Church’s Type Theory
Church’s type theory, aka simple type theory, is a formal logical language which includes classical first-order and propositional logic, but is more expressive in a practical sense. It is used, with some modifications and enhancements, in most modern applications of type theory. It is particularly well suited to the formalization of mathematics and other disciplines and to specifying and verifying hardware and software. It also plays an important role in the study of the formal semantics of natural language. And in particular, when used as a metalogic to semantically embed expressive (quantified) non-classical logics, it enables further topical applications in artificial intelligence and philosophy.
A great wealth of technical knowledge can be expressed very naturally in it. With possible enhancements, Church’s type theory constitutes an excellent formal language for representing the knowledge in automated information systems, sophisticated automated reasoning systems, systems for verifying the correctness of mathematical proofs, and a range of projects involving logic and artificial intelligence. Some examples and further references are given in Sections 1.2.2 and 5 below.
Type theories are also called higher-order logics, since they allow quantification not only over individual variables (as in first-order logic), but also over function, predicate, and even higher-order variables. Type theories characteristically assign types to entities, distinguishing, for example, between numbers, sets of numbers, functions from numbers to sets of numbers, and sets of such functions. As illustrated in Section 1.2.2 below, these distinctions allow one to discuss the conceptually rich world of sets and functions without encountering the paradoxes of naive set theory.
Church’s type theory is a formulation of type theory that was introduced by Alonzo Church in Church 1940. In certain respects, it is simpler and more general than the type theory introduced by Bertrand Russell in Russell 1908 and Whitehead & Russell 1927a. Since properties and relations can be regarded as functions from entities to truth values, the concept of a function is taken as primitive in Church’s type theory, and the λ-notation which Church introduced in Church 1932 and Church 1941 is incorporated into the formal language. Moreover, quantifiers and description operators are introduced in a way so that additional binding mechanisms can be avoided, λ-notation is reused instead. λ-notation is thus the only binding mechanism employed in Church’s type theory.
- 1. Syntax
- 2. Semantics
- 3. Metatheory
- 4. Automation
- 5. Applications
- Bibliography
- Academic Tools
- Other Internet Resources
- Related Entries
1. Syntax
1.1 Fundamental Ideas
We start with an informal description of the fundamental ideas underlying the syntax of Church’s formulation of type theory.
All entities have types, and if α and β are types, the type
of functions from elements of type β to elements of type α
is written as
As noted by Schönfinkel (1924), functions of more than one
argument can be represented in terms of functions of one argument when
the values of these functions can themselves be functions. For
example, if f is a function of two arguments, for each
element x of the left domain of f there is a
function g (depending on x) such that
For a more explicit example, consider the function + which carries any
pair of natural numbers to their sum. We may denote this function by
More generally, if f is a function which maps
n-tuples
A set or property can be represented by a function (often called
characteristic function) which maps elements to truth values,
so that an element is in the set, or has the property, in question iff
the function representing the set or property maps that element to
truth. When a statement is asserted, the speaker means that it is
true, so that
Expressions which denote elements of type α are called
well-formed formulas (wffs) of type α. Thus, statements
of type theory are wffs of type
If
We use
would be written
(By the Axiom of Extensionality for truth values, when
Propositional connectives and quantifiers can be assigned types and
can be denoted by constants of these types. The negation function maps
truth values to truth values, so it has type
The statement
should be true, as well as
for any wff
this equation can be written more simply as
Thus,
1.2 Formulas (as certain terms)
Before we state the definition of a “formula”, a word of
caution is in order. The reader may be accustomed to thinking of a
formula as an expression which plays the role of an assertion in a
formal language, and of a term as an expression which designates an
object. Church’s terminology is somewhat different, and provides
a uniform way of discussing expressions of many different types. What
we call well-formed formula of type α (
1.2.1 Definitions
Type symbols are defined inductively as follows:
is a type symbol (denoting the type of individuals). There may also be additional primitive type symbols which are used in formalizing disciplines where it is natural to have several sorts of individuals. is a type symbol (denoting the type of truth values).- If α and β are type symbols, then
is a type symbol (denoting the type of functions from elements of type β to elements of type α).
The primitive symbols are the following:
- Improper symbols: [, ],
- For each type symbol α, a denumerable list of
variables of type
- Logical constants:
, , and (for each type symbol α). The types of these constants are indicated by their subscripts. The symbol denotes negation, denotes disjunction, and is used in defining the universal quantifier as discussed above. serves either as a description or selection operator as discussed in Section 1.3.4 and Section 1.3.5 below. - In addition, there may be other constants of various types, which will be called nonlogical constants or parameters. Each choice of parameters determines a particular formulation of the system of type theory. Parameters are typically used as names for particular entities in the discipline being formalized.
A formula is a finite sequence of primitive symbols. Certain
formulas are called well-formed formulas (wffs). We
write
- A primitive variable or constant of type α is a
wff
. - If
and are wffs of the indicated types, then is a wff . - If
is a variable of type β and is a wff, then is a wff .
Note, for example, that by (a)
Abbreviations:
stands for . stands for . stands for .- Other propositional connectives, and the existential quantifier,
are defined in familiar ways. In particular,
stands for x y . stands for .
The last definition is known as the Leibnizian definition of equality. It asserts that x and y are the same if y has every property that x has. Actually, Leibniz called his definition “the identity of indiscernibles” and gave it in the form of a biconditional: x and y are the same if x and y have exactly the same properties. It is not difficult to show that these two forms of the definition are logically equivalent.
1.2.2 Examples
We now provide a few examples to illustrate how various assertions and concepts can be expressed in Church’s type theory.
Example 1 To express the assertion that
“Napoleon is charismatic” we introduce constants
If we wish to express the assertion that “Napoleon has all the
properties of a great general”, we might consider interpreting
this to mean that “Napoleon has all the properties of some great
general”, but it seems more appropriate to interpret this
statement as meaning that “Napoleon has all the properties which
all great generals have”. If the constant
As an example of such a property, we note that the sentence “Napoleon’s soldiers admire him” can be expressed in a similar way by the wff
By λ-conversion, this is equivalent to
This statement asserts that one of the properties which Napoleon has is that of being admired by his soldiers. The property itself is expressed by the wff
Example 2 We illustrate some potential applications of type theory with the following fable.
A rich and somewhat eccentric lady named Sheila has an ostrich and a cheetah as pets, and she wishes to take them from her hotel to her remote and almost inaccessible farm. Various portions of the trip may involve using elevators, boxcars, airplanes, trucks, very small boats, donkey carts, suspension bridges, etc., and she and the pets will not always be together. She knows that she must not permit the ostrich and the cheetah to be together when she is not with them.
We consider how certain aspects of this problem can be formalized so that Sheila can use an automated reasoning system to help analyze the possibilities.
There will be a set Moments of instants or intervals of time
during the trip. She will start the trip at the location
We define a plan p to be acceptable if, according to that
plan, the group starts at the hotel, finishes at the farm, and
whenever the ostrich and the cheetah are together, Sheila is there
too. Formally, we define
We can express the assertion that Sheila has a way to accomplish her objective with the formula
Example 3 We now provide a mathematical example.
Mathematical ideas can be expressed in type theory without introducing
any new constants. An iterate of a function f from a
set to itself is a function which applies f one or more
times. For example, if
Thus, g is an iterate of f if g is in every
set p of functions which contains f and which
contains the function
A fixed point of f is an element y such
that
It can be proved that if some iterate of a function f has a unique fixed point, then f itself has a fixed point. This theorem can be expressed by the wff
See Andrews et al. 1996, for a discussion of how this theorem, which is called THM15B, can be proved automatically.
Example 4 An example from philosophy is Gödel’s variant of the ontological argument for the existence of God. This example illustrates two interesting aspects:
-
Church’s type theory can be employed as a meta-logic to concisely embed expressive other logics such as the higher-order modal logic assumed by Gödel. By exploiting the possible world semantics of this target logic, its syntactic elements are defined in such a way that the infrastructure of the meta-logic are reused as much as possible. In this technique, called shallow semantical embedding, the modal operator
, for example, is simply identified with (taken as syntactic sugar for) the λ-formulawhere
denotes the accessibility relation associated with and type is identified with possible worlds. Moreover, since is shorthand in Church’s type theory for , the modal formulais represented as
where
stands for the λ-termand the
gets resolved as described above. The above choice of realizes a possibilist notion of quantification. By introducing a binary “existence” predicate in the metalogic and by utilizing this predicate as an additional guard in the definition of an actualist notion of quantification can be obtained. Expressing that an embedded modal formula is globally valid is captured by the formula . Local validity (and also actuality) can be modeled as , where is a nominal (constant symbol in the meta-logic) denoting a particular possible world. -
The above technique can be exploited for a natural encoding and automated assessment of Gödel’s ontological argument in higher-order modal logic, which unfolds into formulas in Church’s type theory such that higher-order theorem provers can be applied. Further details are presented in Section 6 (Logic and Philosophy) of the SEP entry on automated reasoning and also in Section 5.2; moreover, see Benzmüller & Woltzenlogel-Paleo 2014 and Benzmüller 2019.
Example 5 Suppose we omit the use of type symbols in
the definitions of wffs. Then we can write the formula
1.3 Axioms and Rules of Inference
1.3.1 Rules of Inference
- Alphabetic Change of Bound Variables
-conversion). To replace any well-formed part of a wff by , provided that does not occur in and is not bound in . - β-contraction. To replace any well-formed part
of a wff by , provided that the bound variables of are distinct both from and from the free variables of . - β-expansion. To infer
from if can be inferred from by a single application of β-contraction. - Substitution. From
, to infer , provided that is not a free variable of . - Modus Ponens. From
and , to infer . - Generalization. From
to infer , provided that is not a free variable of .
1.3.2 Elementary Type Theory
We start by listing the axioms for what we shall call elementary type theory.
The theorems of elementary type theory are those theorems which can be
derived, using the rules of inference, from Axioms
To illustrate the rules and axioms introduced above, we give a short
and trivial proof in
Note that line 3 can be written as
and line 7 can be written as
We have thus derived a well known law of quantification theory. We
illustrate one possible interpretation of the line
If it is true of every horse that the gate was closed or that the horse left the corral, then the gate was closed or every horse left the corral.
To the axioms listed above we add the axioms below to obtain Church’s type theory.
1.3.3 Axioms of Extensionality
The axioms of boolean and functional extensionality are the following:
Church did not include Axiom
1.3.4 Descriptions
The expression
stands for
For example,
stands for
By λ-conversion, this is equivalent to
which reduces by λ-conversion to
This asserts that there is a unique element which has the property
When there is a unique such element
Since
Axiom of Descriptions:
This says that when the set
If from certain hypotheses one can prove
then by using Axiom
which can also be written as
We illustrate the usefulness of the description operator with a small
example. Suppose we have formalized the theory of real numbers, and
our theory has constants
Of course, in traditional mathematical notation we would not write the
type symbols, and we would write
1.3.5 Axiom of Choice
The Axiom of Choice can be expressed as follows in Church’s type theory:
It is natural to call
Clearly the Axiom of Choice implies the Axiom of Descriptions, but sometimes formulations of type theory are used which include the Axiom of Descriptions, but not the Axiom of Choice.
Another formulation of the Axiom of Choice simply asserts the existence of a choice function without explicitly naming it:
Normally when one assumes the Axiom of Choice in type theory, one
assumes it as an axiom schema, and asserts AC
Before proceeding, we need to introduce some terminology.
Yasuhara (1975) defined the relation “
- If
, then AC AC . - Given a set S of types, none of which is propositional,
there is a model of
in which AC fails if and only if for some β in S.
The existence of a choice functions for “higher” types thus entails the existence of choice functions for “lower” types, the opposite is generally not the case though.
Büchi (1953) has shown that while the schemas expressing the Axiom of Choice and Zorn’s Lemma can be derived from each other, the relationships between the particular types involved are complex.
1.3.6 Axioms of Infinity
One can define the natural numbers (and therefore other basic mathematical structures such as the real and complex numbers) in type theory, but to prove that they have the required properties (such as Peano’s Postulates), one needs an Axiom of Infinity. There are many viable possibilities for such an axiom, such as those discussed in Church 1940, section 57 of Church 1956, and section 60 of Andrews 2002.
1.4 A Formulation Based on Equality
In
Section 1.2.1,
Tarski (1923) noted that in the context of higher-order logic, one can define propositional connectives in terms of logical equivalence and quantifiers. Quine (1956) showed how both quantifiers and connectives can be defined in terms of equality and the abstraction operator λ in the context of Church’s type theory. Henkin (1963) rediscovered these definitions, and developed a formulation of Church’s type theory based on equality in which he restricted attention to propositional types. Andrews (1963) simplified the axioms for this system.
1.4.1 Definitions
- Type symbols, improper symbols, and variables of
are defined as in Section 1.2.1. - The logical constants of
are and (for each type symbol α). - Wffs of
are defined as in Section 1.2.1.
Abbreviations:
stands for stands for A B stands for stands for stands for stands for stands for stands for stands for
Other propositional connectives, such as
1.4.2 Axioms and Rules of Inference
Rule R: Fromand , to infer the result of replacing one occurrence of in by an occurrence of , provided that the occurrence of in is not (an occurrence of a variable) immediately preceded by λ.
The axioms for
2. Semantics
It is natural to compare the semantics of type theory with the semantics of first-order logic, where the theorems are precisely the wffs which are valid in all interpretations. From an intuitive point of view, the natural interpretations of type theory are standard models, which are defined below. However, it is a consequence of Gödel’s Incompleteness Theorem (Gödel 1931) that axioms (1)–(9) do not suffice to derive all wffs which are valid in all standard models, and there is no consistent recursively axiomatized extension of these axioms which suffices for this purpose. Nevertheless, experience shows that these axioms are sufficient for most purposes, and Leon Henkin considered the problem of clarifying in what sense they are complete. The definitions and theorem below constitute Henkin’s (1950) solution to this problem, which is often referred to as general semantics or Henkin semantics.
A frame is a collection
An interpretation
An assignment of values in the frame
An interpretation
for each variable . if is a primitive constant. (the value of a function at the argument . that function from into whose value for each argument is , where is that assignment such that and if .
If an interpretation
One can easily show that the following statements hold in all general
models
and iff iff or iff and iff or iff iff for all iff there exists an such that
The semantics of general models is thus as expected. However, there is a subtlety to note regarding the following condition for arbitrary types α:
- [equality]
iff
When the definitions of
Section 1.2.1
are employed, where equality has been defined in terms of
Leibniz’ principle, then this statement is not implied for all
types α. It only holds if we additionally require that the
domains
When instead the alternative definitions of
Section 1.4
are employed, then this requirement is obviously met due to the
presence of the logical constants
An interpretation
We say that a wff
A wff
Completeness and Soundness Theorem (Henkin 1950): A wff is a theorem if and only if it is valid in the general sense.
Not all frames belong to interpretations, and not all interpretations are general models. In order to be a general model, an interpretation must have a frame satisfying certain closure conditions which are discussed further in Andrews 1972b. Basically, in a general model every wff must have a value with respect to each assignment.
A model is said to be finite iff its domain of individuals is
finite. Every finite model for
An understanding of the distinction between standard and nonstandard
models can clarify many phenomena. For example, it can be shown that
there is a model
While the Axiom of Choice is presumably true in all standard models,
there is a nonstandard model for
Thus far, investigations of model theory for Church’s type theory have been far less extensive than for first-order logic. Nevertheless, there has been some work on methods of constructing nonstandard models of type theory and models in which various forms of extensionality fail, models for theories with arbitrary (possibly incomplete) sets of logical constants, and on developing general methods of establishing completeness of various systems of axioms with respect to various classes of models. Relevant papers include Andrews 1971, 1972a,b, and Henkin 1975. Further related work can be found in Benzmüller et al. 2004, Brown 2004, 2007, and Muskens 2007.
3. Metatheory
3.1 Lambda-Conversion
The first three rules of inference in
Section 1.3.1
are called rules of λ-conversion. If
By using the Axiom of Extensionality one can obtain the following derived rule of inference:
-Contraction. Replace a well-formed part of a wff by , provided does not occur free in .
This rule and its inverse (which is called
It is worth mentioning (again) that λ-abstraction replaces the need for comprehension axioms in Church’s type theory.
3.2 Higher-Order Unification
The challenges in higher-order unification are outlined very briefly. More details on the topic are given in Dowek 2001; its utilization in higher-order theorem provers is also discussed in Benzmüller & Miller 2014.
Definition. A higher-order unifier for a
pair
Higher-order unification differs from first-order unification (Baader & Snyder 2001) in a number of important respects. In particular:
- Even when a unifier for a pair of wffs exists, there may be no most general unifier (Gould 1966).
- Higher-order unification is undecidable (Huet 1973b), even in the “second-order” case (Goldfarb 1981).
However, an algorithm has been devised (Huet 1975, Jensen & Pietrzykowski 1976), called pre-unification, which will find a unifier for a set of pairs of wffs if one exists. The pre-unifiers computed by Huet’s procedure are substitutions that can reduce the original unification problem to one involving only so called flex-flex unification pairs. Flex-flex pairs have variable head symbols in both terms to be unified and they are known to always have a solution. The concrete computation of these solutions can thus be postponed or omitted. Pre-unification is utilized in all the resolution based theorem provers mentioned in Section 4.
Pattern unification refers a small subset of unification problems, first studied by Miller 1991, whose identification has been important for the construction of practical systems. In a pattern unification problem every occurrence of an existentially quantified variable is applied to a list of arguments that are all distinct variables bound by either a λ-binder or a universal quantifier in the scope of the existential quantifier. Thus, existentially quantified variables cannot be applied to general terms but a very restricted set of bound variables. Pattern unification, like first-order unification, is decidable and most general unifiers exist for solvable problems. This is why pattern unification is preferably employed (when applicable) in some state-of-the-art theorem provers for Church’s type theory.
3.3 A Unifying Principle
The Unifying Principle was introduced in Smullyan 1963 (see also Smullyan 1995) as a tool for deriving a number of basic metatheorems about first-order logic in a uniform way. The principle was extended to elementary type theory by Andrews (1971) and to extensional type theory, that is, Henkin’s general semantics without description or choice, by Benzmüller, Brown and Kohlhase (2004). We outline these extensions in some more detail below.
3.3.1 Elementary Type Theory
The Unifying Principle was extended to elementary type theory (the
system
Definition. A property
- If
, then there is no atom such that and . - If
, then . - If
, then . - If
, then or . - If
, then . - If
, then for each wff . - If
, then , for any variable or parameter which does not occur free in or any wff in .
Note that consistency is an abstract consistency property.
Unifying Principle for
Here is a typical application of the Unifying Principle. Suppose there
is a procedure
3.3.2 Extensional Type Theory
Extensions of the above Unifying principle towards Church’s type
theory with general semantics were studied since the mid nineties. A
primary motivation was to support (refutational) completeness
investigations for the proof calculi underlying the emerging
higher-order automated theorem provers (see
Section 4
below). The initial interest was on a fragment of Church’s type
theory, called extensional type theory, that includes the
extensionality axioms, but excludes
A first attempt towards a Unifying Principle for extensional type theory with primitive equality is presented in Kohlhase 1993. The conditions given there, which are still incomplete[1], were subsequently modified and complemented as follows:
-
To obtain a Unifying Principle for extensional type theory with defined equality, Benzmüller & Kohlhase 1997 added the following conditions for boolean extensionality, functional extensionality and saturation to the above conditions 1.-7. for
(their presentation has been adapted here; for technical reasons, they also employ a slightly stronger variant for condition 2. based on β-conversion rather than β-normalization):- If
, then or - If
, then for any parameter which does not occur free in . or
The saturation condition 10. was required to properly establish the principle. However, since this condition is related to the proof theoretic notion of cut-elimination, it limits the utility of the principle in completeness proofs for machine-oriented calculi. The principle was nevertheless used in Benzmüller & Kohlhase 1998a and Benzmüller 1999a,b to obtain a completeness proof for a system of extensional higher-order resolution. The principle was also applied in Kohlhase 1998 to study completeness for a related extensional higher-order tableau calculus,[2] in which the extensionality rules for Leibniz equality were adapted from Benzmüller & Kohlhase 1998a, respectively Benzmüller 1997.
- If
-
Different options for achieving a Unifying Principle for extensional type theory with primitive equality are presented in Benzmüller 1999a (in this work primitive logical constants
were used in addition to , and the ’s; such a redundant choice of logical constants is not rare in higher-order theorem provers). One option is to introduce a reflexivity and substitutivity condition. An alternative is to combine a reflexivity condition with a condition connecting primitive with defined equality, so that the substitutivity condition follows. Note that introducing a defined notion of equality based on the Leibniz principle is, of course, still possible in this context (defined equality is denoted in the remainder of this section by to properly distinguish it from primitive equality ):- Not
- If
, then or
The saturation condition 10. still has to be added independent of which option is considered. The principle was applied in Benzmüller 1999a,b to prove completeness for the extensional higher-order RUE-resolution[3] calculus underlying the higher-order automated theorem prover LEO and its successor LEO-II.
- Not
-
In Benzmüller et al. 2004 the principle is presented in a very general way which allows for various possibilities concerning the treatment of extensionality and equality in the range between elementary type theory and extensional type theory. The principle is applied to obtain completeness proofs for an associated range of natural deduction calculi. The saturation condition is still used in this work.
-
Based on insights from Brown’s (2004, 2007) thesis, a solution for replacing the undesirable saturation condition by two weaker conditions is presented in Benzmüller, Brown, and Kohlhase 2009; this work also further studies the relation between saturation and cut-elimination. The two weaker conditions, termed mating and decomposition, are easier to demonstrate than saturation in completeness proofs for machine-oriented calculi. They are (omitting some type information in the second one and abusing notation):
- If
for atoms and , then - If
, where head symbol is a parameter, then there is an such that .
The modified principle is applied in Benzmüller et al. 2009 to show completeness for a sequent calculus for extensional type theory with defined equality.
- If
-
A further extended Unifying Principle for extensional type theory with primitive equality is presented and used in Backes & Brown 2011 to prove the completeness of a tableau calculus for type theory which incorporates the axiom of choice.
-
A closely related and further simplified principle has also been presented and studied in Steen 2018, where it was applied for showing completeness of the paramodulation calculus that is underlying the theorem prover Leo-III (Steen & Benzmüller 2018, 2021).
3.4 Cut-Elimination and Cut-Simulation
Cut-elimination proofs (see also the SEP entry on
proof theory)
for Church’s type theory, which are often closely related to
such proofs (Takahashi 1967, 1970; Prawitz 1968; Mints 1999) for other
formulations of type theory, may be found in Andrews 1971, Dowek &
Werner 2003, and Brown 2004. In Benzmüller et al. 2009 it is
shown how certain wffs
3.5 Expansion Proofs
An expansion proof is a generalization of the notion of a
Herbrand expansion of a theorem of first-order logic; it provides a
very elegant, concise, and nonredundant representation of the
relationship between the theorem and a tautology which can be obtained
from it by appropriate instantiations of quantifiers and which
underlies various proofs of the theorem. Miller (1987) proved that a
wff
In Brown 2004 and 2007, this concept is generalized to that of an extensional expansion proof to obtain an analogous theorem involving type theory with extensionality.
3.6 The Decision Problem
Since type theory includes first-order logic, it is no surprise that
most systems of type theory are undecidable. However, one may look for
solvable special cases of the decision problem. For example, the
system
Although the system
A wff of the form
A wff of the form
4. Automation
4.1 Machine-Oriented Proof Calculi
The development, respectively improvement, of machine-oriented proof calculi for Church’s type theory has made good progress in recent years; see, e.g., the references on superposition based calculi given below. The challenges are obviously much bigger than in first-order logic. The practically way more expressive nature of the term-language of Church’s type theory causes a larger, bushier and more difficult to traverse proof search space than in first-order logic. Moreover, remember that unification, which constitutes a very important control and filter mechanism in first-order theorem proving, is undecidable (in general) in type theory; see Section 3.2. On the positive side, however, there is a chance to find significantly shorter proofs than in first-order logic. This is well illustrated with a small, concrete example in Boolos 1987, for which a fully automated proof seems now in reach (Benzmüller et al. 2023).
Clearly, further progress is needed to further leverage the practical relevance of existing calculi for Church’s type theory and their implementations (see Section 4.3). The challenges include
- an appropriate handling of the impredicative nature of Church’s type theory (some form of blind guessing cannot generally be avoided in a complete proof procedure, but must be intelligently guided),
- the elimination/reduction of cut-simulation effects (see Section 3.4) caused by defined equality or cut-strong axioms (e.g., extensionality, description, choice, induction) in the search space,
- the general undecidability of unification, rendering it a rather problematic filter mechanism for controlling proof search,
- the invention of suitable heuristics for traversing the search space,
- the provision of suitable term-orderings and their effective exploitation in term rewriting procedures,
- and efficient data structures in combination with strong technical support for essential operations such λ-conversion, substitution and rewriting.
It is planned that future editions of this article further elaborate on machine-oriented proof calculi for Church’s type theory. For the time being, however, we provide only a selection of historical and more recent references for the interested reader (see also Section 5 below):
- Sequent calculi: Schütte 1960; Takahashi 1970; Takeuti 1987; Mints 1999; Brown 2004, 2007; Benzmüller et al. 2009.
- Mating method: Andrews 1981; Bibel 1981; Bishop 1999.
- Resolution calculi: Andrews 1971; Huet 1973a; Jensen & Pietrzykowski 1976; Benzmüller 1997, 1999a; Benzmüller & Kohlhase 1998a.
- Tableau method: Kohlhase[4] 1995, 1998; Brown & Smolka 2010; Backes & Brown 2011.
- Paramodulation calculi: Benzmüller 1999a,b; Steen 2018, Steen & Benzmüller 2018, 2021.
- Superposition calculi: Bentkamp et al. 2018, Bentkamp et al. 2021, Bentkamp et al. 2023a,c
- Combinator-based superposition calculi: Bhayat & Reger 2020
4.2 Proof Assistants
Early computer systems for proving theorems of Church’s type theory (or extensions of it) include HOL (Gordon 1988; Gordon & Melham 1993), TPS (Andrews et al. 1996; Andrews & Brown 2006), Isabelle (Paulson 1988, 1990), PVS (Owre et al. 1996; Shankar 2001), IMPS (Farmer et al. 1993), HOL Light (Harrison 1996), OMEGA (Siekmann et al. 2006), and λClam (Richardson et al. 1998). Prominent proof assistents that support more powerful dependent type theory include Coq (Bertot & Castéran 2004) and the recent Lean system (de Moura & Ullrich 2021). See Other Internet References section below for links to further info on these and other provers mentioned later.
Most of the early systems mentioned above focused (at least initially) on interactive proof. However, the TPS project in particular had been working since the mid-1980s on the integration of ND-style interactive proof and automated theorem proving using the mating method, and had investigated proof transformation techniques between the two. This research was further intensified in the 1990s, when other projects investigated various solutions such as proof planning and bridges to external theorem provers using so-called hammer tools. The hammers developed at that time were early precursors of todays very successful hammer tools such as Sledgehammer, HolyHammer and related systems (Blanchette et al. 2013, Kaliszyk & Urban 2015 and Czaika & Kaliszyk 2018).
While, triggered by the mentioned dynamics, some first initial progress was made in the late 80s and during the 90s with regards to the automation of Church’s type theory, the resource investments and achievements at the time were lacking much behind those seen, for example, in first-order theorem proving. Good progress was fostered only later, in particular, through the development of a commonly supported syntax for Church’s type theory, called TPTP THF (Sutcliffe & Benzmüller 2010, Sutcliffe 2022), and the inclusion, from 2009 onwards, of a TPTP THF division in the yearly CASC competitions (kind of world championships for automated theorem proving; see Sutcliffe 2016 for further details). These competitions, in combination with further factors, triggered an increasing interest in the full automation of Church’s type theory. Particularly successful was recently the exploration of equality based theorem proving using the superposition approach and the adaptation of SMT techniques to HOL.
4.3 Automated Theorem Provers
A selection of theorem provers for Church's type theory is presented. The focus is on systems that have successfully participated in TPTP THF CASC competitions in the past, and the order of presentation is motivated by their first-time CASC participation. The latest editions of most mentioned systems can be accessed online via the SystemOnTPTP infrastructure (Sutcliffe 2017). Nearly all mentioned systems produce verifiable proof certificates in the TPTP TSTP syntax.
The TPS prover (Andrews et al. 1996, Andrews & Brown 2006) can be used to prove theorems of elementary type theory or extensional type theory automatically, interactively, or semi-automatically. When searching for a proof automatically, TPS first searches for an expansion proof (Miller 1987) or an extensional expansion proof (Brown 2004, 2007) of the theorem. Part of this process involves searching for acceptable matings (Andrews 1981, Bishop 1999). The behavior of TPS is controlled by sets of flags, also called modes. A simple scheduling mechanism is employed in the latest versions of TPS to sequentially run a about fifty modes for a limited amount of time. TPS was the winner of the first THF CASC competition in 2009.
The LEO-II prover (Benzmüller et al. 2015) is the successor of LEO (Benzmüller & Kohlhase 1998b, which was hardwired with the OMEGA proof assistant (LEO stands for Logical Engine of OMEGA). The provers are based on the RUE-resolution calculi developed in Benzmüller 1999a,b. LEO was the first prover to implement calculus rules for extensionality to avoid cut-simulation effects. LEO-II inherits and adapts them, and provides additional calculus rules for description and choice. The prover, which internally collaborates with first-order provers (preferably E) and SAT solvers, has pioneered cooperative higher-order/first-order proof automation. Since the prover is often too weak to find a refutation among the steadily growing set of clauses on its own, some of the clauses in LEO-II’s search space attain a special status: they are first-order clauses modulo the application of an appropriate transformation function. Therefore, LEO-II progressively launches time limited calls with these clauses to a first-order theorem prover, and when the first-order prover reports a refutation, LEO-II also terminates. Parts of these ideas were already implemented in the predecessor LEO. Communication between LEO-II and the cooperating first-order theorem provers uses the TPTP language and standards. LEO-II was the winner of the second THF CASC competition in 2010.
The Satallax prover (Brown 2012) is based on a complete ground tableau calculus for Church’s type theory with choice (Backes & Brown 2011). An initial tableau branch is formed from the assumptions of a conjecture and negation of its conclusion. From that point on, Satallax tries to determine unsatisfiability or satisfiability of this branch. Satallax progressively generates higher-order formulas and corresponding propositional clauses. Satallax uses the SAT solver MiniSat as an engine to test the current set of propositional clauses for unsatisfiability. If the clauses are unsatisfiable, the original branch is unsatisfiable. Satallax provides calculus rules for extensionality, description and choice. If there are no quantifiers at function types, the generation of higher-order formulas and corresponding clauses may terminate. In that case, if MiniSat reports the final set of clauses as satisfiable, then the original set of higher-order formulas is satisfiable (by a standard model in which all types are interpreted as finite sets). Satallax was the winner of the THF CASC competition in 2011 and from 2013 to 2019
The Isabelle/HOL system (Nipkow, Wenzel, & Paulson 2002) has originally been designed as an interactive prover. However, in order to ease user interaction several automatic proof tactics have been added over the years. By appropriately scheduling a subset of these proof tactics, some of which are quite powerful, Isabelle/HOL has since about 2011 been turned also into an automatic theorem prover for TPTP THF (and other TPTP syntax formats), that can be run from a command shell like other provers. The most powerful proof tactics that are scheduled by Isabelle/HOL include the Sledgehammer tool (Blanchette et al. 2013), which invokes a sequence of external first-order and higher-order theorem provers, the model finder Nitpick (Blanchette & Nipkow 2010), the equational reasoner simp, the untyped tableau prover blast, the simplifier and classical reasoners auto, force, and fast, and the best-first search procedure best. In contrast to all other automated theorem provers mentioned above, the TPTP incarnation of Isabelle/HOL does not output proof certificates. Isabelle/HOL was the winner of the THF CASC competition in 2012.
The agsyHOL prover is based on a generic lazy narrowing proof search algorithm. Backtracking is employed and a comparably small search state is maintained. The prover outputs proof terms in sequent style which can be verified in the Agda system.
coqATP implements (the non-inductive) part of the calculus of constructions (Bertot & Castéran 2004). The system outputs proof terms which are accepted as proofs (after the addition of a few definitions) by the Coq proof assistant. The prover employs axioms for functional extensionality, choice, and excluded middle. Boolean extensionality is not supported. In addition to axioms, a small library of basic lemmas is employed.
The Leo-III prover implements a paramodulation calculus for Church’s type theory (Steen 2018). The system, which is a descendant of LEO and LEO-II, provides calculus rules for extensionality, description and choice. The system has put an emphasis on the implementation of an efficient set of underlying data structures, on simplification routines and on heuristic rewriting. In the tradition of its predecessors, Leo-III cooperates with first-order reasoning tools using translations to many-sorted first-order logic. The prover accepts every common TPTP syntax dialect and is thus very widely applicable. Recently, the prover has also been extended to natively supports almost every normal higher-order modal logic (Steen et al. 2023).
CVC4, CVC5 and Verit (Barbosa et al. 2019) are SMT-based automated provers with built-in support for many theories, including linear arithmetic, arrays, bit vectors, data types, finite sets and strings. These provers have been extended to support (fragments of) Church's type theory.
The Zipperposition prover (Bentkamp et al. 2018, Vukmirović et al. 2022) focuses on the effective implementation of superposition calculi for Church's type theory. It comes with Logtk, a supporting library for manipulating terms, formulas, clauses, etc. in Church's type theory, and supports relevant extensions such as datatypes, recursive functions, and arithmetic. Zipperposition was the winner of the THF CASC competition in 2020, 2021 and 2022.
Vampire (Bhayat & Reger 2020), which has dominated the TPTP competitions in first-order logic for more than two decades, now also supports the automation of Church's type theory using a combinator-based superposition calculus. Vampire was the winner of the THF CASC competition in 2023.
The theorem prover E (Vukmirović et al. 2023), another prominent and leading first-order automated theorem prover, has been extended for Church's type theory using a variant of the supperposition approach that is also implemented in Zipperposition.
Lash (Brown and Kaliszyk 2022) is a theorem prover for Church's type theory created as a fork of Satallax.
Duper is a superposition-based theorem prover for dependent type theory, deigned to prove theorems in the proof assistant Lean.
In recent years, there has thus been a significant shift from first-order to higher-order automated theorem proving.
4.4 (Counter-)Model Finding
Support for finding finite models or countermodels for formulas of Church’s type theory was implemented already in the tableau-based prover HOT (Konrad 1998). Restricted (counter-)model finding capabilities are also implemented in the provers Satallax, LEO-II and LEO-III. The most advanced (finite) model finding support is currently realized in the systems Nitpick, Nunchaku and Refute. These tools have been integrated with the Isabelle proof assistant. Nitpick is also available as a standalone tool that accepts TPTP THF syntax. The systems are particularly valuable for exposing errors and misconceptions in problem encodings, and for revealing bugs in the THF theorem provers.
5. Applications
In addition to its elegance, Church's type theory also has many practical advantages. This aspect is emphasised, for example, in the recent textbook by Farmer (2023), which deals with various relevant and important further aspects that could not be covered in this article. Because of its good practical expressiveness, the range of applications of Church's type theory is very wide, and only a few examples can be given below (a good source for further information on practical examples are the proceedings of the conferences on Interactive Theorem Proving).
5.1 Semantics of Natural Language
Church’s type theory plays an important role in the study of the formal semantics of natural language. Pioneering work on this was done by Richard Montague. See his papers “English as a formal language”, “Universal grammar”, and “The proper treatment of quantification in ordinary English”, which are reprinted in Montague 1974. A crucial component of Montague’s analysis of natural language is the definition of a tensed intensional logic (Montague 1974: 256), which is an enhancement of Church’s type theory. Montague Grammar had a huge impact, and has since been developed in many further directions, not least in Typelogical/Categorical Grammar. Further related work on intensional and higher-order modal logic is presented in Gallin 1975 and Muskens 2006.
5.2 Mathematics and Computer Science
Proof assistants based on Church’s Type Theory, including Isabelle/HOL, HOL Light, HOL4, and PVS, have been successfully utilized in a broad range of application in computer science and mathematics.
Applications in computer science include the verification of hardware, software and security protocols. A prominent example is the L4.verified project in which Isabelle/HOL was used to formally prove that the seL4 operating system kernel implements an abstract, mathematical model specifying of what the kernel is supposed to do (Klein et al. 2018).
In mathematics proof assistants have been applied for the development of libraries mathematical theories and the verification of challenge theorems. An early example is the mathematical library that was developed since the eighties in the TPS project. A exemplary list of theorems that were proved automatically with TPS is given in Andrews et al. 1996. A very prominent recent example is Hales Flyspeck in which HOL Light was employed to develop a formal proof for Kepler’s conjecture (Hales et al. 2017). An example that strongly exploits automation support in Isabelle/HOL with Sledgehammer and Nitpick is presented in Benzmüller & Scott 2019. In this work different axiom systems for category theory were explored and compared.
A solid overview of past and ongoing formalization projects can be obtained by consulting appropriate sources such as Isabelle's Archive of Formal Proofs, the Journal of Formalized Reasoning, or the THF entries in Sutcliffe's TPTP problem library. Relevant further information and a discussion of implications and further work can also be found in Bentkamp et al. 2023b and in Bayer et al. 2024.
Further improving proof automation within these proof assistants—based on proof hammering tools or on other forms of prover integration—is relevant for minimizing interaction effort in future applications.
5.3 Computational Metaphysics and Artificial Intelligence
Church’s type theory is a classical logic, but topical applications in philosophy and artificial intelligence often require expressive non-classical logics. In order to support such applications with reasoning tools for Church’s type theory, the shallow semantical embedding technique (see also Section 1.2.2) has been developed that generalizes and extends the ideas underlying the well known standard translation of modal logics to first-order logic. The technique was applied for the assessment of modern variants of the ontological argument with a range of higher-order theorem provers, including LEO-II, Satallax, Nitpick and Isabelle/HOL. In the course of experiments, LEO-II detected an inconsistency in the premises of Gödel’s argument, while the provers succeeded in automatically proving Scott’s emendation of it and to confirm the consistency of the emended premises. More details on this work are presented in a related SEP entry on automated reasoning (see Section 4.6 on Logic and Philosophy). The semantical embedding approach has been adapted and further extended for a range of other non-classical logics and related applications. In philosophy this includes the encoding and formal assessment of ambitious ethical and metaphysical theories, and in artificial intelligence this includes the mechanization of deontic logics and normative reasoning to control AI systems (Benzmüller et al. 2020) as well as an automatic proof of the muddy children puzzle (see Appendix B of dynamic epistemic logic), which is a famous puzzle in epistemic reasoning, respectively dynamic epistemic reasoning.
5.4. Metalogical Studies
Church’s type theory is also well suited to support metalogical studies, including the encoding of other logical formalisms and formalized soundness and completeness studies (see, e.g., Schlichtkrull et al. 2020, Halkjær et al. 2023, and various further pointers given in Benzmüller 2019). In particular, Church's type theory can be studied formally in itself, as shown, for example, by Kumar et al. (2016), Schlichtkrull (2023), and Díaz (2023).
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Acknowledgments
Portions of this material are adapted from Andrews 2002 and Andrews 2001, with permission from the author and Elsevier. Benzmüller received funding for his contribution from Volkswagen Foundation.