Cybersecurity breaches are often attributed to human behaviour, where individuals fail to integrate ethical principles in their decision-making. This empirical study investigates the effectiveness of the Ripple Down Rules (RDR) method, a knowledge acquisition and representation method, in enhancing ethical awareness and reasoning in cybersecurity contexts. The proposed approach combines rule-based reasoning, case-based learning, reflection, and situated cognition to bridge the gap between ethical knowledge and action by systematically connecting scenario elements to ethical principles. Participants, recruited from a cohort of first-year psychology students, were exposed to training incorporating five ethical principles—Beneficence, Non-Maleficence, Justice, Autonomy, and Explicability—applied to realistic cybersecurity scenarios. The study employed a randomised controlled design with two treatment and one control groups, using pre- and post-study assessments to evaluate improvements in ethical principle identification and reasoning. Participants rated RDR as a clear and helpful tool for understanding ethical reasoning, with sensibility and helpfulness scores ranging from moderate to high. Results demonstrate that RDR training significantly improved participants' ability to identify ethical principles compared to learning without RDR, particularly for principles like autonomy and explicability. However, challenges persisted in distinguishing overlapping principles, such as beneficence and non-maleficence. Implications and guidance for use of RDR for ethics training are discussed.

The human factor in information systems is a large vulnerability when implementing cybersecurity, and many approaches, including technical and policy driven solutions, seek to mitigate this vulnerability. Decisions to apply technical or policy solutions must consider how an individual's values and moral stance influence their responses to these implementations. Our research aims to evaluate how individuals prioritise different ethical principles when making cybersecurity sensitive decisions and how much perceived choice they have when doing so. Further, we sought to use participants’ responses to cybersecurity scenarios to create profiles that describe their values and individual factors including personality. Participants (n = 193) in our study responded to five different ethically sensitive cybersecurity scenarios in random order, selecting their action in that scenario and rating and ranking of the ethical principles (i.e., Beneficence, Non-Maleficence, Justice, Autonomy, Explicability) behind that action. Using participants’ demographics, personality, values, and cyber hygiene practices, we created profiles using machine learning to predict participants’ choices and the principle of most importance to them across scenarios. Further, we found that, generalising, for our participants Autonomy was the most important ethical principle in our scenarios, followed by Justice. Our study also suggests that participants felt they had some agency in their decision making and they were able to weigh up different ethical principles.

There is a lack of training for those designing and implementing cybersecurity policies and technologies concerning the ethical ramifications of their choices on users. Using a principlist approach as our ethical framework, we propose a serious game to assist the player to learn about ethical decision making by providing training about five ethical principles (Autonomy, Justice, Beneficence, Non-maleficence, and Explicability) in the context of cybersecurity decision making. There are many factors that impact human decision making, with personality identified as the most influential factor. A novel feature of our work is that it identifies the relationship between specific personality traits (Openness, Extraversion, Emotional Stability, Conscientiousness, and Agreeableness) and the priority given to each of these five ethical principles. We use personality-principle combinations to design the dialogues of multiple embodied conversational agents, or non-player characters (NPCs), to provide alternative ethical perspectives to the player. To improve the player's learning and engagement, they take on the role of a new employee in a cybersecurity firm and they interact with the NPCs in video meetups to discuss ethical dilemmas currently being faced by the cybersecurity team. Our results from a study conducted online with 219 cybersecurity students show that after playing the game, students were more aware of the ethical principles and their application to cybersecurity decisions. Moreover, players were able to recognize the agents' personalities, which could be helpful in achieving future tailored training.

Interventions to improve health and well-being abound. Whether they are designed for prevention, maintenance or improvement, a key challenge is the motivation of the user to change their current behaviours, such as persisting or taking new actions. To encourage someone to change their behaviour requires persuading them to change their goals and/or their beliefs about the behaviour or their ability to perform it. Our embodied conversational agent (ECA) uses explanations based on the goals and beliefs of the user to promote a sense of personalisation and engagement with the treatment plan which could form a bond as the dyad develop shared goals and tasks together. To keep our message minimal and understand whether belief-based or goal-based explanations are more efficacious in changing behaviour intention, we collected data in the context of a scenario where the ECA seeks to change four behaviours recommended to help students manage their study stress. Our findings suggest that when the behaviour requires a change in desire, we need goal-based explanation, when adoption of the behaviour requires addressing a barrier we need belief-based explanation and warrant future investigation. Further, the stratified analysis suggested that more tailoring to the student’s context could provide more motivation to change.

Virtual reality (VR) technology can increase prosocial behaviour toward a target person or group by enhancing their empathic response for the subject, but such technology has not always improved learning outcomes. This interdisciplinary study compared the potential advantages of delivering the same learning material about daily life in an ancient Greek household via two modes of delivery: VR technology and classroom lecture. The VR group explored a Greek villa containing historical artefacts and virtual characters with whom they were able to interact through set dialogues. The dialogues illustrated social hierarchies, gender relations, the situation of slaves, cult practice, and religious beliefs. The classroom group received the same information in a classroom environment. Both randomly-assigned groups answered a multiple-choice quiz to evaluate the knowledge gained. They also responded to open-text questions designed to test the degree of empathy that was aroused. We found that classroom lecture delivery was significantly superior in terms of the acquisition of factual knowledge, consistent with cognitive learning theory. We identified this as learning with the mind. The immersive VR environment, however, imparted a level of empathic response to the lived experiences of people in ancient Greece; in that sense it allowed learning with the heart.

A popular technique for evaluating envisioned AI agents without committing significant engineering resources to implement such an agent is the Wizard of Oz (WoZ) methodology. Thus far, many of the WoZ studies concerned with the design of conversational agents have been standalone systems for individual participants to interact with. The aim of this work was to determine whether a WoZ conversational agent could be embedded within an existing team-based collaborative virtual environment. In doing so, we uncovered a number of potential challenges in designing such a system which may be valuable for those implementing such a system in the future.

Virtual advisors (VAs) are being utilised almost in every service nowadays from entertainment to healthcare. To increase the user's trust in these VAs and encourage the users to follow their advice, they should have the capability of explaining their decisions, particularly, when the decision is vital such as health advice. However, the role of an explainable VA in health behaviour change is understudied. There is evidence that people tend to change their intentions towards health behaviour when the persuasion message is linked to their mental state. Thus, this study explores this link by introducing an explainable VA that provides explanation according to the user's mental state (beliefs and goals) rather than the agent's mental state as commonly utilised in explainable agents. It further explores the influence of different explanation patterns that refer to beliefs, goals, or beliefs&goals on the user's behaviour change. An explainable VA was designed to advise undergraduate students how to manage their study-related stress by motivating them to change certain behaviours. With 91 participants, the VA was evaluated and the results revealed that user-specific explanation could significantly encourage behaviour change intentions and build good user-agent relationship. Small differences were found between the three types of explanation patterns.

To personalise dialogue to different users, relational agents need to learn about the users' preferences for relational cues used by the agent. In the context of a virtual advisor to reduce students' study stress, we designed a between subjects study with three groups (empathic, neutral and adaptive) who either received all cues, no cues or helpful cues only, respectively, and compared rapport and changes in study stress scores. To avoid the cold start problem, we sought to train the agent and adapt its dialogue to include or exclude 10 relational cues based on the user's responses to whether an example of each relational cue is found helpful prior to the session with the virtual advisor. The results of an experiment with 111 students show that the rapport scores for the empathic and adaptive groups were significantly higher than the neutral group; change in rapport scores was significantly higher in the adaptive group than in the empathic group. Furthermore, study stress scores significantly reduced for the adaptive and empathic groups, but not for the neutral group. We found some relationships between the number of times students found helpful what they received and other variables. We also found that the number of discrepancies and matches between what relational cues users received and what they found helpful were greatest in the adaptive group. This indicates the effectiveness of this approach for dealing with the cold start problem.

Intelligent virtual agents are being endowed with empathic behaviours to perform roles such as virtual therapists. Studies often evaluate the level of rapport established, but do not measure the therapeutic benefit and the relative advantage of empathic versus neutral behaviours. We have created two virtual (empathic/neutral) therapists. Our experiment with 63 participants consisted of one within-subjects (empathic/neutral) and one between-subjects (order) factors. Regardless of the virtual therapist used, improvements in baseline emotion were reported after the first interaction (time one) and further improvement after the second interaction (time two). Our study reveals that if the human initially expresses strong emotional feeling for a problem they are facing, rapport will be higher for the empathic therapist and the level of rapport established at the first meeting will persist regardless of whether the second encounter used empathic or neutral dialogue. Conversely, participants experiencing low emotional feeling reported greater rapport with the neutral therapist, and that level of rapport persisted in the second encounter with the alternative therapist. This study shows that an empathic agent will not necessarily build more rapport or deliver better emotional outcomes than a neutral agent. Further studies are needed to determine when tailoring and complex behaviours are justified.

Students require human intelligence and social interaction in the form of academic assistance at different times of their study period. Their desire to get and find academic assistance varies and is dependent on many factors such as attendance mode, personal situation, semester timetables, and assessment due dates. Providing students with access to this expertise when it is needed and to large numbers of students is problematic. Virtual Agents (VAs) seek to provide a technology-enabled social element to encourage and provide timely support to aid students' learning. We have implemented 4 unit-specific VIRtual Teaching Assistants (VIRTAs) across 2 universities to provide support to answer student's questions about various aspects of the unit. In this work-in-progress paper, we present the usage patterns of students to show how many questions were asked by students and at what point of time in the semester the questions were asked addressing the desire to find assistance when required from VIRTA.

In this paper, we report on the multi-year Intelligent Virtual Agents (IVA) community effort, involving more than 80 researchers worldwide, researching the IVA community interests and practises in evaluating human interaction with an artificial social agent (ASA). The effort is driven by previous IVA workshops and plenary IVA discussions related to the methodological crisis on the evaluation of ASAs. A previous literature review showed a continuous practise of creating new questionnaires instead of reusing validated questionnaires. We address this issue by examining questionnaire measurement constructs used in empirical studies between 2013 to 2018 published in the IVA conference. We identified 189 constructs used in 89 questionnaires that are reported across 81 studies. Although these constructs have different names, they often measure the same thing. In this paper, we, therefore, present a unifying set of 19 constructs that captures more than 80% of the 189 constructs initially identified. We established this set in two steps. First, 49 researchers classified the constructs in broad theoretically based categories. Next, 23 researchers grouped the constructs in each category on their similarity. The resulting 19 groups form a unifying set of constructs, which will be the basis for the future questionnaire instrument of human-ASA interaction.

Non-adherence to a treatment plan recommended by the therapist is a key cause of the increasing rate of chronic medical conditions globally. The therapist-patient therapeutic alliance is regarded as a successful intervention and a good predictor of treatment adherence. Similar to the human scenario, embodied conversational agents (ECAs) showed evidence of their ability to build an agent-patient therapeutic alliance, which motivates the effort to advance ECAs as a potential solution to improve treatment adherence and consequently the health outcome. Building therapeutic alliance implies the need for a positive environment where the ECA and the patient can share their knowledge and discuss their goals, preferences and tasks towards building a shared plan, which is commonly done using explanations. However, explainable agents commonly rely on their own knowledge and goals in providing explanations, rather than the beliefs, plans or goals of the user. It is not clear whether such explanations, in individual-specific contexts such as personal health assistance, are perceived by the user as relevant in decision-making towards their own behavior change. Therefore, in this research, we are developing a user-aware explainable ECA by embedding the cognitive agent architecture with a user model, explanation engine and modified planner to implement the concept of SharedPlans. The developed agent will be deployed and evaluated with real patients and the therapeutic alliance will be measured using standard measurements.

Explainable? virtual agents provide insight into the agent's decision-making process, which aims to improve the user's acceptance of the agent's actions or recommendations. However, explainable agents commonly rely on their own knowledge and goals in providing explanations, rather than the beliefs, plans or goals of the user. Little is known about the user perception of such tailored explanations and their impact on their behaviour change. In this paper, we explore the role of belief-based explanation by proposing a user-aware explainable agent by embedding the cognitive agent architecture with a user model and explanation engine to provide a tailored explanation. To make a clear conclusion on the role of explanation in behaviour change intentions, we investigated whether the level of behaviour change intentions is due to building agent-user rapport through the use of empathic language or due to trusting the agent's understanding through providing explanation. Hence, we designed two versions of a virtual advisor agent, empathic and neutral, to reduce study stress among university students and measured students' rapport levels and intentions to change their behaviour. Our results showed that the agent could build a trusted relationship with the user with the help of the explanation regardless of the level of rapport. The results, further, showed that nearly all the recommendations provided by the agent highly significantly increased the intention of the user to change their behavior.

Research into artificial social agents aims at constructing these agents and at establishing an empirically grounded understanding of them, their interaction with humans, and how they can ultimately deliver certain outcomes in areas such as health, entertainment, and education. Key for establishing such understanding is the community's ability to describe and replicate their observations on how users perceive and interact with their agents. In this paper, we address this ability by examining questionnaires and their constructs used in empirical studies reported in the intelligent virtual agent conference proceedings from 2013 to 2018. The literature survey shows the identification of 189 constructs used in 89 questionnaires that were reported across 81 papers. We found unexpectedly little repeated use of questionnaires as the vast majority of questionnaires (more than 76%) were only reported in a single paper. We expect that this finding will motivate joint effort by the IVA community towards creating a unified measurement instrument.

This study investigates the influence of a virtual human (VH) with recorded human voice vs VH with a machine-generated voice (text-to-speech) on building trust and working alliance. We measured the co-presence perception to understand the impact of VH's perception on building the human-VH relationship. The results revealed no differences between the two types of voices on co-presence perception, trust or working alliance.

Treatment non-adherence is a major worldwide problem that results in poor health outcomes among patients with various medical conditions. A good therapist-patient therapeutic alliance and face-to-face communication are regarded as key factors in improving adherence. In this research, we propose the extension of the existing agent architecture FAtiMA to design an embodied conversational agent that plays the role of the therapist by incorporating a shared mental model (SMM) and hybrid state-based and task-based dialogue engine.

We seek to provide tailored support in an educational virtual world (EVW) for high school students to gain scientific knowledge and research inquiry skills. This paper focuses on using virtual humans to capture students' emotional states and provide motivational support. We have conducted two classroom studies involving a total of 49 students in Years 8-9, 19 at a girls-only comprehensive school and another 30 at a co-educational academically selective school. While using the EVW, students encountered five virtual humans who elicited students' emotions by asking "How are you?" We sought to measure students' willingness to disclose their emotions to the virtual human.

Understanding how and in what circumstances users respond to different verbal expressions of empathy will be important for designing Intelligent Virtual Agents able to influence the emotions and intrinsic motivations of users. We report a study to teach healthy study habits and tips involving 239 undergraduate students, 161 of which interacted with a character designed to express empathy through dialogue. We elicited participants' personality and psychological state (depression, anxiety and stress levels) and attitudes to study. In this paper we present a detailed analysis of participants' responses to specific empathic dialogue snippets designed to include one or more empathic cues to determine whether they found it helpful, stupid or empathic. We provide an example of how we have used data mining on our dataset to suggest which cues are most appropriate to which user types to enhance user models and improve agent decision-making regarding the expression of empathy.

Dance has universally been used as a form of human expression for thousands of years. This common human behaviour and communication method has not been explored much in the context of computer-based technology, even within the field of virtual human research. This paper presents an experimental study investigating the impact of watching dancing virtual characters on human emotions. The study analysed the responses of 55 participants, composed of a mix of dancers and non-dancers, who watched a dancing virtual character perform 3 different dances that represented anger, sadness and happiness in different display orders. The participants’ reported changes in their emotions and their feelings of anger, sadness and happiness were significantly dependent on which dancing character’s emotion they watched and the emotional change did not rely on correct recognition of the depicted emotion. For experimental control, our characters were faceless and danced without music. Our results suggest that just by watching a dancing virtual character some of the benefits associated with dancing could be accessed in circumstances where it is not desirable or feasible to dance, justifying further research to develop a personalised character with a face and music that adapts according to the humans’ emotions and preferences.

Nonlife threatening chronic health conditions can significantly reduce the quality of life for the patient and their family. Given pressure on specialist services and lengthy wait times, we propose a novel approach that involves a website and virtual specialist for patients while they are awaiting their specialist appointment. To capture patient history and provide tailored treatment advice, an interactive website was developed. To increase adherence, the website was enhanced with an empathic embodied conversational agent to allow discussion of the suggested treatment. A six-month trial with 74 children with urinary incontinence showed an overall improvement in 74% of patients, with 38% those who used the program reporting a resolution of their wetting without needing a specialist appointment. Capturing the expertise of medical specialists to provide online tailored treatment advice and use of humanlike face-to-face conversations to educate and build rapport with the patient appeared to increase treatment adherence compared to an earlier text-based version without the empathic agent.

Crop production problems are common in Sri Lanka which severely effect rural farmers, agriculture sector and the country's economy as a whole. A deeper analysis revealed that the root cause was farmers and other stakeholders in the domain not receiving right information at the right time in the right format. Inspired by the rapid growth of mobile phone usage among farmers a mobile-based solution is sought to overcome this information gap. Farmers needed published information (quasi static) about crops, pests, diseases, land preparation, growing and harvesting methods and real-time situational information (dynamic) such as current crop production and market prices. This situational information is also needed by agriculture department, agro-chemical companies, buyers and various government agencies to ensure food security through effective supply chain planning whilst minimising waste. We developed a notion of context specific actionable information which enables user to act with least amount of further processing. User centered agriculture ontology was developed to convert published quasi static information to actionable information. We adopted empowerment theory to create empowerment-oriented farming processes to motivate farmers to act on this information and aggregated the transaction data to generate situational information. This created a holistic information flow model for agriculture domain similar to energy flow in biological ecosystems. Consequently, the initial Mobile-based Information System evolved into a Digital Knowledge Ecosystem that can predict current production situation in near real enabling government agencies to dynamically adjust the incentives offered to farmers for growing different types of crops to achieve sustainable agriculture production through crop diversification.

User-Centred Design (UCD) methodologies have been increasingly used during the past decade to develop software applications and products that are tailored to the needs of individuals and allow for human computer interactions on emotional and psychological levels. UCD designers and developers need to have special abilities and training to design products that meet the demands of users. This paper presents novel techniques to identify talented aspiring designers in UCD methodologies. Twenty-three undergraduate students, studying at a research-intensive metropolitan Australian university, participated in this study. Participants completed a spatial ability test, answered personality trait questionnaires and performed a design activity. Our results indicate that students who score high in the imagination personality factor and spatial ability tests are talented aspiring UCD designers. The implication of our study is that talented students who can design using UCD methodologies can be identified early in their studies and they can benefit by receiving advanced training. Likewise the less talented students can be given extra tutoring as abilities are not immutable and, interest and persistence is important in achieving expertise.

Conceptual design is an important skill in Software Engineering. Teaching conceptual design that can deliver a useful product is challenging, particularly when access to real users is limited. This study explores the effects of the use of Holistic Personas (i.e. a persona enriched with personality traits) on students' performance in creating conceptual designs. Our results indicate that the students were able to identify the personality traits of personas and their ratings of the personalities match closely with the intended personalities. A majority of the participants stated that their designs were tailored to meet the needs of the given personas' personality traits. Results suggest that the Holistic Personas can help students to take into account personality traits in the conceptual design process. Further studies are warranted to assess the value of incorporating Holistic Personas in conceptual design training for imparting skills of producing in-depth design by taking personalities into account.

This paper addresses the question of how to develop forecasting models resulting from business processes that can be embodied in an intelligent decision support system. Moreover the design is suitable for evolving logistics and economic situations in which ports plan or foresee to have an improved economic role. The key objective of this work is to offer a model-based approach to Time Series Data Mining (TSDM) based on the assumptions that the time series may be produced by an underlying model, and that its flexibility is suitable to perform multivariate time-series analysis encompassing the notion of model selection and statistical learning known as the core of forecasting systems. Results indicate that for the period 2001 to 2005, the commodity throughput of coffee (tons) handled in the port of Buenaventura gains importance in the prediction of the Colombian national exports of coffee, thus indicating that the port operation was able to affect the economy in this regard. The previous period was strongly affected by outliers, creating a random walk process difficult to fit but feasible to produce due to unstable conditions evidenced in the economy.

In a study conducted in 2004 we found that our students recognized the need to collaborate but that, due to inadequate communication skills, limited time and lack of experience and training in group work, the extent to which they collaborated was far less than they believed to be ideal. Since the advent of Web 2.0 we were interested to see if these increasingly tech-savvy digital natives were choosing to both collaborate more and to use technology to do so. In particular, we consider how 29 groups in a second year software analysis and design unit utilized a collaboration and project management tool known as TRAC. We suggest that different patterns of usage were observed due to different perceived affordances of the technology driven by students' interests and needs. We characterize the affordances and conclude that while we are seeing an increase in collaboration mediated by technology, more intended intervention is needed to assist students to discover a wider range of affordances.

The value and the pitfalls of project and group work are well recognized. The principles and elements which apply to projects in general, apply to project-based courses. Thoughtful and detailed planning, understanding of the stakeholders and their needs, a good design, appropriate testing, monitoring and quality control and continual management can maximize the benefits and minimize the negatives. In this article we draw together the literature to consider key design choices of project-based courses considering: type, length, size, management, participants, and content with a particular focus on the composition of groups and the issues surrounding assessment.

Social software such as Wikis and WebLogs have enabled global knowledge sharing communities to emerge. As with most of the current Web content, the knowledge captured is for human consumption only and too unstructured to allow automated reasoning. On the other hand, while sharing and reuse of knowledge are often the goal behind the development of a knowledge base or ontology, there is still currently little support even at the level of a version control system that notifies users when a module being checked-in has been edited by someone else. A collaborative learning approach is introduced which extends a combined rule and case based knowledge acquisition technique known as multiple classification ripple down rules to allow multiple users to view, define and refine a knowledge base over time and space.

We are interested in using game technology to provide an engaging and immersive environment for experiential learning of workplace situations. Narrative intelligence will be used to provide the adventure. For authoring we provide an adaptive interface that allows the direct capture of the workplace situations and the knowledge driving the interaction. We include an initial study comparing the learning outcomes for an animated demonstration with video footage of a similar scenario.

In this paper, we postulate that the problem solving process in many domains involves identifying the class of problem on hand, identifying an appropriate solution, and recognising opportunities for its reuse. We suggest a solution that builds up knowledge of a given domain by recording observations, diagnoses and actions in a “3Cs form” of Cases, Classifications, and Conclusions. Our solution allows knowledge workers in any domain where heuristics are relied on to form classifications, and then apply generalised conclusions on the basis of the given classification, to collaboratively refine and expand a topic by consistently asking users to confirm, add to, or refine the presented knowledge in the context of the current case being classified. Our solution is presented in the context of the corporate call centre and is a significant extension of the Multiple Classification Ripple Down Rules algorithm. We present a 3Cs Logic Wiki that takes the best features of current collaborative knowledge exchange mechanisms, and captures a logic structure on top of that which provides for rapid indexing of acquired knowledge.

In order to gauge the pedagogical implications of conducting Computer Science practical sessions remotely, the Division of ICS at Macquarie University conducted a formal experiment using a virtual classroom environment called Macromedia Breeze Live. Research results indicated that students who completed their practical in the virtual classroom: i) felt they performed significantly more collaboration, ii) expressed a preference for this mode of practical session over regular laboratory sessions, iii) felt that they learnt significantly more from their classmates and iv) felt that they learnt significantly more from the practical supervisor than students in the standard classroom. Reasons for these results are proposed in the pedagogical context of offering Computer Science practical sessions online.

With the exponential growth of the World Wide Web (WWW), it has become the most popular place to gather information. However the size of the WWW makes it difficult for people to locate relevant information. About 85% of all Web users use search engines of some kind for this purpose. However, existing search engines often do not return relevant information. The main focus of this paper is to improve search performance by using keywords and web pages which have been previously used or visited by other users. The Formal Concept Analysis (FCA) method has been adapted to maintain a concept map of keywords. This paper shows how both precision and recall has improved the specific-domain area, in which users can share the same knowledge.

The Unified Modelling Language (UML) is a visual approach to the analysis and design of object-oriented computer systems. However, when it comes to specifying requirements through use case descriptions we see two weaknesses in the current approach. The descriptions are textual (the success and acceptance of UML is largely due to its visual nature) and the group process for acquiring descriptions can result in a small number of stakeholders having their views represented. To address these issues the RECOCASE methodology captures use case descriptions from multiple viewpoints and automatically generates a visualisation of the individual and shared viewpoints to assist identification and resolution of conflicts. We briefly describe the approach and results from various initial evaluation studies.

We explore the use of an agent factory for the composition of Web services. Previously we proposed a structuring approach for automated reconfiguration of agents by an agent factory. The question is whether the same approach can be applied to Web service composition, i.e. whether DAML-S descriptions of Web services offer enough structure for automated configuration by the agent factory. An example trace of the agent factory for configuration of DAML-S Web services illustrates this approach.

Capturing a consistent, complete and valid set of requirements is a critical prerequisite for designing systems that are useful and usable. One popular technique for capturing the main chunks of functionality from the user's point of view is the specification of use cases by the development team. While there may be agreement at this high level, there are often differences in what each stakeholder perceives as the steps of the use case description. Often the description reflects the views of one or a few dominant individuals resulting in incomplete use case descriptions that do not represent the requirements of the whole group. In our approach, known as RECOCASE as we use a CASE tool to assist requirements reconciliation, the group identify and then asynchronously capture multiple requirements viewpoints. The use case descriptions are entered in natural language and automatically processed by our system to produce a concept lattice. The concept lattice is used to reveal common ground and differences between the stakeholders. Our group process enables conflicts to be identified and resolved, where possible, using our negotiation strategies and resolution operators.

Providing timely and high-quality feedback has been identified as one of the critical factors in learning. In courses with large class sizes the amount of time required for critiquing makes it prohibitive to give feedback to students on more than two or three assignments per semester. As a result, students have limited opportunities to learn from their mistakes, and it is difficult for lecturers to monitor student progress. The alternative to manual critiquing is to automate the process. The authors expect automated program critiquing to deliver two major benefits to students: improved feedback and more useful access to lecturers. This paper introduces their project and the expected benefits.

Experience Based Learning (EBL) is a process in which real world problems are used to help and motivate students to identify, apply, collaborate and communicate their knowledge effectively. However, implementation of EBL requires substantial effort on the part of teacher involving new teaching strategies and significant course restructuring. Also, coming up with suitable problems and solutions in sufficient detail that still allows for creativity and individuality is time consuming and difficult. This project focuses on facilitating the development of a problem-based repository of teaching resources. We are developing a web-based system to capture valuable teaching resources that may otherwise be lost and provide a searchable repository of problems and solutions that can be reused in a number of different units, possibly being expanded on each reuse, to implement EBL.

Ripple down rules (RDR) have a longstanding (and successful) history in the field of biomedical engineering. RDR are a knowledge acquisition and representation technique that allow knowledge to be rapidly acquired and maintained by the domain expert. A key feature of RDR, and the reason why maintenance is easily managed, is that rules are never modified or deleted, but locally patched. That is, new rules are exceptions to previous rules and the new rule is validated within the context of previously seen cases. One drawback of local patching is that knowledge can be repeated in different locations of the knowledge base. This paper describes some work done on removing repeated knowledge. The experiments reported were performed on a pathology knowledge base but the algorithm is applicable to any multiple classification RDR knowledge based system. The results support the findings of others that exception structures are compact representations with few opportunities to reduce further. This also suggests that experts tend to provide overly general rules in the first instance which they modify by adding specialisations in the form of exception rules as new cases are seen.