  Anytime inference is inference performed incrementally, with the accuracy of
the inference being controlled by a tunable parameter, usually time. Such
anytime inference algorithms are also usually interruptible, gradually
converging to the exact inference value until terminated. While anytime
inference algorithms for specific domains like probability potentials exist in
the literature, our objective in this article is to obtain an anytime inference
algorithm which is sufficiently generic to cover a wide range of domains. For
this we utilise the theory of generic inference as a basis for constructing an
anytime inference algorithm, and in particular, extending work done on ordered
valuation algebras. The novel contribution of this work is the construction of
anytime algorithms in a generic framework, which automatically gives us
instantiations in various useful domains. We also show how to apply this
generic framework for anytime inference in semiring induced valuation algebras,
an important subclass of valuation algebras, which includes instances like
probability potentials, disjunctive normal forms and distributive lattices.
  Keywords: Approximation; Anytime algorithms; Resource-bounded computation;
Generic inference; Valuation algebras; Local computation; Binary join trees.
