Computational learning theory is a branch of theoretical computer science that re-imagines the role of an algorithm from an agent of computation to an agent of learning. The operations of computers become those of the human mind; an important step towards illuminating the limitations of artificial intelligence. The central difference between a learning algorithm and
a traditional algorithm is that the learner has access to an oracle who, in constant time, can answer queries about that to be learned. Normally an algorithm would have to discover such information on its own accord. This subtle change in how we model problem solving results in changes in the computational complexity of some classic
problems; allowing us to re-examine them in a new light. Specifically two known result are examined: one positive, one negative.
It is know that one can efficiently learn Deterministic Finite Automatons with queries, not so of Non-Deterministic Finite Automatons. We generalize these Automatons into Labeled Transition Systems and
attempt to learn them using a stronger query.
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/1262 |
| Date | 19 November 2008 |
| Creators | Shenkenfelder, Warren |
| Contributors | Kapron, Bruce, King, Valerie |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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