Unification, or two-way pattern matching, is the process of solving an equation involving two first-order terms with variables. Unification is used in type inference in many programming languages and in the execution of logic programs. This means that unification algorithms have to be written over and over again for different term types. Many other functions also make sense for a large class of datatypes; examples are pretty printers, equality checks, maps etc. They can be defined by induction on the structure of user-defined datatypes. Implementations of these functions for different datatypes are closely related to the structure of the datatypes. We call such functions polytypic. This paper describes a unification algorithm parametrised on the type of the terms, and shows how to use polytypism to obtain a unification algorithm that works for all regular term types.

A higher-order function is a function that takes another function as an argument or returns another function as a result. More specifically, a first-order function takes and returns base types, such as integers or lists. A kth-order function takes or returns a function of order k−1. Currying often artificially inflates the order of a function, so we will ignore all inessential currying. (Whether a particular instance of currying is essential or inessential is open to debate, but we expect that our choices will be uncontroversial.) In addition, when calculating the order of a polymorphic function, we instantiate all type variables with base types. Under these assumptions, most common higher-order functions, such as map and foldr, are second-order, so beginning functional programmers often wonder: What good are functions of order three or above? We illustrate functions of up to sixth-order with examples taken from a combinator parsing library.

Combinator parsing is a classic application of functional programming, dating back to at least Burge (1975). Most combinator parsers are based on Wadler's list-of-successes technique (Wadler, 1985). Hutton (1992) popularized the idea in his excellent tutorial Higher-Order Functions for Parsing. In spite of the title, however, he considered only functions of up to order three.

In this paper we describe the discrete interval encoding tree for storing subsets of types having a total order and a predecessor and a successor function. In the following, we consider for simplicity only the case for integer sets; the generalization is not difficult.

The discrete interval encoding tree is based on the observation that the set of integers {i[mid ]a[les ]i[les ]b} can be perfectly represented by the closed interval [a, b]. The general idea is to represent a set by a binary search tree of integers in which maximal adjacent subsets are each represented by an interval. For example, inserting the sequence of numbers 6, 9, 2, 13, 8, 14, 10, 7, 5 into a binary search tree, respectively, into a discrete interval encoding tree results in the tree structures shown in figure 1.

The efficiency of the interval representation, both in terms of space and time, improves with the density of the set, i.e. with the number of adjacencies between set elements. So what we propose is a ‘diet’ (discrete interval encoding tree) for ‘fat’ sets in the sense of ‘the same amount of information with less nodes’.

In type theory a proposition is represented by a type, the type of its proofs. As a consequence, the equality relation on a certain type is represented by a binary family of types. Equality on a type may be conventional or inductive. Conventional equality means that one particular equivalence relation is singled out as the equality, while inductive equality – which we also call identity – is inductively defined as the ‘smallest reflexive relation’. It is sometimes convenient to know that the type representing a proposition is collapsed, in the sense that all its inhabitants are identical. Although uniqueness of identity proofs for an arbitrary type is not derivable inside type theory, there is a large class of types for which it may be proved. Our main result is a proof that any type with decidable identity has unique identity proofs. This result is convenient for proving that the class of types with decidable identities is closed under indexed sum. Our proof of the main result is completely formalized within a kernel fragment of Martin-Löf's type theory and mechanized using ALF. Proofs of auxiliary lemmas are explained in terms of the category theoretical properties of identity. These suggest two coherence theorems as the result of rephrasing the main result in a context of conventional equality, where the inductive equality has been replaced by, in the former, an initial category structure and, in the latter, a smallest reflexive relation.

Expansion postponement is a tantalisingly simple conjecture about pure type systems which has so far resisted all attempts to prove it for any interesting class of systems. We prove the property for all normalising pure type systems, and discuss the connection with typechecking.

This paper is a tutorial on defining recursive descent parsers in Haskell. In the spirit of one-stop shopping, the paper combines material from three areas into a single source. The three areas are functional parsers (Burge, 1975; Wadler, 1985; Hutton, 1992; Fokker, 1995), the use of monads to structure functional programs (Wadler, 1990, 1992a, 1992b), and the use of special syntax for monadic programs in Haskell (Jones, 1995; Peterson et al., 1996). More specifically, the paper shows how to define monadic parsers using do notation in Haskell.

Of course, recursive descent parsers defined by hand lack the efficiency of bottom-up parsers generated by machine (Aho et al., 1986; Mogensen, 1993; Gill and Marlow, 1995). However, for many research applications, a simple recursive descent parser is perfectly sufficient. Moreover, while parser generators typically offer a fixed set of combinators for describing grammars, the method described here is completely extensible: parsers are first-class values, and we have the full power of Haskell available to define new combinators for special applications. The method is also an excellent illustration of the elegance of functional programming.