Affiliations: Computer Science Department, Georgetown University, Washington DC, USA. [email protected] | Computer Science Department, Georgetown University, Washington DC, USA. [email protected]
Note: [] Supported in part by Craves Family Professorship. This material was based on work supported by the National Science Foundation, while working at the Foundation. Any opinion, finding, and conclusions or recommendations expressed in this material; are those of the author and do not necessarily reflect the views of the National Science Foundation.
Note: [] Supported in part by McBride Chair. Address for correspondence: Department of Computer Science, Georgetown University, Washington, DC., USA
Abstract: We consider the inductive inference model of Gold [15]. Suppose we are given a set of functions that are learnable with certain number of mind changes and errors. What can we consistently predict about those functions if we are allowed fewer mind changes or errors? In [20] we relaxed the notion of exact learning by considering some higher level properties of the input-output behavior of a given function. in this context, a learner produces a program that describes the property of a given function. Can we predict generic properties such as threshold or modality if we allow fewer number of mind changes or errors? These questions were completely answered in [20] when the learner is restricted to a single IIM. In this paper we allow a team of IIMs to collaborate in the learning process. The learning is considered to be successful if any one of the team member succeeds. A motivation for this extension is to understand and characterize properties that are learnable for a given set of functions in a team environment.
Keywords: Inductive Inference, properties of functions, mind changes, errors, learning, teams
