Unit 7 1 IntroductionYouTube↗  IA↗(long shot)
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welcome back so far we've talked about AI as managing complexity and uncertainty we've seen how search can discover sequences of actions to solve
お帰りなさい これまではAIについて複雑性や不確実性を 管理するものとして話してきました 検索で問題を解決するための 一連の動きを見つける方法や
problems we've seen how probability theory can represent and reason with
確率論が不確実性の理由を表す方法を 見てきました
uncertainty and we've seen how machine learning can be used to learn and improve AI is a big and dynamic field
そして機械学習が学習と向上に 利用される方法を見てきました
because we're pushing against complexity in at least three directions first in
AIは大きく力強い分野です 複雑ではあるものの 3つの分野に力を入れているからです
terms of agent design we start with a
まずエージェントデザインに関してです
simple reflex based agent and move into
単純反射エージェントから始めて
goal based and utility based ages
目的ベースや 効用ベースエージェントに移ります
secondly in terms of the complexity of
2つ目は環境の複雑性に関してです
the environment we start with simple environments and then start looking at partial observability at stochastic
簡単な環境から始めて部分観測性や
actions at multiple agents and so on and
複数のエージェントへの確率的な動作などを見ます
finally in terms of representation the
そして最後に表現についてです
agents model of the world becomes increasingly complex in this unit we'll concentrate on that third aspect of
世界のエージェントモデルは 徐々に複雑になっています このレッスンでは 3つ目の表現の点に集中します
representation showing how the tools of logic can be used by an agent to better model the world
論理のツールが世界をより上手くモデル化するため エージェントにどう利用されるのかを見せます
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