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Much of the work in the area of automated scheduling systems is based on the assumption that the intended execution environment is static and deterministic. The work presented in this thesis is motivated by recognition of the fact that most real world scheduling environments are dynamic and stochastic. It views the scheduling task as one of satisfaction rather than optimisation, and maintenance over creation. This thesis reviews existing work in the area and identifies an opportunity to combine recent advances in scheduling technology with the power of distributed processing. Within a suitable problem-solving architecture it is argued that this combination can help to address the fundamental problems of execudonal uncertainty, conflicting objectives and combinatorial complexity. A scheduling system, DAS, which employs such a problem-solving architecture, is presented. It is distributed, asynchronous and hierarchical, and requires careful management of problem-solving effort. DAS adopts an opportunistic approach to problem-solving and the management of problem-solving effort. The mechanisms which manage problem-solving effort within DAS are also presented. In conclusion it is argued that the architecture and mechanisms presented lend themselves very well to the view taken of the scheduling task.

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Variation in tutoring strategy plays an important part in Intelligent Tutoring Systems (ITSs). The potential for providing an adaptive ITS depends initially on having a range of tutoring strategies to select from. However, in order to react effectively to the student's needs, an ITS not only has to be able to simply offer different tutoring strategies but to choose intelligently among them and determine which one is best for an individual student at a particular moment. This thesis first examines, through literature review and interactions with existing systems, the current practices of ITSs regarding the provision of multiple tutoring strategies and tutoring strategy selection. What stems from this examination are the principles that underlie tutoring strategys election. These principles of tutoring strategy selection serve as a foundation for the construction of the model for tutoring strategy selection. To demonstrate the benefits of having such a model for formalising selection, the model is then implemented in ARISTOTLE, an existing ITS for tutoring zoology that includes several tutoring strategies but uses ad hoc mechanisms for choosing among them. This research is therefore contributing, through the principles of, and the model for tutoring strategy selection, a formal basis for selecting among tutoring strategies in ITSs that incorporate multiple tutoring strategies.