Ad hoc routing inherently serves two separate and conflicting divisions of interest: the needs of the user or individual and the needs of the network or community. These interests should be secured differently. The proposed research is a dual-agent approach for securing ad hoc routing protocols. This approach assumes a physical division of tasks into user agent tasks and tasks performed by a trustworthy network agent. The research, motivated by the need to reduce the tasks of the network agent, investigates strategies for an optimal division of labor while promoting the faithful execution of the routing protocol. This investigation employs the dual-agent approach for securing a variant of distance vector routing.

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Modern robotics demonstrates the reality of near sci-fi solutions regularly. Swarms of interconnected robotic agents have been proven to have benefits in scalability, robustness, and efficiency. In communication restricted environments, such teams of robots are often required to support their own navigation, planning, and decision making processes, through use of onboard processors and collaboration. Example scenarios that exhibit restriction include unmanned underwater surveys and robots operating in indoor or remote environments without cloud connectivity. We begin this thesis by discussing multi-agent state estimation and it's observability properties, specifically for the case of an agent-to-agent range measurement system. For this case, inspired by navigation requirements underwater, we derive several conditions under which the system's state is guaranteed to be locally weakly observable. Ensuring a state is observable is necessary to maintain an estimate of it via filters, thus observability is required to support higher level navigation and planning. We conclude this section by creating an observability-based planner to control a subset of the agents' inputs. For the next contribution, we discuss scalability for coverage maximizing path planners. Typically planning for many individual robots incurs significant computational complexity which increases exponentially with the number of agents, this is often exacerbated when the objective function is collaborative as in coverage optimization. To maintain feasibility while planning for a large team of robots, we call upon a powerful relation from combinatorics which utilizes the greedy selection algorithm and a matroid condition to create an efficient planner that maintains a fixed performance ratio when compared to the optimal path. We then introduce a motivating example of autonomously assisted search and rescues using multiple aerial agents, and derive planners and models to suit the application. The framework begins by estimating the likely locations of a lost person through a Monte Carlo simulation, yielding a heatmap covering the area of interest. The heatmap is then used in combination with parametrized agent trajectories and a machine learning optimization algorithm to maximize the search efficiency. The search and rescues use case provides an excellent computational testbed for the final portion of the work. We close by discussing a computation architecture to support multi-agent system autonomy. Modern robotic autonomy results, especially computer vision and machine learning algorithms, often require large amounts of processing to yield quality results. With general purpose computing devices reaching a progression barrier, one that is not expected to be solved in the near term, increasingly devices must be designed with their end purposes in mind. To better support autonomy in multi-agent systems, we propose to use a distributed cluster of embedded processors which allows the sharing of computation and storage resources among the component members with minimal communication overhead. Our proposed architecture is composed of mature softwares already well-known in the robotics community, Kubernetes and the robot operating system, allowing ease of use and interoperability with existing algorithms. / Doctor of Philosophy / The traditional approach of robotics typically uses a single large platform capable of accomplishing all tasks assigned to it. However, it has been discovered that deploying multiple smaller platforms, each with their own processor and specific expertise, can have massive performance benefits compared to previous approaches. This development has been driven largely by readily available computing and mobility hardware. Termed as multi-agent systems, they can excel in areas that benefit from multiple perspectives, simultaneous task execution, and redundancy. In addition, planning algorithms developed for previous approaches often can map well onto multi-agent systems, provided there is adequate computational support. In cases where network or cloud connectivity is limited, teams of agents must use their own processors and sensors to make decisions and communicate. However, often an individual agent's computing hardware is limited in mass or size, thus limiting it's processing capabilities. In this work we will first discuss several multi-agent system algorithms, starting with estimation and navigation and ending with area search. We then conclude the work by proposing a novel architecture designed to distribute the computation load across the team in a highly scalable way.

Multi-Agent Systems (MASs) and Partial and Ordinary Differential Equations (PDEs and ODEs respectively) have often been employed by researchers to effectively model and simulate Complex Adaptive Systems (CASs). PDEs and ODEs are reduction based approaches which view the system globally and ignore any local interactions and processes. MASs are considered by many to be a better tool to model CASs, but have issues as well. Case in point, there is concern that present day MASs fail to capture the true essence of inter-cellular communication in a CAS. In this work we present a realistic and utilizable communication framework for inter-agent communication for a CAS simulation. We model the dynamic properties of the communication signals and show that our model is a realistic model for inter-cellular communication. We validate our system by modeling and simulating pattern formation in Dictyostelium discoideum, a unicellular organism. / Master of Science

This dissertation explores the feasibility of employing software agent technology to support large-scale assessment. The research included the design, development, and evaluation of the Assessment Agent System for assessing comprehensive understanding based on concept maps. The system was designed by following an agent-oriented software design method. The Assessment Agent System is composed of five types of software agents: instructor agent, student agent, management agent, assessment agent, and reporting agent. Each of these agents was designed to possess different capabilities. Software agents in the system, through communication and cooperation, collectively provide the functionalities of user-system interaction, user management, task authoring and management, assessment delivery, task presentation, response collection, automatic assessing with feedback, and reporting. Through the process of design, development, and evaluation of the Assessment Agent System, this study demonstrates an approach that employs an agent-oriented software design method to produce sophisticated educational software applications. Furthermore, this study explored the concept map assessing method for the Assessment Agent System. When node terms and linking phrases are provided, assessing student concept maps can be automated by comparing student concept maps with assessment criteria, proposition by proposition. However, the usefulness of the proposition-comparing method depends heavily on the accuracy and thoroughness of the criterion propositions. Therefore, assessment criteria need to be continually refined and improved through examining student-created propositions. / Ph. D.

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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.

Internet computing enables the construction of large-scale and complex applications by aggregating and sharing computational, data and other resources across institutional boundaries. The agent model can address the ever-increasing challenges of scalability and complexity, driven by the prevalence of Internet computing, by its intrinsic properties of autonomy and reactivity, which support the flexible management of application execution in distributed, open, and dynamic environments. However, the non-deterministic behaviour of autonomous agents leads to a lack of control, which complicates exception management in the system, thus threatening the robustness and reliability of the system, because improperly handled exceptions may cause unexpected system failure and crashes. / In this dissertation, we investigate and develop mechanisms to integrate intrinsic support for concurrency control, exception handling, recoverability, and robustness into multi-agent systems. The research covers agent specification, planning and scheduling, execution, and overall coordination, in order to reduce the impact of environmental uncertainty. Simulation results confirm that our model can improve the robustness and performance of the system, while relieving developers from dealing with the low level complexity of exception handling. / A survey, along with a taxonomy, of existing proposals and approaches for building robust multi-agent systems is provided first. In addition, the merits and limitations of each category are highlighted. / Next, we introduce the ARTS (Agent-Oriented Robust Transactional System) platform which allows agent developers to compose recursively-defined, atomically-handled tasks to specify scoped and hierarchically-organized exception-handling plans for a given goal. ARTS then supports automatic selection, execution, and monitoring of appropriate plans in a systematic way, for both normal and recovery executions. Moreover, we propose multiple-step backtracking, which extends the existing step-by-step plan reversal, to serve as the default exception handling and recovery mechanism in ARTS. This mechanism utilizes previous planning results in determining the response to a failure, and allows a substitutable path to start, prior to, or in parallel with, the compensation process, thus allowing an agent to achieve its goals more directly and efficiently. ARTS helps developers to focus on high-level business logic and relaxes them from considering low-level complexity of exception management. / One of the reasons for the occurrence of exceptions in a multi-agent system is that agents are unable to adhere to their commitments. We propose two scheduling algorithms for minimising such exceptions when commitments are unreliable. The first scheduling algorithm is trust-based scheduling, which incorporates the concept of trust, that is, the probability that an agent will comply with its commitments, along with the constraints of system budget and deadline, to improve the predictability and stability of the schedule. Trust-based scheduling supports the runtime adaptation and evolvement of the schedule by interleaving the processes of evaluation, scheduling, execution, and monitoring in the life cycle of a plan. The second scheduling algorithm is commitment-based scheduling, which focuses on the interaction and coordination protocol among agents, and augments agents with the ability to reason about and manipulate their commitments. Commitment-based scheduling supports the refactoring and parallel execution of commitments to maximize the system's overall robustness and performance. While the first scheduling algorithm needs to be performed by a central coordinator, the second algorithm is designed to be distributed and embedded into the individual agent. / Finally, we discuss the integration of our approaches into Internet-based applications, to build flexible but robust systems. Specifically, we discuss the designs of an adaptive business process management system and of robust scientific workflow scheduling.