The thesis of this dissertation is that formal definitions of the syntax and semantics of computer languages are needed. This dissertation investigates two candidates for formally defining computer languages: (1) the formalism of canonical systems for defining the syntax of a computer language and its translation into a target language, and (2) the formalisms of the l-calculus and extended Markov algorithms as a combined formalism used as the basis of a target language for defining the semantics of a computer language.

Dr. Ken Perlin, Advisor“If I have said anything to the contrary I was mistaken. If I say anything to the contrary again I shall be mistaken again. Unless I am mistaken now. Into the dossier with it in any case, in support of whatever thesis you fancy.”- Samuel BeckettACKNOWLEDGEMENTS I would like to thank my advisor, Ken Perlin, for his continual support and inspiration over the course of this research. I am also indebted to my committee members, Helen Nissenbaum, Katherine Isbister, Matthew Stone, and Alan Siegal for their ongoing support and generous contributions of time and input at every stage of the project. Additionally I wish to thank John Cayley, Bill Seaman, Braxton Soderman and Linnea Ogden, each of whom provided important contributions to the ideas presented here. While there are too many to mention individually here, I would like to thank my colleagues, collaborators, and friends who have been generous and tolerant enough to put up with me over the past five years. Finally, I would like to thank my parents and brother who have given me their unconditionally support, however misguided or unintelligible my direction, since the beginning.

The individual cognitive science disciplines all have contributions to make to the understanding and modelling of human learning. Our previous research has explored unsupervised learning of phonology, morphology and low-level syntax, as well as basic noun, verb and preposition ontology and semantics, plus musical and speech prosody. Successful applications using a mix of supervised and unsupervised techniques include speech control of equipment, deep web search, confused word spelling correction, multi-lingual semantic models and audio-visual speech recognition. Our current research is focused on doing simultaneous learning of ontology, syntax and semantics by embedding the learner in realistic situations and by developing lowlevel biologically-plausible models of perceptual and cognitive processing.