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From the abstract: "Collective Artificial Intelligence (CAI) simulates human intelligence from data contributed by many humans, mined for inter-related patterns. This thesis applies CAI to social role-playing, introducing an end-to-end process for compositing recorded performances from thousands of humans, and simulating open-ended interaction from this data. The CAI process combines crowdsourcing, pattern discovery, and case-based planning. Content creation is crowdsourced by recording role-players online. Browser-based tools allow non-experts to annotate data, organizing content into a hierarchical narrative structure. Patterns discovered from data power a novel system combining plan recognition with case-based planning. The combination of this process and structure produces a new medium, which exploits a massive corpus to realize characters who interact and converse with humans. This medium enables new experiences in videogames, and new classes of training simulations, therapeutic applications, and social robots. .... As a proof of concept, a CAI system has been evaluated by recording over 10,000 performances in The Restaurant Game, automating an AI-controlled waitress who interacts in the world, and converses with a human via text or speech. Quantitative results demonstrate CAI supports significantly open-ended interaction with humans, while focus groups reveal factors for improving engagement."