We consider the problems of first-order unification and type inference from a general perspective on problem-solving, namely that of information increase in the problem context. This leads to a powerful technique for implementing type inference algorithms. We describe a unification algorithm and illustrate the technique for the familiar Hindley-Milner type system, but it can be applied to more advanced type systems. The algorithms depend on well-founded contexts: type variable bindings and type-schemes for terms may depend only on earlier bindings. We ensure that unification yields a most general unifier, and that type inference yields principal types, by advancing definitions earlier in the context only when necessary

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