Studies of minimalist multi-robot systems consider multiple robotic agents, each with limited individual capabilities, but with the capacity for self-organization in order to collectively perform coordinated tasks. Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Our work considers a variation of an object clustering derived from the influential ant-inspired work of Beckers, Holland and Deneubourg which proposed stigmergy as a design principle for such multi-robot systems. Since puck mechanics contribute to cluster accrual dynamics, we studied a new scenario with square objects because these pucks into clusters differently from cylindrical ones. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes - an especially challenging instance since flat edges exacerbate adhesion to boundaries - using simpler robots than previous published research. Our solution consists of two novel behaviours, Twisting and Digging, which exploit the objects’ geometry to pry boxes free from boundaries. Physical robot experiments illustrate that cooperation between twisters and diggers can succeed in forming a single central cluster. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance. Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient.

In order to understand and predict climate in a world driven by anthropogenic influences, increased understanding of natural climate variability is vital. The doctoral dissertation presented here focuses on multi-decadal climate modes, and in particular, the Atlantic Multi-decadal Oscillation (AMO), and how it is expressed in the tropical to sub-tropical western Atlantic Ocean over the last ~700 years. Multi-decadal modes have been linked to weather around the Atlantic, and have also been correlated with the occurrence of anomalous climatic events (in both temperature and precipitation), although the relationship of these modes to anthropogenic impacts is still undefined. The study area for this project encompasses South Florida, the Bahamas, and the Caribbean; however, the dissertation begins with a review article covering the current published records and reconstructions of multi-decadal variability from the Atlantic sector. Beyond this, the research turns to the reconstruction of multi-decadal variability from biogenic carbonates, including corals and sclerosponges. Because Atlantic Multi-decadal Variability (AMV) is still poorly understood and current reconstructions diverge beyond the instrumental period, the use of long-lived coral and sclerosponge specimens allows for an examination of multi-decadal variability over the last several centuries. Two multi-century records of temperature and salinity are reconstructed from the geochemistry of a coral from coastal Florida and sclerosponge collected in the Bahamas. Additional analyses from corals collected throughout the Lesser Antilles are also examined to determine their sensitivity to Atlantic AMV for future work. The final chapter of this dissertation presents a study investigating the reproducibility and fidelity of some of the geochemical proxy techniques utilized in biogenic carbonates.

Natural disasters result in loss of lives, damage to built facilities, and interruption of businesses. The losses are not instantaneous rather they continue to occur until the community is restored to a functional socio-economic entity. Hence, it is essential that policy makers recognize this dynamic aspect of the incurring losses and make realistic plans to enhance the recovery. However, this cannot take place without understanding how homeowners react to recovery signals. These signals can come in different ways: from policy makers showing their strong commitment to restore the community by providing financial support and/or restoration of lifeline infrastructure; or from the neighbors showing their willingness to reconstruct. The goal of this research is to develop a model that can account for homeowners’ dynamic interactions in both organizational and spatial domains. Spatial domain of interactions focuses on how homeowners process signals from the environment such as neighbors reconstructing and local agencies restoring infrastructure, while organizational domain of interactions focuses on how agents process signals from other stakeholders that do not directly affect the environment like insurers. The hypothesis of this study is that these interactions significantly influence decisions to reconstruct and stay, or sell and leave. A multi-agent framework is used to capture emergent behavior such as spatial patterns and formation of clusters. The developed framework is illustrated and validated using experimental data sets.

This dissertation presents several applications of the multi-scaling (multi-timing) technique to the analysis of both single and two species population models where the defining parameters vary slowly with time. Although exact solutions in such cases would be preferred, they are almost always impossible to obtain when slow variation is involved. Numerical solutions can be obtained in these cases, however they are often time consuming and offer limited insight into what is causing the behaviour we see in the solution. Here an approximation method is chosen as it gives an explicit analytic approximate expression for the solutions of such population models. The multi-scaling method was chosen because the defining parameters are varying slowly compared to the response of the system. This technique is well-established in the physical and engineering sciences literature; however, it has rarely been applied in the area of population modelling. All single species differential equation population models incorporate parameters which define the model - for example, the growth rate r and the carrying capacity k, for the Logistic model. For constant parameter values an exact solution may be found, giving the population as a function of time. However, for arbitrary time-varying parameters, exact solutions are rarely possible, and numerical solution techniques must be employed. Here we will demonstrate that for a Logistic model where the growth rate and carrying capacity both vary slowly with time, an analysis with multiple time scales leads to approximate closed form solutions that are explicit. These solutions prove to be valid for a range of parameter values and compare favourably with numerically generated ones.

Software agents and multi-agent systems (MAS) have grown into a very active area of research and commercial development activity. There are many current emerging real-world applications spanning multitude of diverse domains. In the context of agents, ontology has been widely recognised for their significant benefits to interoperability, reusability, and both development and operational aspects of agent systems and applications. Ontology-based multi-agent systems (OBMAS) exploit these advantages in providing intelligent and semantically aware applications. In addressing the lack of support for ontology in existing methodologies for multi-agent development, this thesis proposes a design methodology for the building of such intelligent multi-agent applications called MOMA. This alternative approach focuses on the development of ontology as the driving force of the development process. By allowing the domain and characteristics of utilisation and experimentation to be dictated through ontology, researchers and domain experts can specify the agent application without any knowledge of agent design and lower level programming. Through the use of a structured ontology model and the use of integrated tools, this approach contributes towards the building of semantically aware intelligent applications for use by researchers and domain experts. MOMA is evaluated through case studies in two different domains: financial services and e-Health.

This thesis offers one step in a direction that will help consumers make better choices in response to a growing demand for a more sustainable living (Grant 2008, Pollan 2008). In a world of seismic economic, environmental and social change the need for a more sustainable way of behaving is rapidly becoming a priority for mere survival (Porritt 2006). Indeed, it has been suggested that the collapse of economic growth in 2008 has primarily been the result of a dependence on outmoded models of consumption (Hamilton 2003, James 2008). The first section of the thesis documents as a narrative the shift from a label design, which is the result of a research paper, to the launch of a food brand within a university community, which is the commercial outcome of research. The second section of the thesis is the study that examines a nascent label design consisting of a list of ingredients as semiotic triggers that inform the consumer about the product at the point of purchase. The methodology is drawn from mediated discourse analysis (Scollon 2005, Norris & Jones 2005) and multimodal discourse analysis where each mode is viewed as a system of representation with rules and regularities attached to it (Kress & van Leeuwen 2006). I focus on the nascent shift in modal values of packaging design within the site of engagement of a supermarket. The site of engagement is where mediated actions at moments in time and space occur (Norris & Jones 2005). These mediated actions are the focus of attention of the relevant participants (Scollon 2005), and operate at different levels of attention (Norris 2004). The third section contains the appendices.

We investigate the problem of developing a formal language for specifying and reasoning about real-time embedded distributed computer systems. In particular we investigate the problem of developing a theoretical framework for specifying and analyzing different aspects of real-time embedded distributed coordination. In addition to the theoretical framework we also consider the practical aspects of developing real-time embedded distributed systems. (For complete abstract open document)

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying suitable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle level that demonstrates autonomous, cooperative and optimal behaviour whilst the preserv ing precision and reliability at the local vehicle level.

Software agents and multi-agent systems (MAS) have grown into a very active area of research and commercial development activity. There are many current emerging real-world applications spanning multitude of diverse domains. In the context of agents, ontology has been widely recognised for their significant benefits to interoperability, reusability, and both development and operational aspects of agent systems and applications. Ontology-based multi-agent systems (OBMAS) exploit these advantages in providing intelligent and semantically aware applications. In addressing the lack of support for ontology in existing methodologies for multi-agent development, this thesis proposes a design methodology for the building of such intelligent multi-agent applications called MOMA. This alternative approach focuses on the development of ontology as the driving force of the development process. By allowing the domain and characteristics of utilisation and experimentation to be dictated through ontology, researchers and domain experts can specify the agent application without any knowledge of agent design and lower level programming. Through the use of a structured ontology model and the use of integrated tools, this approach contributes towards the building of semantically aware intelligent applications for use by researchers and domain experts. MOMA is evaluated through case studies in two different domains: financial services and e-Health.

Master of Engineering (Research) / In many multi-objective missions there are situations when actions based on maximum information gain may not be the `best' given the overall mission objectives. In addition to properties such as entropy, information also has value, which is situationally dependent. This thesis examines the concept of information value in a multi-objective mission from an information theory perspective. A derivation of information value is presented that considers both the context of information, via a fused world belief state, and a system mission. The derived information value is used as part of the objective function for control of autonomous platforms within a framework developed for human robot cooperative control. A simulated security operation in a structured environment is implemented to test both the framework, and information value based control. The simulation involves a system of heterogeneous, sensor equipped Unmanned Aerial Vehicles (UAVs), tasked with gathering information regarding ground vehicles. The UAVs support an e ort to protect a number of important buildings in the area of operation. Thus, the purpose of the information is to aid the security operation by ensuring that security forces can deploy e ciently to counter any threat. A number of di erent local controllers using information based control are implemented and compared to a task based control scheme. The relative performance of each is examined with respect to a number of performance metrics with conclusions drawn regarding the performance and exibility of information value based control.