Modelling financial investment planning from agent perspectives

Multi-agent Systems (MASs) offer strong models for representing complex and dynamic real-world environments. Taking financial investment planning as an example, this paper describes how to model complex systems from agent perspectives. Different agents and their behaviours are identified for financial investment planning. These agents are put together as an agent-based system. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that (MASs) can significantly facilitate the modelling of complex systems.

Incorporating track records of agents into matchmaking in middle agents

Middle agents play a very important role in multiagent systems used in open and dynamic environments. The performance of middle agents relies heavily on the matchmaking algorithms used. The actual performance of provider agents in accomplishing delegated tasks has a significant impact on the matchmaking outcomes of middle agents. With those observations in mind, this paper discusses the incorporation of the track records of agents in accomplishing delegated tasks into the matchmaking process. How to provide initial values for track records in the algorithm are detailed. A prototype is also built to verify the algorithm. Based on the improved algorithm, the matchmaking outcomes are more accurate and reasonable.

An improved trust model in agent-mediated E-commerce

Trust is a fundamental issue in multiagent systems, especially in agent-mediated e-commence. The trust model plays an important role in determining who and how to interact in open and dynamic environments. To this end, many trust models have been developed. Based on the confidence–reputation model proposed by other researchers, an improved trust model is discussed. The original model was enhanced in two aspects: (1) when evaluating the trustworthiness of target agents, the reliability of the witness itself is taken into account and further aggregated by Dempster-Shafer evidence theory and (2) the ontological property of trust is considered, which implies that trust can be calculated more precisely. A case study is provided to show the effectiveness of the improved model.

Assisting independence with "the latest and greatest" of technologies

A case study of a long term wheelchair user: This presentation will highlight the difficulties associated with matching client's environmental access, seating and mobility needs using multi-functional systems, which also happen to be the "latest and greatest" technologies. The paper addresses the issues experienced by these clinicians when prescribing systems with minimal trial history (due to their newness) and the compatibility issues encountered by the supplier when matching the new with the old of the current powered wheelchair for a particularly motivated wheelchair user.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents' track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified.

Where and when to revegetate : a quantitative method for scheduling landscape reconstruction

Restoration of native vegetation is required in many regions of the world, but determining priority locations for revegetation is a complex problem. We consider the problem of determining spatial and temporal priorities for revegetation to maximize habitat for 62 bird species within a heavily cleared agricultural region, 11 000 km2 in area. We show how a reserve-selection framework can be applied to a complex, large-scale restoration-planning problem to account for multi-species objectives and connectivity requirements at a spatial extent and resolution relevant to management. Our approach explicitly accounts for time lags in planting and development of habitat resources, which is intended to avoid future population bottlenecks caused by delayed provision of critical resources, such as tree hollows. We coupled species-specific models of expected habitat quality and fragmentation effects with the dynamics of habitat suitability following replanting to produce species-specific maps for future times. Spatial priorities for restoration were determined by ranking locations (150-m grid cells) by their expected contribution to species habitat through time using the conservation planning tool, ‘‘Zonation.’’ We evaluated solutions by calculating expected trajectories of habitat availability for each species. We produced a spatially explicit revegetation schedule for the region that resulted in a balanced increase in habitat for all species. Priority areas for revegetation generally were clustered around existing vegetation, although not always. Areas on richer soils and with high rainfall were more highly ranked, reflecting their potential to support high-quality habitats that have been disproportionately cleared for agriculture. Accounting for delayed development of habitat resources altered the rank-order of locations in the derived revegetation plan and led to improved expected outcomes for fragmentation-sensitive species. This work demonstrates the potential for systematic restoration planning at large scales that accounts for multiple objectives, which is urgently needed by land and natural resource managers.

On the geometry of localization, tracking and navigation

The thesis characterizes the geometry of certain localization, tracking and navigation problems. For instance, the potential localization performance of certain multi-sensor systems is measurably linked to the relative sensor-target geometry. Additionally, several optimal and robust algorithms for localization and tracking are designed which exploit the underlying geometry of the problem considered.

Optimization-based formation of autonomous mobile robots

The formation of autonomous mobile robots to an arbitrary geometric pattern in a distributed fashion is a fundamental problem in formation control. This paper presents a new asynchronous, memoryless (oblivious) algorithm to the formation problem via distributed optimization techniques. The optimization minimizes an appropriately defined difference function between the current robot distribution and the target geometric pattern. The optimization processes are performed independently by individual robots in their local coordinate systems. A movement strategy derived from the results of the distributed optimizations guarantees that every movement makes the current robot configuration approaches the target geometric pattern until the final pattern is reached.

Mobile agent tracking with single frequency continuous wave radar : a linear robust filtering based approach

3-alkylpyrrole to the fabric surface. Direct applications of a conductive paint to the textile surface eliminate the exposure of the substrate to damaging oxidizing agents which allow the coating of more sensitive and delicate substrates. All textiles produced are tested for abrasion resistance and conductivity. For alkyl polypyrrole coated fabrics, the optimum carbon chain lengths are between n=10 and n=14, which result in optimum values of conductivity and solubility. The darkness of the tone is inversely related to the surface resistivity of the resulting conductive fabric. Therefore, deep black coatings have low resistivity whereas light gray coatings on a white fabric surface have higher surface resistivity. Longer alkyl chains result in higher surface resistivity in fabrics. The conductive coating of poly(3-decanylpyrrole) on the textile surface has a better abrasion resistance compared to that of an unsubstituted polypyrrole coating.

A framework for co-ordination and learning among teams of agents

We present a framework for team coordination under incomplete information based on the theory of incomplete information games. When the true distribution of the uncertainty involved is not known in advance, we consider a repeated interaction scenario and show that the agents can learn to estimate this distribution and share their estimations with one another. Over time, as the set of agents' estimations become more accurate, the utility they can achieve approaches the optimal utility when the true distribution is known, while the communication requirement for exchanging the estimations among the agents can be kept to a minimal level.

Seabird and marine mammal management options in the face of climate change

Climate change is already impacting Australia’s oceans. Responses by marine life to both climate variability and change have been documented for low trophic levels, however, responses for Australia’s iconic higher trophic level marine taxa are poorly understood, including for many conservation-dependent seabirds and marine mammals. We report initial results from a national study evaluating impacts an adaptation options. Individual time series and combined analyses show consistent responses to historical climate signals, however, improved monitoring protocols are needed to maximize detection of any climate-related demographic signals. Despite difference in sampling , the development of regional multi-species-indices of environmental change provides robust climate indicators over large regions.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

An overview of recent advances in intrusion detection

The intrusion detection system is one of the security defense tools for computer networks. In recent years this research has lacked in direction and focus. In this paper we present a survey on the recent progression of multiagent intrusion detection systems. We survey the existing types, techniques and architectures of Intrusion Detection Systems in the literature. Finally we outline the present research challenges and issues