Recent years have seen a growing interest in player modeling for digital games. Goal
recognition, which aims to accurately recognize players' goals from observations of low-level …

In exploratory domains, agents' behaviors include switching between activities, extraneous
actions, and mistakes. Such settings are prevalent in real world applications such as …

This article provides new techniques for optimizing domain design for goal and plan
recognition using plan libraries. We define two new problems: Goal Recognition Design for …

Contemporary cost-based goal-recognition assumes rationality: that observed behaviour is
more or less optimal. Probabilistic goal recognition systems, however, explicitly depend on …

Plan recognition, the problem of inferring the goals or plans of an observed agent, is a key
element of situation awareness in human-machine and machine-machine interactions for …

Plan Recognition algorithms require to recognize a complete hierarchy explaining the
agent's actions and goals. While the output of such algorithms is informative to the …

Plan recognition algorithms output hypotheses about an agent's plans from its observed
actions. Due to imperfect knowledge about the agent's behavior and the environment, it is …

Hierarchical task network (HTN) planning is a model-based approach to planning. The HTN
domain model consists of tasks and methods to decompose them into subtasks until …

Plan recognition aims to discover target plans (ie, sequences of actions) behind observed
actions, with history plan libraries or action models in hand. Previous approaches either …

Hierarchical Task Networks were proposed as a method to describe plans by decomposition
of tasks to subtasks until primitive tasks, actions, are obtained. Valid plans-sequences of …