Applying self-organised learning to develop critical thinkers for learning organisation: a conversational action research report.

The information explosion characteristic of a knowledge-based economy is fuelled by rapid technological changes. As technology continues to permeate our lives, there will be fresh demands upon the conduct of learning and teaching to ensure that learners are equipped with new economy skills and dispositions for creating significant and relevant meaning out of the large chunks of transmitted data. In the spirit of building learning organisations, this paper proposes that a two-pronged strategy of promoting self-organised learning (SoL) amongst educators and students be adopted. As an enabling framework based on social constructivism, the model of SoL, originally developed by Harri-Augstein & Thomas, is described and applied to an educational setting. For educators engaged in action research, SoL is suited as an approach for managing and reflecting upon change. The use of two such thinking tools, the Personal Learning Contract and the Purpose-Strategy-Outcome-Review (PSOR) reflective learning scaffolds are considered. For students who are now expected to learn independently in situations requiring problem-solving skills, much akin to real life contexts, this article also considers the application of Learning Plans as a conversational tool for personal project management. The authors conclude that SoL promotes skilful critical thinking through a systems thinking process of continuous reflective learning. It is proposed that these are essential qualities for citizens working in a technological age. Case study samples of the thinking tools used in this action research project are included as appendices and evaluated in this article

Learning in Real-Time Search: A Unifying Framework

Real-time search methods are suited for tasks in which the agent is interacting with an initially unknown environment in real time. In such simultaneous planning and learning problems, the agent has to select its actions in a limited amount of time, while sensing only a local part of the environment centered at the agents current location. Real-time heuristic search agents select actions using a limited lookahead search and evaluating the frontier states with a heuristic function. Over repeated experiences, they refine heuristic values of states to avoid infinite loops and to converge to better solutions. The wide spread of such settings in autonomous software and hardware agents has led to an explosion of real-time search algorithms over the last two decades. Not only is a potential user confronted with a hodgepodge of algorithms, but he also faces the choice of control parameters they use. In this paper we address both problems. The first contribution is an introduction of a simple three-parameter framework (named LRTS) which extracts the core ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*, SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they are unified and extended with additional features. Second, we prove completeness and convergence of any algorithm covered by the LRTS framework. Third, we prove several upper-bounds relating the control parameters and solution quality. Finally, we analyze the influence of the three control parameters empirically in the realistic scalable domains of real-time navigation on initially unknown maps from a commercial role-playing game as well as routing in ad hoc sensor networks