  We propose a theory of learning aimed to formalize some ideas underlying
Coquand's game semantics and Krivine's realizability of classical logic. We
introduce a notion of knowledge state together with a new topology, capturing
finite positive and negative information that guides a learning strategy. We
use a leading example to illustrate how non-constructive proofs lead to
continuous and effective learning strategies over knowledge spaces, and prove
that our learning semantics is sound and complete w.r.t. classical truth, as it
is the case for Coquand's and Krivine's approaches.
