Plan Recognition Using Statistical–Relational Models
Sindhu Raghavana, Parag Singlab and Raymond J. Mooneya, aUniversity of Texas at Austin, Austin, TX, USA, bIndian Institute of Technology Delhi, Hauz Khas, DL, India
Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive-reasoning task that involves inferring plans that best explain observed actions. Most existing approaches to plan-recognition and other abductive-reasoning tasks either use first-order logic (or subsets of it) or probabilistic graphical models. While the former cannot handle uncertainty in the data, the latter cannot handle structured representations. To overcome these limitations, we explore ...