Following the suggestion by reviewer # 3, we will change the title to "A Novel Automated Curriculum Strategy

In this direction, a number of techniques from the field of AI have been applied to BPMSs with the aim of increasing the degree of automated process adaptation (Marrella, 2018, 2019). In (Gajewski et al., 2005; Ferreira and Ferreira, 2006; Marrella and Lespérance, 2013, 2017), if a task failure occurs at run-time and leads to a process goal violation, a new complete process definition that complies with the goal is generated relying on a partial-order AI planner. As a side effect, this often significantly modifies the assignment of tasks to process participants. The work (Bucchiarone et al., 2011) proposes a goal-driven approach to adapt processes to run-time context changes. Process and context changes that prevent goal achievement are specified at design-time and recovery strategies are built at run-time through an adaptation mechanism based on service composition via AI planning.

Verification of AI is a challenge that has engineering, algorithmic and programming language components. For example, AI planners are deployed to model actions of autonomous agents. They comprise a number of searching algorithms that, given a set of specified properties, find a sequence of actions that satisfy these properties. Although AI planners are mature tools from the algorithmic and engineering points of view, they have limitations as programming languages. Decidable and efficient automated search entails restrictions on the syntax of the language, prohibiting use of higher-order properties or recursion. This paper proposes a methodology for embedding plans produced by AI planners into dependently-typed language Agda, which enables users to reason about and verify more general and abstract properties of plans, and also provides a more holistic programming language infrastructure for modelling plan execution.

The open-endedness of the challenge means that AIs need to master multiple objectives. To win, they must impress eight human judges from a range of backgrounds, including architects, archaeologists, and game designers. These judges score the AI city planners in four areas: how well they adapt their designs to specific locations; how well the layouts work, according to criteria such as whether there are bridges and roads between different areas; how appealing they are aesthetically; and how much the designs evoke a narrative--are there details that tell a story about how a town came to be, such as a ruin or a pit from which building materials might have been mined? "Making a Minecraft village for an unseen map is something a 10-year-old human could do," says Salge. "But it is really difficult for an AI." For example, one entrant started by identifying the type of environment--desert or forest, say--and then generated buildings that looked as if they had been built out of common local materials.

Use the AI Planner package to create agents that generate and execute plans. For example, use AI Planner to create an NPC, generate storylines, or validate game/simulation mechanics. The AI Planner package also includes authoring tools and a plan visualizer. To install this package, follow the instructions in the Package Manager documentation. During execution, it is also useful to view an agent's plan through the plan visualizer.