ABSTRACT
This new environment provides an interesting framework for different kind of researchers: Artificial Intelligence (AI) researchers that can experiment with their automatic problem solving algorithms, or to develop and design new algorithms in this complex domain; and educational researchers that can use a new kind of tools and techniques that could aid to detect, reason, and solve (automatically) their problems. One of the AI areas most suitable to merge within this context is Automated Planning and Scheduling. Planning generates a plan (sequence or parallelization of activities) such that it achieves a set of goals given an initial state and satisfying a set of domain constraints represented in operators schemas. In scheduling systems, activities are organised along the time line having in mind the resources available. These systems can perfectly handle temporal reasoning and resource consumption, together with some quality criteria (usually time or resource usage) but they cannot produce the needed activities and their precedence relations given that they
lack an expressive language to represent the activities. Traditionally, planning was first performed and the solution was given as an input to the scheduling systems.
