Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

Patterns in complex systems modeling

The design, development, and use of complex systems models raises a unique class of challenges and potential pitfalls, many of which are commonly recurring problems. Over time, researchers gain experience in this form of modeling, choosing algorithms, techniques, and frameworks that improve the quality, confidence level, and speed of development of their models. This increasing collective experience of complex systems modellers is a resource that should be captured. Fields such as software engineering and architecture have benefited from the development of generic solutions to recurring problems, called patterns. Using pattern development techniques from these fields, insights from communities such as learning and information processing, data mining, bioinformatics, and agent-based modeling can be identified and captured. Collections of such 'pattern languages' would allow knowledge gained through experience to be readily accessible to less-experienced practitioners and to other domains. This paper proposes a methodology for capturing the wisdom of computational modelers by introducing example visualization patterns, and a pattern classification system for analyzing the relationship between micro and macro behaviour in complex systems models. We anticipate that a new field of complex systems patterns will provide an invaluable resource for both practicing and future generations of modelers.

Maintaining the change: Studying and supporting teaching for new learning

Teacher educators who advocate new learning approaches hope that their graduates will address the needs of digitally and globally sophisticated students. A critical, enquiry-based framework for teaching attempts to unravel many traditional assumptions about learning, assumptions which continue to shape preservice teachers’ practices even through early career years. Evidence in relation to effective take up of New Learning education approaches by graduates is sparse. This paper will explore how three teacher educators attempt to wrestle with ways New Learning frameworks can transform outmoded yet embedded views in beginning teachers. They ask: Can changed approaches be consolidated and mobilised against some of the adverse conditions that predominate in schools? And if this is possible, what support might be required for beginning teachers who are struggling to implement a change process

Verification and validation of autonomous systems

NASA is working on complex future missions that require cooperation between multiple satellites or rovers. To implement these systems, developers are proposing and using intelligent and autonomous systems. These autonomous missions are new to NASA, and the software development community is just learning to develop such systems. With these new systems, new verification and validation techniques must be used. Current techniques have been developed based on large monolithic systems. These techniques have worked well and reliably, but do not translate to the new autonomous systems that are highly parallel and nondeterministic.

Learning to play Pac-Man: An evolutionary, rule-based approach

Pac-Man is a well-known, real-time computer game that provides an interesting platform for research. We describe an initial approach to developing an artificial agent that replaces the human to play a simplified version of Pac-Man. The agent is specified as a simple finite state machine and ruleset. with parameters that control the probability of movement by the agent given the constraints of the maze at some instant of time. In contrast to previous approaches, the agent represents a dynamic strategy for playing Pac-Man, rather than a pre-programmed maze-solving method. The agent adaptively "learns" through the application of population-based incremental learning (PBIL) to adjust the agents' parameters. Experimental results are presented that give insight into some of the complexities of the game, as well as highlighting the limitations and difficulties of the representation of the agent.

Investigating Concept Mapping and Stimulated Recall to Reveal Academic Teacher Beliefs and how they are enacted through learning designs for the Web

Increasingly, academic teachers are designing their own web sites to add value to or replace other forms of university teaching. These web sites are tangible and dynamic constructions that represent the teachers thinking and decisions derived from an implicit belief system about teaching and learning. The emphasis of this study is to explore the potential of the research techniques of concept-mapping and stimulated recall to locate the implicit pedagogies of academic teachers and investigate how they are enacted through the learning designs of their web sites. The rationale behind such an investigation is that once these implicit belief systems are made visible, then conversations can commence about how these beliefs are transformed into practice, providing a potent departure point for academic development.