Sarah Keren

Goal recognition is the task of understanding the goal of an acting agent by the online observation of its behavior. Goal recognition design (GRD) facilitates goal recognition by the analysis and redesign of goal recognition models. In a nutshell, given a model of a domain and a set of possible goals, a solution to a GRD problem determines: (1) to what extent do actions, performed by an agent, reveal the agent's objective? and (2) what is the best way to modify the model so that the objective of an agent can be detected as early as possible? GRD answers these questions by offering a solution for assessing and minimizing the maximal progress of any agent before recognition is guaranteed. This approach is relevant to any domain for which quickly performing goal recognition is essential and in which the model design can be controlled. Applications include intrusion detection, assisted cognition, computer games, and human-robot collaboration. The tutorial will provide an overview of the GRD problem in both deterministic and stochastic environments, and present the solutions developed so far for its evaluation and optimization.