Artificial Grammar Learning (AGL) is a powerful experimental paradigm for testing specific hypotheses about language acquisition but is limited because of its reliance on meaningless grammatical...Show moreArtificial Grammar Learning (AGL) is a powerful experimental paradigm for testing specific hypotheses about language acquisition but is limited because of its reliance on meaningless grammatical structures. Meanwhile, formal and computational semantics provide rigorous ways to define and calculate meanings for formal languages, but are typically only used to describe or simulate the linguistic competence of adult speakers. This thesis attempts to connect these two fields by proposing a new type of AGL experiment that uses a language with both a context-free syntax and a formally defined semantics which can be used to express spatial relationships between objects. Moreover, using a computer simulation in which an Intelligent Agent (IA) acquires such a language, it shows how this new paradigm can be used to test psycholinguistic hypotheses about the acquisition of both syntax and semantics.Show less