Building agent-based hybrid intelligent systems : a case study

Many complex problems (e.g., financial investment planning, foreign exchange trading, data mining from large/multiple databases) require hybrid intelligent systems that integrate many intelligent techniques (e.g., fuzzy logic, neural networks, and genetic algorithms). However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. Thus, this paper discusses the development of an agent-based hybrid intelligent system for financial investment planning, in which a great number of heterogeneous computing techniques/packages are easily integrated into a unifying agent framework. This shows that agent technology can indeed facilitate the development of hybrid intelligent systems.

Optimal multicast tree routing for cluster computing in hypercube interconnection networks

Cluster computation has been used in the applications that demand performance, reliability, and availability, such as cluster server groups, large-scale scientific computations, distributed databases, distributed media-on-demand servers and search engines etc. In those applications, multicast can play the vital roles for the information dissemination among groups of servers and users. This paper proposes a set of novel efficient fault-tolerant multicast routing algorithms on hypercube interconnection of cluster computers using multicast shared tree approach. We present some new algorithms for selecting an optimal core (root) and constructing the shared tree so as to minimize the average delay for multicast messages. Simulation results indicate that our algorithms are efficient in the senses of short end-to-end average delay, load balance and less resource utilizations over hypercube cluster interconnection networks.

The generation of superstructure geometry in latina temples : a hybrid approach

The Nagara tradition of temple building created a rich corpus of Latina (single-spired) temples spread across Northern India between the fifth and thirteenth centuries. Computing methods offer a distinct methodology for reconstructing the genesis and evolution of geometry in this tradition over time. This paper reports a hybrid technique, comprising three distinct computations for recovering and explaining the geometry of temples. The application of the technique enables scholars to bring together fragments of evidence, construe "best-fit" strategies and unearth implicit or hidden relationships. The advantage of this approach is that changes in assumptions and testing of geometric alternatives can be easily simulated from multiple sources of information, such as texts, sacred diagrams and individual temples.

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.

Hybrid perspectives : defining the relativity of culture and identity through migration experiences

This paper examines how culture and identity can be relatively defined through hybrid perspectives in relation to migration experiences. Addressing and portraying definitions of culture and identity is crucial in understanding how notions of such issues connect and initiate the migrant subject through new experiences, perspectives and ways of being. In enunciating the transitions from home to a new place, and elaborating on the rupture of an inherited culture and grounded identity, I refer to them through self-reflexive perspectives. The search for meaning through appraisals of cultural lineage and linguistic capital through a Diaspora, a post colonial history and lived life experiences from my home country, pre-empts the ambivalent and hybrid status in defining culture(s) and identit(ies). It is crucial to recognise how challenges for adaptation to new culture, language, societal norms, and differences in class, nationality, race and gender play specific roles in the migrant experience. My current experiences of migration to Australia are narrations of encountered difficulties, fears, inhibitions, new aspirations, perceptions and perspectives, which map an ‘identity crisis.’ From this narrative structure, I investigate through my ongoing PhD study, how my artistic expression and representations progress towards experiences, and themes that metaphorically reflect, inspire and enact the hybrid structures of culture(s) and identit(ies). Explored reflexively my representations suggest how the ‘liminal space’ or the ‘third space,’ (Bhabha, 1990) express transitions about the ‘self’ and my artistic expression, which enable further reflection and positions to emerge and extend to metaphorical expressions.

Intelligent energy management for plug-in hybrid electric vehicles : the role of ITS infrastructure in vehicle electrification

The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.

Execution of compute intensive applications on hybrid clouds (case study with mpiBLAST)

Cloud computing is the most recent realisation of computing as a utility. Recently, fields with substantial computational requirements, e.g., biology, are turning to clouds for cheap, on-demand provisioning of resources. Of interest to this paper is the execution of compute intensive applications on hybrid clouds. If application requirements exceed private cloud resource capacity, clients require scaling down their applications. The outcome of this research is Web technology realising a new form of cloud called HPC Hybrid Deakin (H2D) Cloud -- an experimental hybrid cloud capable of utilising both local and remote computational services for single large embarrassingly parallel applications.

The simple yet complex development of social and participatory media for classroom learning : building a hybrid learning environment through action research

This paper reports on part of a teacher/researcher’s PhD action research study. It explains the complexity of features that social media brings to the teaching and learning process while discussing the simplicity and power of its use. Through the action research cycle, learning programs were designed to take advantage of the unique communicative methods offered by social media and web 2.0 whilst maintaining the value of face-to-face learning. Students used social media spaces such as blogs, groups and discussion forums as well as developing their own profiles and avatars to communicate online by making friends, leaving comments and uploading content which included publishing, peer reviewing and self assessment. The author argues that, by designing learning that valued and combined the attributes of social media, Web 2.0 and face-to-face teaching she was able to produce a more student-centred approach; hence, developing a ‘Hybrid’ learning environment which supported many 21st Century skills.

Social media in the classroom : a simple yet complex hybrid environment for students

This article reports on part of the author’s PhD action research study. It examines the complexity of features that social media and Web 2.0 offer when combined with face-to-face teaching and learning. Action research was used to help redesign the learning programs of thirteen Middle Years classes over an eighteen month period. These learning programs took advantage of the unique communicative methods offered by social media and provided spaces such as blogs, groups and discussion forums. Students developed their own identity when working online, made online friends, left comments for peers and uploaded content which included publishing, peer reviewing and self assessment. The research highlighted the simplicity in the creation and exchange of user-generated content and interaction while identifying a complex depth behind such interaction. Designing learning programs using social media enabled the students to be active and valued participants in the learning process and a ‘hybrid’ learning environment

Hybrid cloud resource provisioning policy in the presence of resource failures

Resource provisiomng is an important and challenging problem in the large-scale distributed systems such as Cloud computing environments. Resource management issues such as Quality of Service (QoS) further exacerbate the resource provisioning problem. Furthermore, with the increasing functionality and complexity of Cloud computing, resource failures are inevitable. Therefore, the question we address in this paper is how to provision resources to applications in the presence of resource failures in a hybrid Cloud computing environment. To this end, we propose three Cloud resource provisioning policies where we utilize workflow applications to drive the system workload. The proposed strategies take into account the workload model and the failure correlations to redirect requests to appropriate Cloud providers. Using real failure traces and workload models, we evaluated the performance and monetary cost of the proposed policies. The results of our experiments show that we can decrease the deadline violation rate of users' requests to as low as 20% with a limited cost on Amazon public Cloud.

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task- scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

Adaptive intelligent energy management system of plug-in hybrid electric vehicle

Efficient energy management in hybrid vehicles is the key for reducing fuel consumption and emissions. To capitalize on the benefits of using PHEVs (Plug-in Hybrid Electric Vehicles), an intelligent energy management system is developed and evaluated in this paper. Models of vehicle engine, air conditioning, powertrain, and hybrid electric drive system are first developed. The effect of road parameters such as bend direction and road slope angle as well as environmental factors such as wind (direction and speed) and thermal conditions are also modeled. Due to the nonlinear and complex nature of the interactions between PHEV-Environment-Driver components, a soft computing based intelligent management system is developed using three fuzzy logic controllers. The crucial fuzzy engine controller within the intelligent energy management system is made adaptive by using a hybrid multi-layer adaptive neuro-fuzzy inference system with genetic algorithm optimization. For adaptive learning, a number of datasets were created for different road conditions and a hybrid learning algorithm based on the least squared error estimate using the gradient descent method was proposed. The proposed adaptive intelligent energy management system can learn while it is running and makes proper adjustments during its operation. It is shown that the proposed intelligent energy management system is improving the performance of other existing systems. © 2014 Elsevier Ltd.

A hybrid multi-population genetic algorithm applied to solve the multi-level capacitated lot sizing problem with backlogging

The present paper proposes a new hybrid multi-population genetic algorithm (HMPGA) as an approach to solve the multi-level capacitated lot sizing problem with backlogging. This method combines a multi-population based metaheuristic using fix-and-optimize heuristic and mathematical programming techniques. A total of four test sets from the MULTILSB (Multi-Item Lot-Sizing with Backlogging) library are solved and the results are compared with those reached by two other methods recently published. The results have shown that HMPGA had a better performance for most of the test sets solved, specially when longer computing time is given. © 2012 Elsevier Ltd.

Towards a hybrid approach for adapting web graphical user interfaces to heterogeneous devices using context

Ubiquitous Computing promises seamless access to a wide range of applications and Internet based services from anywhere, at anytime, and using any device. In this scenario, new challenges for the practice of software development arise: Applications and services must keep a coherent behavior, a proper appearance, and must adapt to a plenty of contextual usage requirements and hardware aspects. Especially, due to its interactive nature, the interface content of Web applications must adapt to a large diversity of devices and contexts. In order to overcome such obstacles, this work introduces an innovative methodology for content adaptation of Web 2.0 interfaces. The basis of our work is to combine static adaption - the implementation of static Web interfaces; and dynamic adaptation - the alteration, during execution time, of static interfaces so as for adapting to different contexts of use. In hybrid fashion, our methodology benefits from the advantages of both adaptation strategies - static and dynamic. In this line, we designed and implemented UbiCon, a framework over which we tested our concepts through a case study and through a development experiment. Our results show that the hybrid methodology over UbiCon leads to broader and more accessible interfaces, and to faster and less costly software development. We believe that the UbiCon hybrid methodology can foster more efficient and accurate interface engineering in the industry and in the academy.