Adam Jardine

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The current study examines the generative power of Autosegmental Phonology (Goldsmith, 1976, 1979, 1990) in the framework of Formal Language Theory, with which we can study the computational complexity of phonological phenomena and formalisms independent of specific theoretical frameworks in phonology. A methodology for a model-theoretic study of(More)
The Tier-based Strictly 2-Local (TSL2) languages are a class of formal languages which have been shown to model long-distance phonotactic generalizations in natural language (Heinz et al., 2011). This paper introduces the Tier-based Strictly 2-Local Inference Algorithm (2TSLIA), the first nonenumerative learner for the TSL2 languages. We prove the 2TSLIA is(More)
In this work, we consider an agent playing a turnbased game in a known environment against an adversary with unknown dynamics. The model of the adversary is assumed to belong to a subclass of regular languages that can be learned in the limit. We use tools from formal methods to synthesize a control strategy for the agent to win the game as it learns the(More)
In this paper, we present a new algorithm that can identify in polynomial time and data using positive examples any class of subsequential functions that share a particular finitestate structure. While this structure is given to the learner a priori, it allows for the exact learning of partial functions, and both the time and data complexity of the(More)
Long-distance phonotactics, in which the surface sounds of a language are subject to coocurrence constraints referring to nonadjacent segments, present a difficult learning problem. To acquire such patterns, a learner must find dependencies among distant segments. This paper approaches an idealized version of this problem: how can these patterns even be(More)
The paper demonstrates that aspects of resilience of supervisory Cyber-Physical Systems (CPSs) can be improved through the inclusion of appropriate learning modules in the subordinate autonomous agents. During normal operation, individual agents keep track of their supervisor's commands and utilize the learning module, based on Grammatical Inference, to(More)