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 finite-state 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)
The Tier-based Strictly 2-Local (TSL 2) 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 non-enumerative learner for the TSL 2 languages. We prove the 2TSLIA… (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)
Autosegmental phonology represents words with graph structures. This paper introduces a way of reasoning about autosegmental graphs as strings of concatenated graph primitives. The main result shows that the sets of au-tosegmental graphs so generated obey two important, putatively universal, constraints in phonological theory provided that the graph… (More)
We present a theory of phonology based on the computational properties of input-output mappings. These properties define restrictive classes of mappings which are learnable by a provably correct, efficient algorithm. The algorithm learns both the active surface constraints and the repairs, including opaque mappings.