Learning Local Forward Models on Unforgiving Games
  This paper examines learning approaches for forward models based on local
cell transition functions. We provide a formal definition of local forward
models for which we propose two basic learning approaches. Our analysis is
based on the game Sokoban, where a wrong action can lead to an unsolvable game
state. Therefore, an accurate prediction of an action's resulting state is
necessary to avoid this scenario.
  In contrast to learning the complete state transition function, local forward
models allow extracting multiple training examples from a single state
transition. In this way, the Hash Set model, as well as the Decision Tree
model, quickly learn to predict upcoming state transitions of both the training
and the test set. Applying the model using a statistical forward planner showed
that the best models can be used to satisfying degree even in cases in which
the test levels have not yet been seen.
  Our evaluation includes an analysis of various local neighbourhood patterns
and sizes to test the learners' capabilities in case too few or too many
attributes are extracted, of which the latter has shown do degrade the
performance of the model learner.
