Sidekick agents for sequential planning problems

[摘要] Effective Al sidekicks must solve the interlinked problems of understanding what their human collaborator;;s intentions are and planning actions to support them. This thesis explores a range of approximate but tractable approaches to planning for AI sidekicks based on decision-theoretic methods that reason about how the sidekick;;s actions will effect their beliefs about unobservable states of the world, including their collaborator;;s intentions. In doing so we extend an existing body of work on decision-theoretic models of assistance to support information gathering and communication actions. We also apply Monte Carlo tree search methods for partially observable domains to the problem and introduce an ensemble-based parallelization strategy. These planning techniques are demonstrated across a range of video game domains.

[发布机构] Massachusetts Institute of Technology