Preference-Based Monte Carlo Tree Search
  Monte Carlo tree search (MCTS) is a popular choice for solving sequential
anytime problems. However, it depends on a numeric feedback signal, which can
be difficult to define. Real-time MCTS is a variant which may only rarely
encounter states with an explicit, extrinsic reward. To deal with such cases,
the experimenter has to supply an additional numeric feedback signal in the
form of a heuristic, which intrinsically guides the agent. Recent work has
shown evidence that in different areas the underlying structure is ordinal and
not numerical. Hence erroneous and biased heuristics are inevitable, especially
in such domains. In this paper, we propose a MCTS variant which only depends on
qualitative feedback, and therefore opens up new applications for MCTS. We also
find indications that translating absolute into ordinal feedback may be
beneficial. Using a puzzle domain, we show that our preference-based MCTS
variant, wich only receives qualitative feedback, is able to reach a
performance level comparable to a regular MCTS baseline, which obtains
quantitative feedback.
