Due to widespread use and limited radio spectrum of 802.11 based networks, interference between collocated networks is an important problem. In this paper we expand our prior work on the multiagent system for channel assignment based topology control on multi-radio wireless mesh networks. The aim of the system is to dynamically reduce the overall interference and increase the aggregate capacity of the network. To realise this goal agents require information about radio spectrum utilisation, thus we propose a distributed measurement protocol. To obtain good spectrum utilisation the measurements focus on empirical issues such as contention interference and the use of partially overlapping channels. The simulation results obtained for realistic scenarios of MR-WMN node densities and topologies show performance comparable with the current state of the art in addition to stability, flexibility and extensibility, features achieved by the multiagent system implementation.

This article addresses the problem of finding suitable agents to collaborate with for a given interaction in distributed open systems, such as multiagent and P2P systems. The agent in question is given the chance to describe its confidence in its own capabilities. However, since agents may be malicious, misinformed, suffer from miscommunication, and so on, one also needs to calculate how much trusted is that agent. This article proposes a novel trust model that calculates the expectation about an agent's future performance in a given context by assessing both the agent's willingness and capability through the semantic comparison of the current context in question with the agent's performance in past similar experiences. The proposed mechanism for assessing trust may be applied to any real world application where past commitments are recorded and observations are made that assess these commitments, and the model can then calculate one's trust in another with respect to a future commitment by assessing the other's past performance.

The continuing rise in telecommunication and cloud services usage is matched by an increased complexity in maintaining adequate performance management. To combat this complexity, researchers and telecommunication companies are exploring a variety of management strategies to leverage their individual infrastructures to provide better performance and increased utilisation. We extend these strategies by addressing the complexities that arise through the interaction of multiple telecommunication and cloud providers when providing a modern complex service. Our overall aim is for the management to accept responsibility for the complex service in an open marketplace. Responsibility is, firstly, defined by aiming to cover the totality of modern complex services, managing both the connectivity and virtual infrastructure. Secondly, responsibility is defined as managing risk and resilience in the provisioning and operation of the complex service. With these aims, we are working towards a bundled service provider agent architecture, which can negotiate on the open service market. This approach aims to also optimise the utilisation of the providers infrastructure while reducing the risk of failure to users through total service management. We present the specification, design and simulation of the Complete Autonomous Responsible Management Agents (CARMA) in a marketplace environment.

Contemporary Web-based electronic markets reflect the dominating content-based systems approach of Web 2.0. Though useful, these electronic markets are far from being believable trading places. Marketplace is where things and traders have presence, constituting a rich interaction space. The believability of the place depends on the believability of the presence and interactions in it, including the players' behaviour and the narrative scenarios of the marketplace. This paper discusses what constitutes the believability of electronic marketplaces and presents the technologies that support it. Believability of electronic marketplaces can be described through three metaphors: ``marketplaces where people are'', ``marketplaces that are alive and engaging'', and ``market places where information is valuable and useful''. The paper presents the core technologies that enable the perceivable believability of electronic marketplaces. It describes a demonstrable prototype of a Web-based electronic marketplace that integrates these technologies. This is part of a larger project that aims to make informed automated trading an enjoyable reality of Web 3.0.

First, a hierarchical modelling method, VQSVM, is introduced, and some remarks are discussed. Secondly the proposed VQSVM is applied to a nonstandard learning environment, imbalanced data sets. In cases of extremely imbalanced dataset with high dimensions, standard machine learning techniques tend to be overwhelmed by the large classes. The hierarchical VQSVM contains a set of local models i.e. codevectors produced by the vector quantization and a global model, i.e. support vector machine, to rebalance datasets without significant information loss. Some issues, e.g. distortion and support vectors, have been discussed to address the trade-off between the information loss and undersampling rate. Experiments compare VQSVM with random resampling techniques on some imbalanced datasets with varied imbalance ratios, and results show that the performance of VQSVM is superior or equivalent to random resampling techniques, especially in case of extremely imbalanced large datasets.

The paper discusses an approach of using traditional time series analysis, as domain knowledge, to help the data-preparation of support vector machine for classifying documents. Classifying unexpected news impacts to the stock prices is selected as a case study. As a result, we present a novel approach for providing approximate answers to classifying news events into simple three categories. The process of constructing training datasets is emphasized, and some time series analysis techniques are utilized to pre-process the dataset. A rule-base associated with the net-of-market return and piecewise linear fitting constructs the training data set. A classifier mainly built by support vector machine uses the training data set to extract the interrelationship between unexpected news events and the stock price movements.

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

Fully automated trading, such as e-procurement, using the Internet is virtually unheard of today. Three core technologies are needed to fully automate the trading process: data mining, intelligent trading agents and virtual institutions in which informed trading agents can trade securely both with each other and with human agents in a natural way. This paper describes a demonstrable prototype e-trading system that integrates these three technologies and is available on the World Wide Web. This is part of a larger project that aims to make informed automated trading a reality.

Strategic processes are high-level business processes; they are distinct from production workflows. Strategic processes are opportunistic in nature whereas production workflows are routine. Strategic business processes are decomposed into goal-driven sub-processes and emergent sub-processes. An intelligent multiagent system manages strategic business processes. Each player is supported by an agent. The system manages goal-driven sub-processes and manages the commitments that players make to each other during emergent sub-processes. Commitments are derived through a process of inter-agent negotiation that considers each individual's constraints and performance statistics. The conceptual agent architecture is a three-layer belief-desire-intention (BDI), hybrid architecture. The system adapts its behaviour on the basis of performance estimates that are provided by the individual agents in contact net style bids for work. The system has been trialed on business process management in a university administrative context.

Maintaining the integrity of the knowledge represented within database rules is a complex business. In order to analyse the maintenance of integrity, a uniform framework for the representation of both data and database rules is introduced. A conceptual model is constructed using this uniform framework. The maintenance of the integrity of this conceptual model is analysed. In particular a generalised form of normalisation enables both the data and the database rules to be engineered so that they are maintainable in a technical sense. Database integrity is also protected by a comprehensive range of constraints including knowledge constraints.

Article “Normalising knowledge objects” Share on Author: John Debenham Key Centre for Advanced Computing Sciences, University of Technology, Sydney, PO Box 123, NSW 2007, Australia and CSIRO Division of Information Technology, Locked Bag 17, North Ryde, NSW 2113, Australia Key Centre for Advanced Computing Sciences, University of Technology, Sydney, PO Box 123, NSW 2007, Australia and CSIRO Division of Information Technology, Locked Bag 17, North Ryde, NSW 2113, AustraliaView Profile Authors Info & Claims CIKM '93: Proceedings of the second international conference on Information and knowledge managementDecember 1993 Pages 335–343https://doi.org/10.1145/170088.170164Online:01 December 1993Publication History 4citation334DownloadsMetricsTotal Citations4Total Downloads334Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access