  Game semantics is a powerful method of semantic analysis for programming
languages. It gives mathematically accurate models ("fully abstract") for a
wide variety of programming languages. Game semantic models are combinatorial
characterisations of all possible interactions between a term and its syntactic
context. Because such interactions can be concretely represented as sets of
sequences, it is possible to ask whether they can be learned from examples.
Concretely, we are using long short-term memory neural nets (LSTM), a technique
which proved effective in learning natural languages for automatic translation
and text synthesis, to learn game-semantic models of sequential and concurrent
versions of Idealised Algol (IA), which are algorithmically complex yet can be
concisely described. We will measure how accurate the learned models are as a
function of the degree of the term and the number of free variables involved.
Finally, we will show how to use the learned model to perform latent semantic
analysis between concurrent and sequential Idealised Algol.
