Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?
This is an incremental improvement for researchers and practitioners in AI planning and goal recognition, addressing a specific bottleneck in computational efficiency.
The paper tackles the problem of computational efficiency in goal recognition by showing that using initial state landmarks in a planning landmark based approach does not improve performance, and empirical results demonstrate that omitting these landmarks enhances goal recognition performance.
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.