Tamarian, a fictional language introduced in the Star Trek episode Darmok, communicates meaning through utterances of metaphorical references, such as "Darmok and Jalad at Tanagra" instead of "We should work together." This work assembles a Tamarian-English dictionary of utterances from the original episode and several follow-on novels, and uses this to construct a parallel corpus of 456 English-Tamarian utterances. A machine translation system based on a large language model (T5) is trained using this parallel corpus, and is shown to produce an accuracy of 76% when translating from English to Tamarian on known utterances.

The combination of learning by demonstration and planning has proved an effective solution for real-time strategy games. Nevertheless, learning hierarchical plans from expert traces also has its limitations regarding the number of training traces required, and the absence of mechanisms for rapidly reacting to high priority goals. We propose to bring the game designer back into the loop, by allowing him to explicitly inject decision making knowledge, in the form of behavior trees, to complement the knowledge obtained from the traces. By providing a natural mechanism for designers to inject knowledge into the plan library, we intend to integrate the best of both worlds: learning from traces and hard-coded rules.