Testing by means of inductive program learning

Inductive Learning of Pronunciation Rules by hypothesis Testing and Correction

This paper describes a system that learns the ru les o f pronunciat ion i nduc t i ve l y . I t begins wi th a set of 26 ru les for s i n g l e l e t t e r pronunc ia t ion. Ind iv idua l words are presented to i t , and the system uses i t s ru le set to hypothesise a pronunc ia t ion . This is compared wi th a d ic t ionary pronunciat ion, and if any part of the pronunciat ion is incor rec t ...

Constructing Orders By Means of Inductive Definitions

Applying Inductive Program Synthesis to Macro Learning

The goal of this paper is to demonstrate that inductive progrwn synthesis can be applied to learning macrooperators from planning experience. We define macros as recursive program schemes (RPSs). An RPS represents the complete subgoal structure of a given problem domain with arbitrary complexity (e. g., rocket transportation problem with n objects), that is, it represents domain specific contro...

Consequences of Means Testing Social Security: Evidence from the SSI Program by David Neumark and Elizabeth Powers CONSEQUENCES OF MEANS TESTING SOCIAL SECURITY: EVIDENCE FROM THE SSI PROGRAM

We attempt to draw inferences about the potential behavioral responses to means testing Social Security by examining the effects of the Supplementary Security Income (SSI) program for the aged on wealth accumulation and employment. Part of the SSI program provides payments to the poor elderly, thus operating as a means-tested public retirement program. The federal government sets eligibility cr...

Inductive Modelling of Temporal Sequences by Means of Self-organization

In this paper we present a new self-organizing neural network, which builds a spatiotemporal model of an input temporal sequence inductively. The network is an extension of Kohonen’s Self-organizing Map with a modified Hebb’s rule for update of temporal synapses. The model building behavior is shown on inductive learning of a transition matrix from a data generated by a Markov Chain.