Abstract Education research has emphasized the need to develop instructional design tools to facilitate the generation of learning paths for students. Learning paths are important because they enable the personalization and optimization of the learning process. In this work, we present a flexible conceptual framework that allows the representation of curricula information as Artificial Intelligence Planning and Mathematical Programming models to facilitate the generation of learning paths by domain independent algorithms. The resulting models consider a rich set of properties from the education domain, like hierarchical learning structures, enabling conditions, temporal actions, mandatory activities, quality accumulation functions, and metric information. We show that the proposed mathematical models return optimal solutions very efficiently if we relax the total ordering constraints of learning paths. These relaxations allow evaluating greedy planning algorithms to identify the properties from the models that increase the complexity of solution synthesis. We expect that the results of this research can be helpful to education researchers and computer scientists in the quest of scalable systems that capture more flexible standards to model learning and compute more informed learning paths for students.

One of the most difficult tasks in software development is understanding the behavior of the final product. Making sure that a system behaves as users expect is a challenging endeavor. Understanding the behavior of a multi-agent system is even more challenging given the additional complexities of multi-agent problems. In this paper, we address the problem of users creating visualizations to debug and understand complex multi-agent systems. We introduce VizScript, a generic framework that expedites the process of creating such visualizations. VizScript combines a generic application instrumentation, a knowledge-base, and simple scene definitions primitives with a reasoning system, to produce an easy to use visualization platform. Using VizScript, we were able to recreate the visualizations for a complex multiagent system with an order-of-magnitude less effort than was required in a Java implementation

Coordinators are intelligent software agents that help humans to collaborate and coordinate execution of multiple activities in dynamic, distributed and uncertain domains. One of the main challenges is the generation of effective user interfaces for team coordination. Intelligent interfaces that avoid information overload, and facilitate user interactions to repair mission plans are needed, and they are the focus of this work.

We address the problem of users creating visualizations to debug and understand multi-agent systems. The key challenges are that (1) needs arise dynamically, i.e., it is difficult to know a priori what visualizations one wants, (2) extensive expertise on the system, the algorithms and visualization tools are often needed for implementation, and (3) agents can be running in a distributed environment. We have developed VizScript, a collection of tools to expedite the process of creating visualizations. VizScript combines a generic application instrumentation, a knowledge base, and simple scene definition primitives with a reasoning system, to produce an easy to use visualization system. Using VizScript we were able to recreate the visualizations for a complex multi-agent system with an order-of-magnitude less effort than was required in a Java implementation.

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt, called AltAltp which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAltp derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAltp is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners.