The BOSS system for on-line submission and assessment of computing assignments

Practical computing courses which involve significant amounts of programming continue to suffer from increasing student numbers. This makes their delivery and management more difficult to achieve effectively with the available resources. One solution to this problem is to develop methods for automating the submission and testing of student programs to support the marking effort and to enable the division of marking tasks among several individuals while ensuring consistency and rigour throughout. We have developed such methods in our system, called BOSS, and have successfully deployed different versions of it on several courses over a number of years. Here, we describe the original system and its recent enhancements, and discuss the benefits it has provided us with, both in terms of administration and in improving the learning process

Motivated Inductive Discovery

Research in machine discovery to date has tended to concentrate on the replication of particular episodes in the history of science, and more recently on the extraction of regularities from large databases. In this respect, current models of induction and discovery concentrate solely on the acquisition of knowledge, and lack the flexibility of reasoning that is necessary in a real-world changing environment. Against this backdrop, this dissertation addresses inductive reasoning, specifically based around the scientific discovery paradigm. A framework for inductive reasoning is presented which includes the six stages of prediction, experimentation, observation, evaluation, revision and selection. Within this framework, different kinds of inductive reasoning can be reduced to the same basic component processes. The difference between the various kinds of reasoning arises not through the use of different mechanisms, but through the influence of motivations which bias the application of these mechanisms accordingly. Also within this framework, a model and its implementation as a computer program, the MID system, have been developed, concentrating primarily on the internal stages of the framework, prediction, evaluation, revision and selection. The role of motivations in allowing reasoning for both knowledge and action is investigated and implemented in the program. By choosing different internal models of motivation for reasoning systems, different behaviours can be achieved from the same basic architecture. The MID system reasons in simple physical domains, both for knowledge and for action. It demonstrates how a basic mechanism can be used to provide an effective means for reasoning in a variety of contexts, and also how a simple motivational representation can be used as an effective control strategy

An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling

In many dynamic open systems, autonomous agents must interact with one another to achieve their goals. Such agents may be self-interested and, when trusted to perform an action, may betray that trust by not performing the action as required. Due to the scale and dynamism of these systems, agents will often need to interact with other agents with which they have little or no past experience. Each agent must therefore be capable of assessing and identifying reliable interaction partners, even if it has no personal experience with them. To this end, we present HABIT, a Hierarchical And Bayesian Inferred Trust model for assessing how much an agent should trust its peers based on direct and third party information. This model is robust in environments in which third party information is malicious, noisy, or otherwise inaccurate. Although existing approaches claim to achieve this, most rely on heuristics with little theoretical foundation. In contrast, HABIT is based exclusively on principled statistical techniques: it can cope with multiple discrete or continuous aspects of trustee behaviour; it does not restrict agents to using a single shared representation of behaviour; it can improve assessment by using any observed correlation between the behaviour of similar trustees or information sources; and it provides a pragmatic solution to the whitewasher problem (in which unreliable agents assume a new identity to avoid bad reputation). In this paper, we describe the theoretical aspects of HABIT, and present experimental results that demonstrate its ability to predict agent behaviour in both a simulated environment, and one based on data from a real-world webserver domain. In particular, these experiments show that HABIT can predict trustee performance based on multiple representations of behaviour, and is up to twice as accurate as BLADE, an existing state-of-the-art trust model that is both statistically principled and has been previously shown to outperform a number of other probabilistic trust models

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

A Hierarchical Bayesian Trust Model based on Reputation and Group Behaviour

In many systems, agents must rely on their peers to achieve their goals. However, when trusted to perform an action, an agent may betray that trust by not behaving as required. Agents must therefore estimate the behaviour of their peers, so that they may identify reliable interaction partners. To this end, we present a Bayesian trust model (HABIT) for assessing trust based on direct experience and (potentially unreliable) reputation. Although existing approaches claim to achieve this, most rely on heuristics with little theoretical foundation. In contrast, HABIT is based on principled statistical techniques; can be used with any representation of behaviour; and can assess trust based on observed similarities between groups of agents. In this paper, we describe the theoretical aspects of the model and present experimental results in which HABIT was shown to be up to twice as accurate at predicting trustee performance as an existing state-of-the-art trust model

Can models of agents be transferred between different areas?

One of the main reasons for the sustained activity and interest in the field of agent-based systems, apart from the obvious recognition of its value as a natural and intuitive way of understanding the world, is its reach into very many different and distinct fields of investigation. Indeed, the notions of agents and multi-agent systems are relevant to fields ranging from economics to robotics, in contributing to the foundations of the field, being influenced by ongoing research, and in providing many domains of application. While these various disciplines constitute a rich and diverse environment for agent research, the way in which they may have been linked by it is a much less considered issue. The purpose of this panel was to examine just this concern, in the relationships between different areas that have resulted from agent research. Informed by the experience of the participants in the areas of robotics, social simulation, economics, computer science and artificial intelligence, the discussion was lively and sometimes heated

Weaving a fabric of socially aware agents

The expansion of web-enabled social interaction has shed light on social aspects of intelligence that have not been typically studied within the AI paradigm so far. In this context, our aim is to understand what constitutes intelligent social behaviour and to build computational systems that support it. We argue that social intelligence involves socially aware, autonomous individuals that agree on how to accomplish a common endeavour, and then enact such agreements. In particular, we provide a framework with the essential elements for such agreements to be achieved and executed by individuals that meet in an open environment. Such framework sets the foundations to build a computational infrastructure that enables socially aware autonomy.This work has been supported by the projects EVE(TIN2009-14702-C02-01) and AT (CSD2007-0022)Peer Reviewe