In this paper, an argumentative knowledge-based model construction (KBMC) technique for Bayesian networks is presented. This approach allows an agent to collect and instantiate the most accepted subset of an imperfect knowledge base to dynamically construct a Bayesian network. Arguments are constructed to represent active paths through an agent's knowledge base - paths consisting of information that is computationally relevant in the evaluation of a query Pr(Q|E). Argumentation over paths is used to select the valid or most accepted information according to the preferences of the agent. This information is consequently formed into candidate network structures by accrual. This work is presented as an extension of the KBMC approach of Haddawy [5]. The potential of the approach to be used in multi-agent network construction is discussed.

This paper presents an argumentation-based interpreter for Golog programs. Traditional Golog interpreters are not designed to find the most preferred executions of a program from the perspective of an agent. Existing techniques developed to discover these executions are limited in terms of how the preferences of an agent can be expressed, and the variety of preference types that can be used to guide search for a solution. The presented work combines the use of argumentation to compare executions relative to a set of general comparison principles, and the theory behind best first search to reduce the cost of the search process. To the best of our knowledge this is the first work to integrate argumentation and the interpretation of Golog programs, and to use argumentation as a tool for best first search.

GOLOG is an agent programming language designed to represent complex actions and procedures in the situation calculus. In this paper we apply relaxation-based heuristics – often used in classical planning – to find (near) optimal executions of a GOLOG program. We present and utilise a theory of relaxed regression for the approximate interpretation of a GOLOG program. This relaxed interpreter is used to heuristically evaluate the available choices in the search for a program execution. We compare the performance of our heuristic interpreter (in terms of the quality of executions found) with a traditional depth-first search interpreter and one guided by a greedy heuristic without a look-ahead on three domains: spacecraft control, mine planning, and task scheduling.

This paper presents some preliminary work toward the development of an argumentative interpreter for Golog programs. Traditional Golog interpreters are not designed to find the most preferred executions of a program from the perspective of an agent. Existing techniques that optimise the interpretation of Golog programs suffer from either a lack of exibility in how preference may be expressed or require the consideration of all program executions. The presented work combines the use of argumentation to compare executions relative to a set of general comparison criteria, and the theory behind informed search techniques to reduce the cost of the search process. The use of this argumentative informed search in the interpretation of ConGolog programs (with true concurrency), and its potential to find a program execution that is the most preferred by a collection of agents, is also discussed.

and control of information security risks have emerged as a primary mean by which organizations secure information infrastructure. Key assets are identified and protected as a part of risk management strategy. In this process, commonly Denial of Service (or DoS) attacks are overlooked. DoS service is traditionally not considered as information security risk, hence the treatment of that remains low priority. But in the recent past, several such attacks had made high profile business’s web servers unavailable or un-accessible for considerable period of time, which consequently caused monitory and reputational losses. Hence now there is a substantial need to consider DoS attacks as a potential risk for information security and its assessment and treatment should be included in organization’s risk management process. This paper examines the major forms of DoS attacks that are lodged on critical network infrastructure of an organization, targeting the availability and access of its critical business and IT Services and further how the risk of such attacks can be reduced or mitigated through risk management process.

The user base of Social Media Networks (SMN) has grown dramatically over the last 10 years, with the Facebook platform alone commanding 18% of the world’s population as active users. Thus SMN provide a mechanism to disseminate information both rapidly and globally. Despite this fact, little research has been conducted into the implications of SMN on information security risk. Here we conduct a literature review in order to provide information security professionals with insight into the threats, threat agents, vulnerabilities and potential risks faced by individuals and organisations from SMN. Findings suggest that confidentiality and integrity of information can be threatened by multiple actors and mechanisms, putting information and reputation at risk. Information security professionals face a mammoth task to manage such risks and a standard approach to risk management seems unlikely to be effective.

Knowledge sharing is a vital business strategy that creates value for an organisation. It also leads to accidental or deliberate loss of information and knowledge. With an ideal culture, the knowledge sharing barrier can be broken without leaking information. We gathered data from the literature on the benefits of knowledge sharing in organisations and the related risks, addressing the role of a positive organisational culture. We interviewed information security specialists in small and large organisations in Melbourne and overseas. The study confirms the findings from literature that organisations value knowledge sharing to gain a competitive advantage. They also revealed that the preventive measures of knowledge leakage usually involved fostering a sharing culture with strategy, policies and controls in place with regular training and awareness. Based on these observations, we propose the need for future research on organisations that have fostered a culture of sharing knowledge without compromising its security.

Relations between learning outcomes and the learning objects which are assembled to facilitate their achievement are the subject of increasingly prevalent investigation, particularly with approaches which advocate the aggregation of learning objects as complex constituencies for achieving learning outcomes. From the perspective of situated learning, we show how the CASE framework imbues learning objects with a closed set of properties which can be classified and aggregated into learning object assemblies in a principled fashion. We argue that the computational and pedagogical tractability of this model provides a new insight into learning object evaluation, and hence learning outcomes.

Discourse in and about computational linguistics depends on a shared body of knowledge. However, little content is shared across the introductory courses in this field. Instead, they typically cover a diverse assortment of topics tailored to the capabilities of the students and the interests of the instructor. If the core body of knowledge could be agreed and incorporated into introductory courses several benefits would ensue, such as the proliferation of instructional materials, software support, and extension modules building on a common foundation. This paper argues that it is worthwhile to articulate a core body of knowledge, and proposes a starting point based on the ACM Computer Science Curriculum. A variety of issues specific to the multidisciplinary nature of computational linguistics are explored.