最終更新日：2025/12/07
(probability theory, machine learning) An algorithm that allocates a fixed limited set of resources between competing alternative choices so as to maximize the expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or allocations are made.
音声機能が動作しない場合はこちらをご確認ください
正解を見る
multi-armed bandit
編集履歴（0）
元となった辞書の項目
multi-armed bandit
名詞
(probability
theory,
machine
learning)
An
algorithm
that
allocates
a
fixed
limited
set
of
resources
between
competing
alternative
choices
so
as
to
maximize
the
expected
gain,
when
each
choice's
properties
are
only
partially
known
at
the
time
of
allocation,
and
may
become
better
understood
as
time
passes
or
allocations
are
made.
日本語の意味
限られた資源を複数の選択肢（腕）に配分し、各選択肢の特性が部分的にしか把握されていない状況で、期待される収益を最大化するためのアルゴリズムである。 / 実験と活用のバランスをとるため、各選択肢の性質が順次明らかになる中で、最適な選択を導くための手法と解釈できる。
意味（1）
(probability
theory,
machine
learning)
An
algorithm
that
allocates
a
fixed
limited
set
of
resources
between
competing
alternative
choices
so
as
to
maximize
the
expected
gain,
when
each
choice's
properties
are
only
partially
known
at
the
time
of
allocation,
and
may
become
better
understood
as
time
passes
or
allocations
are
made.
（ plural ）
