Cate Blanchett – beloved thespian, film star and refugee advocate – is standing at a lectern, addressing the European Union parliament. "The future is now," she says, authoritatively. So far, so normal, until: "But where the fuck are the sex robots?" The footage is from a 2023 address that Blanchett actually gave – but the rest has been made up. Her voice was generated by Australian artist Xanthe Dobbie using the text-to-speech platform PlayHT, for Dobbie's 2024 video work Future Sex/Love Sounds – an imagining of a sex robot-induced feminist utopia, voiced by celebrity clones.

The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains.