The development of the GDA model and the generative design grammar framework provides new perspectives for understanding and developing virtual worlds. The GDA model challenges the conventional way that virtual worlds are designed and implemented, and this leads to dynamic designs of virtual worlds. The generative design grammar framework provides a computational approach to formally defining design languages for virtual worlds.

Bonn, University, Diss., 2006.

Zugl.: München, Techn. Univ., Diss., 2007