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.Comment: Will be presented at KI 202
