Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
AI planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will tra...
In test generation based on model-checking, white-box test criteria are represented as trap conditions written in a temporal logic. A model checker is used to refute trap conditio...
Symbolic analysis shows promise as a foundation for bug-finding, specification inference, verification, and test generation. This paper addresses demand-driven symbolic analysi...