  Speech-controlled user interfaces facilitate the operation of devices and
household functions to laymen. State-of-the-art language technology scans the
acoustically analyzed speech signal for relevant keywords that are subsequently
inserted into semantic slots to interpret the user's intent. In order to
develop proper cognitive information and communication technologies, simple
slot-filling should be replaced by utterance meaning transducers (UMT) that are
based on semantic parsers and a mental lexicon, comprising syntactic, phonetic
and semantic features of the language under consideration. This lexicon must be
acquired by a cognitive agent during interaction with its users. We outline a
reinforcement learning algorithm for the acquisition of syntax and semantics of
English utterances, based on minimalist grammar (MG), a recent computational
implementation of generative linguistics. English declarative sentences are
presented to the agent by a teacher in form of utterance meaning pairs (UMP)
where the meanings are encoded as formulas of predicate logic. Since MG
codifies universal linguistic competence through inference rules, thereby
separating innate linguistic knowledge from the contingently acquired lexicon,
our approach unifies generative grammar and reinforcement learning, hence
potentially resolving the still pending Chomsky-Skinner controversy.
