The More the Merrier?! Evaluating the Effect of Landmark Extraction
  Algorithms on Landmark-Based Goal Recognition
  Recent approaches to goal and plan recognition using classical planning
domains have achieved state of the art results in terms of both recognition
time and accuracy by using heuristics based on planning landmarks. To achieve
such fast recognition time these approaches use efficient, but incomplete,
algorithms to extract only a subset of landmarks for planning domains and
problems, at the cost of some accuracy. In this paper, we investigate the
impact and effect of using various landmark extraction algorithms capable of
extracting a larger proportion of the landmarks for each given planning
problem, up to exhaustive landmark extraction. We perform an extensive
empirical evaluation of various landmark-based heuristics when using different
percentages of the full set of landmarks. Results show that having more
landmarks does not necessarily mean achieving higher accuracy and lower spread,
as the additional extracted landmarks may not necessarily increase be helpful
towards the goal recognition task.
