6 volumes http://www.aaai.org/Press/Proceedings/aaai17.phpPublisher PD

Meneguzzi, Felipe

Oren, Nir

Pereira, Ramon Fraga

Proceedings of the AAAI Conference on Artificial Intelligence

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art

Landmark-Based Heuristics for Goal Recognition

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