### Abstract

Predictive State Representations (PSRs) are an efficient
method for modelling partially observable
dynamical systems. They have shown advantages
over the latent state-based approaches by using
functions of a set of observable quantities, called
tests, to represent model states. As a consequence,
discovering the set of tests is one of the central
problems in PSRs. Existing techniques either discover
the tests through iterative methods, which
can only solve toy problems, or avoid the complex
discovery problem by maintaining a very large
set of tests, which may be prohibitively expensive.
In this paper, we propose a new approach to discovering
tests by formulating it as one sequential
decision making problem. To solve the decision
making problem, we resort to the Monte-Carlo tree
search (MCTS) algorithm that has shown significant
advantage on solving complex search problems.
We further develop the concept of model entropy
for measuring the model accuracy as the evaluation
function in MCTS. We conduct experiments
on several domains including one extremely large
domain and the experimental results show the effectiveness
of our approach.

Original language | English |
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Publication status | Published - 9 Jul 2016 |

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## Cite this

Liu, Y., Zhu, H., Zeng, Y., & Dai, Z. (2016).

*Learning Predictive State Representations via Monte-Carlo Tree Search*.