Goal Recognition Design
Goal recognition design (GRD) is the task of redesigning environments in order to facilitate online goal recognition. As such, while goal recognition tools are typically aimed at efficiently analyzing online observations of agents (human or automated) in order to infer their objective, GRD focuses on manipulating the environments in which agents act to promote early recognition.
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 within the model, reveal the agent’s objective? and (2) what is the best way to modify the model so that the objective of an agent is revealed as early as possible? GRD answers these questions by offering a solution for assessing and minimizing the maximal progress of an agent in the model before its goal is revealed. 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.
Using several motivating examples, my talk will cover the models and methods created to asses and optimize various GRD settings. In addition, I will present recent work which extends the redesign approach to settings with arbitrary utility measures. The utility maximizing design (UMD) framework brings new exciting directions to explore, such as formulating the design process as a heuristic search and finding informative heuristics to guide the search for optimal design strategies.
Sarah Keren is a postdoctoral fellow at Harvard University, where she is affiliated with the Center for Research on Computation and Society (CRCS). Her mentors are Prof. Barbara Grosz and Prof. David Parkes. Before coming to Harvard, Sarah completed her Ph.D. at the Faculty of Industrial Engineering and Management of the Technion - Israel Institute of Technology, where she was advised by Prof. Avigdor Gal and Dr. Erez Karpas. Sarah's research focuses on manipulating and redesigning environments for optimizing their utility. Her work has appeared in three leading artificial intelligence conferences (AAAI, ICAPS and IJCAI). She has received different excellence awards including an honorable mention for best paper in ICAPS 2014, as well as the Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences.