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A problem being presented to an automated reasoning program consists of two main items, namely a statement expressing the particular question being asked called the problem's conclusion, and a collection of statements expressing all the relevant information available to the program  the problem's assumptions. Solving a problem means proving the conclusion from the given assumptions by the systematic application of rules of deduction embedded within the reasoning program. The problem solving process ends when one such proof is found, when the program is able to detect the nonexistence of a proof, or when it simply runs out of resources.
A first important consideration in the design of an automated reasoning program is to delineate the class of problems that the program will be required to solve  the problem domain. The domain can be very large, as would be the case for a generalpurpose theorem prover for firstorder logic, or be more restricted in scope as in a specialpurpose theorem prover for Tarski's geometry, or the modal logic K. A typical approach in the design of an automated reasoning program is to provide it first with sufficient logical power (e.g., firstorder logic) and then further demarcate its scope to the particular domain of interest defined by a set of domain axioms. To illustrate, EQP, a theoremproving program for equational logic, was used to solve an open question in Robbins algebra (McCune 1997): Are all Robbins algebras Boolean? For this, the program was provided with the axioms defining a Robbins algebra:
(A1) x + y = y + x (commutativity) (A2) (x + y) + z = x + (y + z) (associativity) (A3) ((x + y) + (x + y)) = x (Robbins equation)
The program was then used to show that a characterization of Boolean algebra that uses Huntington's equation,
(x + y) + (x + y) = x,follows from the axioms. We should remark that this problem is nontrivial since deciding whether a finite set of equations provides a basis for Boolean algebra is undecidable, that is, it does not permit an algorithmic representation; also, the problem was attacked by Robbins, Huntington, Tarski and many of his students with no success. The key step was to establish that all Robbins algebras satisfy
(x)(y)(x + y = x),since it was known that this formula is a sufficient condition for a Robbins algebra to be Boolean. When EQP was supplied with this piece of information, the program provided invaluable assistance by completing the proof automatically.
A specialpurpose theorem prover does not draw its main benefit by restricting its attention to the domain axioms but from the fact that the domain may enjoy particular theoremproving techniques which can be hardwired  coded  within the reasoning program itself and which may result in a more efficient logic implementation. Much of EQP's success at settling the Robbins question can be attributed to its builtin associativecommutative inference mechanisms.
A second important consideration in the building of an automated reasoning program is to decide (1) how problems in its domain will be presented to the reasoning program; (2) how they will actually be represented internally within the program; and, (3) how the solutions found  completed proofs  will be displayed back to the user. There are several formalisms available for this, and the choice is dependent on the problem domain and the underlying deduction calculus used by the reasoning program. The most commonly used formalisms include standard firstorder logic, typed calculus, and clausal logic. We take up clausal logic here and assume that the reader is familiar with the rudiments of firstorder logic; for the typed calculus the reader may want to check Church 1940. Clausal logic is a quantifierfree variation of firstorder logic and has been the most widely used notation within the automated reasoning community. Some definitions are in order: A term is a constant, a variable, or a function whose arguments are themselves terms. For example, a, x, f(x), and h(c,f(z),y) are all terms. A literal is either an atomic formula, e.g. F(x), or the negation of an atomic formula, e.g. ~R(x,f(a)). Two literals are complementary if one is the negation of the other. A clause is a (possibly empty) finite disjunction of literals l_{1} … l_{n} where no literal appears more than once in the clause (that is, clauses can be alternatively treated as sets of literals). Ground terms, ground literals, and ground clauses have no variables. The empty clause, [ ], is the clause having no literals and, hence, is unsatisfiable  false under any interpretation. Some examples: ~R(a,b), and F(a) ~R(f(x),b) F(z) are both examples of clauses but only the former is ground. The general idea is to be able to express a problem's formulation as a set of clauses or, equivalently, as a formula in conjunctive normal form, that is, as a conjunction of clauses.
For formulas already expressed in standard logic notation, there is a systematic twostep procedure for transforming them into conjunctive normal form. The first step consists in reexpressing a formula into a semantically equivalent formula in prenex normal form, (x_{1})…(x_{n})(x_{1},…,x_{n}), consisting of a string of quantifiers (x_{1})…(x_{n}) followed by a quantifierfree expression (x_{1},…,x_{n}) called the matrix. The second step in the transformation first converts the matrix into conjunctive normal form by using wellknown logical equivalences such as DeMorgan's laws, distribution, doublenegation, and others; then, the quantifiers in front of the matrix, which is now in conjunctive normal form, are dropped according to certain rules. In the presence of existential quantifiers, this latter step does not always preserve equivalence and requires the introduction of Skolem functions whose role is to "simulate" the behaviour of existentially quantified variables. For example, applying the skolemizing process to the formula
(x)(y)(z)(u)(v)[R(x,y,v) ~K(x,z,u,v)]
requires the introduction of a oneplace and twoplace Skolem functions, f and g respectively, resulting in the formula
(x)(z)(v) [R(x,f(x),v) ~K(x,z,g(x,z),v)]
The universal quantifiers can then be removed to obtain the final clause, R(x,f(x),v) ~K(x,z,g(x,z), v) in our example. The Skolemizing process may not preserve equivalence but maintains satisfiability, which is enough for clausebased automated reasoning.
Although clausal form provides a more uniform and economical notation  there are no quantifiers and all formulas are disjunctions  it has certain disadvantages. One drawback is the exponential increase in the size of the resulting formula when transformed from standard logic notation into clausal form. The increase in size is accompanied by an increase in cognitive complexity that makes it harder for humans to read proofs written with clauses. Another disadvantage is that the syntactic structure of a formula in standard logic notation can be used to guide the construction of a proof but this information is completely lost in the transformation into clausal form.
A third important consideration in the building of an automated reasoning program is the selection of the actual deduction calculus that will be used by the program to perform its inferences. As indicated before, the choice is highly dependent on the nature of the problem domain and there is a fair range of options available: Generalpurpose theorem proving and problem solving (firstorder logic, simple type theory), program verification (firstorder logic), distributed and concurrent systems (modal and temporal logics), program specification (intuitionistic logic), hardware verification (higherorder logic), logic programming (Horn logic), and so on.
A deduction calculus consists of a set of logical axioms and a collection of deduction rules for deriving new formulas from previously derived formulas. Solving a problem in the program's problem domain then really means establishing a particular formula  the problem's conclusion  from the extended set consisting of the logical axioms, the domain axioms, and the problem assumptions. That is, the program needs to determine if . How the program goes about establishing this semantic fact depends, of course, on the calculus it implements. Some programs may take a very direct route and attempt to establish that by actually constructing a stepbystep proof of from . If successful, this shows of course that derives  proves  , a fact we denote by writing . Other reasoning programs may instead opt for a more indirect approach and try to establish that by showing that {~} is inconsistent which, in turn, is shown by deriving a contradiction, , from the set {~}. Automated systems that implement the former approach include natural deduction systems; the latter approach is used by systems based on resolution, sequent deduction, and matrix connection methods.
Soundness and completeness are two (metatheoretical) properties of a calculus that are particularly important for automated deduction. Soundness states that the rules of the calculus are truthpreserving. For a direct calculus this means that if then . For indirect calculi, soundness means that if {~} then . Completeness in a direct calculus states that if then . For indirect calculi, the completeness property is expressed in terms of refutations since one establishes that by showing the existence of a proof, not of from , but of from {~}. Thus, an indirect calculus is refutation complete if implies {~} . Of the two properties, soundness is the most desirable. An incomplete calculus indicates that there are entailment relations that cannot be established within the calculus. For an automated reasoning program this means, informally, that there are true statements that the program cannot prove. Incompleteness may be an unfortunate affair but lack of soundness is a truly problematic situation since an unsound reasoning program would be able to generate false conclusions from perfectly true information.
It is important to appreciate the difference between a logical calculus and its corresponding implementation in a reasoning program. The implementation of a calculus invariably involves making some modifications to the calculus and this results, strictly speaking, in a new calculus. The most important modification to the original calculus is the “mechanization” of its deduction rules, that is, the specification of the systematic way in which the rules are to be applied. In the process of doing so, one must exercise care to preserve the metatheoretical properties of the original calculus.
Two other metatheoretical properties of importance to automated deduction are decidability and complexity. A calculus is decidable if it admits an algorithmic representation, that is, if there is an algorithm that, for any given and , it can determine in a finite amount of time the answer, “Yes” or “No”, to the question “Does ?” A calculus may be undecidable in which case one needs to determine which decidable fragment to implement. The timespace complexity of a calculus specifies how efficient its algorithmic representation is. Automated reasoning is made the more challenging because many calculi of interest are not decidable and have poor complexity measures forcing researchers to seek tradeoffs between deductive power versus algorithmic efficiency.
Of the many calculi used in the implementation of reasoning programs, the ones based on the resolution principle have been the most popular. Resolution is modeled after the chain rule (of which Modus Ponens is a special case) and essentially states that from p q and ~q r one can infer p r. More formally, let C l denote the clause C with the literal l removed. Assume that C_{1} and C_{2} are ground clauses containing, respectively, a positive literal l_{1} and a negative literal ~l_{2} such that l_{1} and ~l_{2} are complementary. Then, the rule of ground resolution states that, as a result of resolving C_{1} and C_{2}, one can infer (C_{1} l_{1}) (C_{2} ~l_{2}):
C_{1} C_{2}  (ground resolution) (C_{1} l_{1}) (C_{2} ~l_{2})
Herbrand's theorem (Herbrand 1930) assures us that the nonsatisfiability of any set of clauses, ground or not, can be established by using ground resolution. This is a very significant result for automated deduction since it tells us that if a set is not satisfied by any of the infinitely many interpretations, this fact can be determined in finitely many steps. Unfortunately, a direct implementation of ground resolution using Herbrand's theorem requires the generation of a vast number of ground terms making this approach hopelessly inefficient. This issue was effectively addressed by generalizing the ground resolution rule to binary resolution and by introducing the notion of unification (Robinson 1965a). Unification allows resolution proofs to be “lifted” and be conducted at a more general level; clauses only need to be instantiated at the moment where they are to be resolved. Moreover, the clauses resulting from the instantiation process do not have to be ground instances and may still contain variables. The introduction of binary resolution and unification is considered one of the most important developments in the field of automated reasoning.
{x b, y b, z f(a,b)}is a unifier for
R(x,f(a,y)) and R(b,z)since when applied to both expressions makes them equal:
A most general unifier (mgu) produces the most general instance shared by two unifiable expressions. In the previous example, the substitution {x b, y b, z f(a,b)} is a unifier but not an mgu; however, {x b, z f(a,y)} is an mgu. Note that unification attempts to "match" two expressions and this fundamental process has become a central component of most automated deduction programs, resolutionbased and otherwise. Theoryunification is an extension of the unification mechanism that includes builtin inference capabilities. For example, the clauses R(g(a,b),x) and R(g(b,a),d) do not unify but they ACunify, where ACunification is unification with builtin associative and commutative rules such as g(a,b) = g(b,a). Shifting inference capabilities into the unification mechanism adds power but at a price: The existence of an mgu for two unifiable expressions may not be unique (there could actually be infinitely many), and the unification process becomes undecidable in general.
R(x,f(a,y)){x b, y b, z f(a,b)} = R(b,f(a,b)) = R(b,z){x b, y b, z f(a,b)}
by binary resolution; the clause (C_{1} l_{1}) (C_{2} ~l_{2}) is called a binary resolvent of C_{1} and C_{2}.
C_{1} C_{2}  (binary resolution) (C_{1} l_{1}) (C_{2} ~l_{2})
Resolution proofs, more precisely refutations, are constructed by deriving the empty clause [ ] from {~} using resolution; this will always be possible if {~} is unsatisfiable since resolution is refutation complete (Robinson 1965a). As an example of a resolution proof, we show that the set {(x)(P(x) Q(x)), (x)(P(x) R(x)),(x)(Q(x) R(x))}, denoted by , entails the formula (x)R(x). The first step is to find the clausal form of {~(x)R(x)}; the resulting clause set, denoted by S_{0}, is shown in steps 1 to 4 in the refutation below. The refutation is constructed by using a levelsaturation method: Compute all the resolvents of the initial set, S_{0}, add them to the set and repeat the process until the empty clause is derived. (This produces the sequence of increasingly larger sets: S_{0}, S_{1}, S_{2},…) The only constraint that we impose is that we do not resolve the same two clauses more than once.
S_{0} 1 P(x) Q(x) Assumption 2 ~P(x) R(x) Assumption 3 ~Q(x) R(x) Assumption 4 ~R(a) Negation of the conclusion S_{1} 5 Q(x) R(x) Res 1 2 6 P(x) R(x) Res 1 3 7 ~P(a) Res 2 4 8 ~Q(a) Res 3 4 S_{2} 9 Q(a) Res 1 7 10 P(a) Res 1 8 11 R(x) Res 2 6 12 R(x) Res 3 5 13 Q(a) Res 4 5 14 P(a) Res 4 6 15 R(a) Res 5 8 16 R(a) Res 6 7 S_{3} 17 R(a) Res 2 10 18 R(a) Res 2 14 19 R(a) Res 3 9 20 R(a) Res 3 13 21 [ ] Res 4 11
Although the resolution proof is successful in deriving [ ], it has some significant drawbacks. To start with, the refutation is too long as it takes 21 steps to reach the contradiction, [ ]. This is due to the naïve bruteforce nature of the implementation. The approach not only generates too many formulas but some are clearly redundant. Note how R(a) is derived six times; also, R(x) has more “information content” than R(a) and one should keep the former and disregard the latter. Resolution, like all other automated deduction methods, must be supplemented by strategies aimed at improving the efficiency of the deduction process. The above sample derivation has 21 steps but researchtype problems command derivations with thousands or hundreds of thousands of steps.
Instead of removing redundant clauses, some strategies prevent the generation of useless clauses in the first place. The setofsupport strategy (Wos, Carson and Robinson 1965) is one of the most powerful strategies of this kind. A subset T of the set S, where S is initially {~}, is called a set of support of S iff S T is satisfiable. Setofsupport resolution dictates that the resolved clauses are not both from S T. The motivation behind setofsupport is that since the set is usually satisfiable it might be wise not to resolve two clauses from against each other. Hyperresolution (Robinson 1965b) reduces the number of intermediate resolvents by combining several resolution steps into a single inference step.
Independently codiscovered, linear resolution (Loveland 1970, Luckham 1970) always resolves a clause against the most recently derived resolvent. This gives the deduction a simple “linear” structure affording a straightforward implementation; yet, linear resolution preserves refutation completeness. Using linear resolution we can derive the empty clause in the above example in only eight steps:
1 P(x) Q(x) Assumption 2 ~P(x) R(x) Assumption 3 ~Q(x) R(x) Assumption 4 ~R(a) Negation of the conclusion 5 ~P(a) Res 2 4 6 Q(a) Res 1 5 7 R(a) Res 3 6 8 [ ] Res 4 7
With the exception of unrestricted subsumption, all the strategies mentioned so far preserve refutation completeness. Efficiency is an important consideration in automated reasoning and one may sometimes be willing to trade completeness for speed. Unit resolution and input resolution are two such refinements of linear resolution. In the former, one of the resolved clauses is always a literal; in the latter, one of the resolved clauses is always selected from the original set to be refuted. Albeit efficient, neither strategy is complete. Ordering strategies impose some form of partial ordering on the predicate symbols, terms, literals, or clauses occurring in the deduction. Ordered resolution treats clauses not as sets of literals but as sequences  linear orders  of literals. Ordered resolution is extremely efficient but, like unit and input resolution, is not refutation complete. To end, it must be noted that some strategies improve certain aspects of the deduction process at the expense of others. For instance, a strategy may reduce the size of the proof search space at the expense of increasing, say, the length of the shortest refutations.
There are several automated reasoning programs that are based on resolution, or refinements of resolution. Otter is one of the most versatile among these programs and is being used in a growing number of applications (Wos, Overbeek, Lusk and Boyle 1984). Resolution also provides the underlying logicocomputational mechanism for the popular logic programming language Prolog (Clocksin and Mellish 1981).
Hilbertstyle calculi (Hilbert and Ackermann 1928) have been traditionally used to characterize logic systems. These calculi usually consist of a few axiom schemata and a small number of rules that typically include modus ponens and the rule of substitution. Although they meet the required theoretical requisites (soundness, completeness, etc.) the approach at proof construction in these calculi is difficult and does not reflect standard practice. It was Gentzen's goal “to set up a formalism that reflects as accurately as possible the actual logical reasoning involved in mathematical proofs” (Gentzen 1935). To carry out this task, Gentzen analyzed the proofconstruction process and then devised two deduction calculi for classical logic: the natural deduction calculus, NK, and the sequent calculus, LK. (Gentzen actually designed NK first and then introduced LK to pursue metatheoretical investigations). The calculi met his goal to a large extent while at the same time managing to secure soundness and completeness. Both calculi are characterized by a relatively larger number of deduction rules and a simple axiom schema. Of the two calculi, LK is the one that has been most widely used in implementations of automated reasoning programs, and it is the one that we will discuss first; NK will be discussed in the next section.
Although the application of the LK rules affect logic formulas, the rules are seen as manipulating not logic formulas themselves but sequents. Sequents are expressions of the form , where both and are (possibly empty) sets of formulas. is the sequent's antecedent and its succedent. Sequents can be interpreted thus: Let be an interpretation. Then,
satisfies the sequent (written as: ) iffIn other words,
either (for some ) or (for some ).
iff (_{1} & … & _{n}) (_{1} … _{n}), where _{1} & … & _{n} is the iterated conjunction of the formulas in and _{1} … _{n} is the iterated disjunction of those in .If or are empty then they are respectively valid or unsatisfiable. An axiom of LK is a sequent where . Thus, the requirement that the same formula occurs at each side of the sign means that the axioms of LK are valid, for no interpretation can then make all the formulas in true and, simultaneously, make all those in false. LK has two rules per logical connective, plus one extra rule: the cut rule.
Axioms Cut Rule 
, ,, ,

, ,Antecedent Rules () Succedent Rules () & , ,

, && , ,

, &, ,

,, ,

,, ,

,, ,

,, , , ,

,, , , , ,

,~ ,

, ~~ ,

, ~, (a/x)

, (x)(x), (t/x), (x)(x)

, (x)(x), (t/x), (x)(x)

, (x)(x), (a/x)

, (x)(x)
The sequents above a rule's line are called the rule's premises and the sequent below the line is the rule's conclusion. The quantification rules and have an eigenvariable condition that restricts their applicability, namely that a must not occur in , or in the quantified sentence. The purpose of this restriction is to ensure that the choice of parameter, a, used in the substitution process is completely “arbitrary”.
Proofs in LK are represented as trees where each node in the tree is labeled with a sequent, and where the original sequent sits at the root of the tree. The children of a node are the premises of the rule being applied at that node. The leaves of the tree are labeled with axioms. Here is the LKproof of (x)R(x) from the set {(x)(P(x) Q(x)), (x)(P(x) R(x)),(x)(Q(x) R(x))}. In the tree below, stands for this set:
, P(a) P(a), R(a),(x)R(x)  , P(a), R(a) R(a), (x)R(x)  , Q(a) Q(a), R(a), (x)R(x)  , Q(a), R(a) R(a), (x)R(x) 
 , P(a), P(a) R(a) R(a), (x)R(x)  , P(a) R(a), (x)R(x) 
 , Q(a), Q(a) R(a) R(a), (x)R(x)  , Q(a) R(a), (x)R(x) 

 , P(a) Q(a) R(a), (x)R(x)  , R(a), (x)R(x)  (x)R(x) 
In our example, all the leaves in the proof tree are labeled with axioms. This establishes the validity of (x)R(x) and, hence, the fact that (x)R(x). LK takes an indirect approach at proving the conclusion and this is an important difference between LK and NK. While NK constructs an actual proof (of the conclusion from the given assumptions), LK instead constructs a proof that proves the existence of a proof (of the conclusion from the assumptions). For instance, to prove that is entailed by , NK constructs a stepbystep proof of from (assuming that one exists); in contrast, LK first constructs the sequent which then attempts to prove valid by showing that it cannot be made false. This is done by searching for a counterexample that makes (all the sentences in) true and makes false: If the search fails then a counterexample does not exist and the sequent is therefore valid. In this respect, proof trees in LK are actually refutation proofs. Like resolution, LK is refutation complete: If then the sequent has a proof tree.
As it stands, LK is unsuitable for automated deduction and there are some obstacles that must be overcome before it can be efficiently implemented. The reason is, of course, that the statement of the completeness of LK only has to assert, for each entailment relation, the existence of a proof tree but a reasoning program has the more difficult task of actually having to construct one. Some of the main obstacles: First, LK does not specify the order in which the rules must be applied in the construction of a proof tree. Second, and as a particular case of the first problem, the premises in the rules and rules inherit the quantificational formula to which the rule is applied, meaning that the rules can be applied repeatedly to the same formula sending the proof search into an endless loop. Third, LK does not indicate which formula must be selected next in the application of a rule. Fourth, the quantifier rules provide no indication as to what terms or free variables must be used in their deployment. Fifth, and as a particular case of the previous problem, the application of a quantifier rule can lead into an infinitely long tree branch because the proper term to be used in the instantiation never gets chosen. Fortunately, as we will hint at below each of these problems can be successfully addressed.
Axiom sequents in LK are valid, and the conclusion of a rule is valid iff its premises are. This fact allows us to apply the LK rules in either direction, forwards from axioms to conclusion, or backwards from conclusion to axioms. Also, with the exception of the cut rule, all the rules' premises are subformulas of their respective conclusions. For the purposes of automated deduction this is a significant fact and we would want to dispense with the cut rule; fortunately, the cutfree version of LK preserves its refutation completeness (Gentzen 1935). These results provide a strong case for constructing proof trees in a backwards fashion; indeed, by working this way a refutation in cutfree LK gets increasingly simpler as it progresses since subformulas are simpler than their parent formulas. Moreover, and as far as propositional rules go, the new subformulas entered into the tree are completely dictated by the cutfree LK rules. Furthermore, and assuming the proof tree can be brought to completion, branches eventually end up with atoms and the presence of axioms can be quickly determined. Another reason for working backwards is that the truthfunctional fragment of cutfree LK is confluent in the sense that the order in which the nonquantifier rules are applied is irrelevant: If there is a proof, regardless of what you do, you will run into it! To bring the quantifier rules into the picture, things can be arranged so that all rules have a fair chance of being deployed: Apply, as far as possible, all the nonquantifier rules before applying any of the quantifier rules. This takes care of the first and second obstacles, and it is no too difficult to see how the third one would now be handled. The fourth and fifth obstacles can be addressed by requiring that the terms to be used in the substitutions be suitably selected from the Herbrand universe (Herbrand 1930).
The use of sequenttype calculi in automated theorem proving was initiated by efforts to mechanize mathematics (Wang 1960). At the time, resolution captured most of the attention of the automated reasoning community but during the 1970's some researchers started to further investigate nonresolution methods (Bledsoe 1977), prompting a frutiful and sustained effort to develop more humanoriented theorem proving systems (Bledsoe 1975, Nevins 1974). Eventually, sequenttype deduction gained momentum again, particularly in its reincarnation as analytic tableaux (Fitting 1990). The method of deduction used in tableaux is essentially cutfree LK's with sets used in lieu of sequents.
Although LK and NK are both commonly labeled as “natural deduction” systems, it is the latter which better deserves the title due to its more natural, humanlike, approach to proof construction. The rules of NK are typically presented as acting on standard logic formulas in an implicitly understood context, but they are also commonly given in the literature as acting more explicitly on “judgements”, that is, expressions of the form where is a set of formulas and is a formula. This form is typically understood as making the metastatement that there is a proof of from (Kleene 1962). Following Gentzen 1935 and Prawitz 1965 here we take the former approach. The system NK has no logical axioms and provides two introductionelimination rules for each logical connective:
Introduction Rules (I) Elimination Rules (E) &I

&&E _{1} & _{2}

_{i} (for i = 1,2)I _{i} (for i = 1,2)

_{1} _{2}E
[  ]
[  ]

I [  ]

E

I [  ]
[  ]

E _{i} (i = 0,1) _{0} _{1}

_{1i}~I [  ]

~~E [~  ]

I (t/x)

(x)(x)E (x)(x)
[(a/x)  ]

I (a/x)

(x)(x)E (x)(x)

(t/x)
A few remarks: First, the expression [  ] represents the fact that is an auxiliary assumption in the proof of that eventually gets discharged, i.e. discarded. For example, E tells us that if in the process of constructing a proof one has already derived (x)(x) and also with (a/x) as an auxiliary assumption then the inference to is allowed. Second, the eigenparameter, a, in E and I must be foreign to the premises, undischarged  “active”  assumptions, to the rule's conclusion and, in the case of E, to (x)(x). Third, is shorthand for two contradictory formulas, and ~. Finally, NK is complete: If then there is a proof of from using the rules of NK.
As in LK, proofs constructed in NK are represented as trees with the proof's conclusion sitting at the root of the tree, and the problem's assumptions sitting at the leaves. (Proofs are also typically given as sequences of judgements, , running from the top to the bottom of the printed page.) Here is a natural deduction proof tree of (x)R(x) from (x)(P(x) Q(x)), (x)(P(x) R(x)) and (x)(Q(x) R(x)):
(x)(P(x) R(x))

P(a) R(a)(x)(Q(x) R(x))

Q(a) R(a)(x)(P(x) Q(x))

P(a) Q(a)[P(a)  R(a)]

R(a)[Q(a)  R(a)]

R(a)
R(a)

(x)R(x)
As in LK, a forwardchaining strategy for proof construction is not well focused. So, although proofs are read forwards, that is, from leaves to root or, logically speaking, from assumptions to conclusion, that is not the way in which they are typically constructed. A backwardchaining strategy implemented by applying the rules in reverse order is more effective. Many of the obstacles that were discussed above in the implementation of sequent deduction are applicable to natural deduction as well. These issues can be handled in a similar way, but natural deduction introduces some issues of its own. For example, as suggested by the Introduction rule, to prove a goal of the form one could attempt to prove on the assumption that . But note that although the goal does not match the conclusion of any other introduction rule, it matches the conclusion of all elimination rules and the reasoning program would need to consider those routes too. Similarly to forwardchaining, here there is the risk of setting goals that are irrelevant to the proof and that could lead the program astray. To wit: What prevents a program from entering the neverending process of building, say, larger and larger conjunctions? Or, what is there to prevent an uncontrolled chain of backward applications of, say, Elimination? Fortunately, NK enjoys the subformula property in the sense that each formula entering into a natural deduction proof can be restricted to being a subformula of {}, where is the set of auxiliary assumptions made by the ~Elimination rule. By exploiting the subformula property a natural deduction automated theorem prover can drastically reduce its search space and bring the backward application of the elimination rules under control (Portoraro 1998, Sieg and Byrnes 1996). Further gains can be realized if one is willing to restrict the scope of NK's logic to its intuitionistic fragment where every proof has a normal form in the sense that no formula is obtained by an introduction rule and then is eliminated by an elimination rule (Prawitz 1965).
Implementations of automated theorem proving systems using NK deduction have been motivated by the desire to have the program reason with precisely the same proof format and methods employed by the human user. This has been particularly true in the area of education where the student is engaged in the interactive construction of formal proofs in an NKlike calculus working under the guidance of a theorem prover ready to provide assistance when needed (Portoraro 1994, Suppes 1981). Other, researchoriented, theorem provers true to the spirit of NK exist (Pelletier 1998) but are rare.
The name of the matrix connection method (Bibel 1981) is indicative of the way it operates. The term “matrix” refers to the form in which the set of logic formulas expressing the problem is represented; the term “connection” refers to the way the method operates on these formulas. To illustrate the method at work, we will use an example from propositional logic and show that R is entailed by P Q, P R and Q R. This is done by establishing that the formula
(P Q) & (P R) & (Q R) & ~Ris unsatisfiable. To do this, we begin by transforming it into conjunctive normal form:
(P Q) & (~P R) & (~Q R) & ~R
This formula is then represented as a matrix, one conjunct per row and, within a row, one disjunct per column:
P Q ~P R ~Q R ~R
The idea now is to explore all the possible vertical paths running through this matrix. A vertical path is a set of literals selected from each row in the matrix such that each literal comes from a different row. The vertical paths:
Path 1 P, ~P, ~Q and ~R Path 2 P, ~P, R and ~R Path 3 P, R, ~Q and ~R Path 4 P, R, R and ~R Path 5 Q, ~P, ~Q and ~R Path 6 Q, ~P, R and ~R Path 7 Q, R, ~Q and ~R Path 8 Q, R, R and ~R
A path is complementary if it contains two literals which are complementary. For example, Path 2 is complementary since it has both P and ~P but so is Path 6 since it contains both R and ~R. Note that as soon as a path includes two complementary literals there is no point in pursuing the path since it has itself become complementary. This typically allows for a large reduction in the number of paths to be inspected. In any event, all the paths in the above matrix are complementary and this fact establishes the unsatisfiability of the original formula. This is the essence of the matrix connection method. The method can be extended to predicate logic but this demands additional logical apparatus: Skolemnization, variable renaming, quantifier duplication, complementarity of paths via unification, and simultaneous substitution across all matrix paths (Bibel 1981, Andrews 1981). Variations of the method have been implemented in reasoning programs in higherorder logic (Andrews 1981) and nonclassical logics (Wallen 1990).
Equality is an important logical relation whose behavior within automated deduction deserves its own separate treatment. Equational logic and, more generally, term rewriting treat equalitylike equations as rewrite rules, also known as reduction or demodulation rules. An equality statement like f(a)= a allows the simplification of a term like g(c,f(a)) to g(c,a). However, the same equation also has the potential to generate an unboundedly large term: g(c,f(a)), g(c,f(f(a))), g(c,f(f(f(a)))), … . What distinguishes term rewriting from equational logic is that in term rewriting equations are used as unidirectional reduction rules as opposed to equality which works in both directions. Rewrite rules have the form t_{1} t_{2} and the basic idea is to look for terms t occurring in expressions e such that t unifies with t_{1} with unifier so that the occurrence t_{1} in e can be replaced by t_{2}. For example, the rewrite rule x + 0 x allows the rewriting of succ(succ(0) + 0) as succ(succ(0)).
To illustrate the main ideas in term rewriting, let us explore an example involving symbolic differentiation (the example and ensuing discussion are adapted from Chapter 1 of Baader and Nipkow 1998). Let der denote the derivative respect to x, let y be a variable different from x, and let u and v be variables ranging over expressions. We define the rewrite system:
(R1) der(x) 1
(R2) der(y) 0
(R3) der(u + v) der(u) + der(v)
(R4) der(u × v) (u × der(v)) + (der(u) × v)
Again, the symbol indicates that a term matching the lefthand side of a rewrite rule should be replaced by the rule's righthand side. To see the differentiation system at work, let us compute the derivative of x × x respect to x, der(x × x):
der(x × x) (x × der(x)) + (der(x) × x) by R4 (x × 1) + (der(x) × x) by R1 (x × 1) + (1 × x) by R1
At this point, since none of the rules (R1)(R4) applies, no further reduction is possible and the rewriting process ends. The final expression obtained is called a normal form, and its existence motivates the following question: Is there an expression whose reduction process will never terminate when applying the rules (R1)(R4)? Or, more generally: Under what conditions a set of rewrite rules will always stop, for any given expression, at a normal form after finitely many applications of the rules? This fundamental question is called the termination problem of a rewrite system, and we state without proof that the system (R1)(R4) meets the termination condition.
There is the possibility that when reducing an expression, the set of rules of a rewrite system could be applied in more than one way. This is actually the case in the system (R1)(R4) where in the reduction of der(x × x) we could have applied R1 first to the second subexpression in (x × der(x)) + (der(x) × x), as shown below:
der(x × x) (x × der(x)) + (der(x) × x) by R4 (x × der(x)) + (1 × x) by R1 (x × 1) + (1 × x) by R1
Following this alternative course of action, the reduction terminates with the same normal form as in the previous case. This fact, however, should not be taken for granted: A rewriting system is said to be (globally) confluent if and only if independently of the order in which its rules are applied every expression always ends up being reduced to its one and only normal form. It can be shown that (R1)(R4) is confluent and, hence, we are entitled to say: “Compute the derivative of an expression” (as opposed to simply “a” derivative). Adding more rules to a system in an effort to make it more practical can have undesired consequences. For example, if we add the rule
(R5) u + 0 u
to (R1)(R4) then we will be able to further reduce certain expressions but at the price of losing confluency. The following reductions show that der(x + 0) now has two normal forms:
der(x + 0) der(x) + der(0) by R3 1 + der(0) by R1
der(x + 0) der(x) by R5 1 by R1
Adding the rule, (R6) der(0) 0, would allow the further reduction of 1 + der(0) to 1 + 0 and then, by R5, to 1. Although the presence of this new rule actually increases the number of alternative paths  der(x + 0) can now be reduced in four possible ways  they all end up with the same normal form, namely 1. This is no coincidence as it can be shown that (R6) actually restores confluency. This motivates another fundamental question: Under what conditions can a nonconfluent system be made into an equivalent confluent one? The KnuthBendix completion algorithm (Knuth and Bendix 1970) gives a partial answer to this question.
Term rewriting, like any other automated deduction method, needs strategies to direct its application. Rippling (Bundy, Stevens and Harmelen 1993, Basin and Walsh 1996) is a heuristic that has its origins in inductive theoremproving that uses annotations to selectively restrict the rewriting process.
Mathematical induction is a very important technique of theorem proving in mathematics and computer science. Problems stated in terms of objects or structures that involve recursive definitions or some form of repetition invariably require mathematical induction for their solving. In particular, reasoning about the correctness of computer systems requires induction and an automated reasoning program that effectively implements induction will have important applications.
To illustrate the need for mathematical induction, assume that a property is true of the number zero and also that if true of a number then is true of its successor. Then, with our deductive systems, we can deduce that for any given number n, is true of it, (n). But we cannot deduce that is true of all numbers, (x)(x); this inference step requires the rule of mathematical induction:
(0) [(n)  (succ(n))]  (mathematical induction) (x)(x)
In other words, to prove that (x)(x) one proves that (0) is the case, and that (succ(n)) follows from the assumption that (n). The implementation of induction in a reasoning system presents very challenging search control problems. The most important of these is the ability to determine the particular way in which induction will be applied during the proof, that is, finding the appropriate induction schema. Related issues include selecting the proper variable of induction, and recognizing all the possible cases for the base and the inductive steps.
Nqthm (Boyer and Moore 1979) has been one of the most successful implementations of automated inductive theorem proving. In the spirit of Gentzen, Boyer and Moore were interested in how people prove theorems by induction. Their theorem prover is written in the functional programming language Lisp which is also the language in which theorems are represented. For instance, to express the commutativity of addition the user would enter the Lisp expression (EQUAL (PLUS X Y) (PLUS Y X)). Everything defined in the system is a functional term, including its basic “predicates”: T, F, EQUAL X Y, IF X Y Z, AND, NOT, etc. The program operates largely as a black box, that is, the inner working details are hidden from the user; proofs are conducted by rewriting terms that posses recursive definitions, ultimately reducing the conclusion's statement to the T predicate. The BoyerMoore theorem prover has been used to check the proofs of some quite deep theorems (Boyer, Kaufmann, and Moore 1995). Lemma caching, problem statement generalization, and proof planning are techniques particularly useful in inductive theorem proving (Bundy, Harmelen and Hesketh 1991).
Higherorder logic differs from firstorder logic in that quantification over functions and predicates is allowed. The statement “Any two people are related to each other in one way or another” can be legally expressed in higherorder logic as (x)(y)(R)R(x,y) but not in firstorder logic. Higherorder logic is inherently more expressive than firstorder logic and is closer in spirit to actual mathematical reasoning. For example, the notion of set finiteness cannot be expressed as a firstorder concept. Due to its richer expressiveness, it should not come as a surprise that implementing an automated theorem prover for higherorder logic is more challenging than for firstorder logic. This is largely due to the fact that unification in higherorder logic is more complex than in the firstorder case: unifiable terms do not always posess a most general unifier, and higherorder unification is itself undecidable. Finally, given that higherorder logic is incomplete, there are always proofs that will be entirely out of reach for any automated reasoning program.
Methods used to automate firstorder deduction can be adapted to higherorder logic. TPS (Andrews et al. 1996) is a theorem proving system for higherorder logic that uses Church's typed calculus as its logical representation language and is based on a connectiontype deduction mechanism that incorporates Huet's unification algorithm (Huet 1975). As a sample of the capabilities of TPS, the program has proved automatically that a subset of a finite set is finite, the equivalence among several formulations of the Axiom of Choice, and Cantor's Theorem that a set has more subsets than members. The latter was proved by the program by asserting that there is no onto function from individuals to sets of individuals, with the proof proceeding by a diagonal argument. HOL (Gordon and Melham 1993) is another higherorder proof development system primarily used as an aid in the development of hardware and software safetycritical systems. HOL is based on the LCF approach to interactive theorem proving (Gordon, Milner and Wadsworth 1979), and it is built on the strongly typed functional programming language ML. HOL, like TPS, can operate in automatic and interactive mode. Availability of the latter mode is welcomed since the most useful automated reasoning systems may well be those which place an emphasis on interactive theorem proving (Farmer, Guttman and Thayer 1993) and can be used as assistants operating under human guidance. Isabelle (Paulson 1994) is a generic, higherorder, framework for rapid prototyping of deductive systems. Object logics can be formulated within Isabelle's metalogic by using its many syntactic and deductive tools. Isabelle also provides some readymade theorem proving environments, including Isabelle/HOL, Isabelle/ZF and Isabelle/FOL, which can be used as starting points for applications and further development by the user. Isabelle/ZF has been used to prove equivalent formulations of the Axiom of Choice, formulations of the WellOrdering Principle, as well as the key result about cardinal arithmetic that, for any infinite cardinal , = (Paulson and Grabczewski 1996).
Nonclassical logics such as modal logics, intuitionsitic logic, multivalued logics, autoepistemic logics, nonmonotonic reasoning, commonsense and default reasoning, relevant logic, paraconsistent logic, and so on, have been increasingly gaining the attention of the automated reasoning community. One of the reasons has been the natural desire to extend automated deduction techniques to new domains of logic. Another reason has been the need to mechanize nonclassical logics as an attempt to provide a suitable foundation for artificial intelligence. A third reason has been the desire to attack some problems that are combinatorially too large to be handled by paper and pencil. Indeed, some of the work in automated nonclassical logic provides a prime example of automated reasoning programs at work. To illustrate, the Ackerman Constant Problem asks for the number of nonequivalent formulas in the relevant logic R. There are actually 3,088 such formulas (Slaney 1984) and the number was found by “sandwiching” it between a lower and an upper limit, a task that involved constraining a vast universe of 20^{400} 20element models in search of those models that rejected nontheorems in R. It is safe to say that this result would have been impossible to obtain without the assistance of an automated reasoning program.
There have been three basic approaches to automate the solving of problems in nonclassical logic (McRobie 1991). One approach has been, of course, to try to mechanize the nonclassical deductive calculi. Another has been to simply provide an equivalent formulation of the problem in firstorder logic and let a classical theorem prover handle it. A third approach has been to formulate the semantics of the nonclassical logic in a firstorder framework where resolution or connectionmatrix methods would apply.
Modal logic. Modal logics find extensive use in computing science as logics of knowledge and belief, logics of programs, and in the specification of distributed and concurrent systems. Thus, a program that automates reasoning in a modal logic such as K, K4, T, S4, or S5 would have important applications. With the exception of S5, these logics share some of the important metatheoretical results of classical logic, such as cutelimination, and hence cutfree (modal) sequent calculi can be provided for them, along with techniques for their automation. Connection methods (Andrews 1981, Bibel 1981) have played an important role in helping to understand the source of redundancies in the search space induced by these modal sequent calculi and have provided a unifying framework not only for modal logics but also for intuitionistic and classical logic as well (Wallen 1990).
Intutionistic logic. There are different ways in which intuitionsitic logic can be automated. One is to directly implement the intuitionistic versions of Gentzen's sequent and natural deduction calculi, LJ and NJ respectively. This approach inherits the stronger normalization results enjoyed by these calculi allowing for a more compact mechanization than their classical counterparts. Another approach at mechanizing intuitionistic logic is to exploit its semantic similarities with the modal logic S4 and piggy back on an automated implementation of S4. Automating intuitionistic logic has applications in software development since writing a program that meets a specification corresponds to the problem of proving the specification within an intuitionistic logic (MartinLöf 1982). A system that automated the proof construction process would have important applications in algorithm design but also in constructive mathematics. Nuprl (Constable et al. 1986) is a computer system supporting a particular mathematical theory, namely constructive type theory, and whose aim is to provide assistance in the proof development process. The focus is on logicbased tools to support programming and on implementing formal computational mathematics. Over the years the scope of the Nuprl project has expanded from “proofsasprograms” to “systemsastheories”.
Logic programming, particularly represented by the language Prolog (Colmerauer et al. 1973), is probably the most important and widespread application of automated theorem proving. During the early 1970s, it was discovered that logic could be used as a programming language (Kowalski 1974). What distinguishes logic programming from other traditional forms of programming is that logic programs, in order to solve a problem, do not explicitly state how a specific computation is to be performed; instead, a logic program states what the problem is and then delegates the task of actually solving it to an underlying theorem prover. In Prolog, the theorem prover is based on a refinement of resolution known as SLDresolution. SLDresolution is a variation of linear input resolution that incorporates a special rule for selecting the next literal to be resolved upon; SLDresolution also takes into consideration the fact that, in the computer's memory, the literals in a clause are actually ordered, that is, they form a sequence as opposed to a set. A Prolog program consists of clauses stating known facts and rules. For example, the following clauses make some assertions about flight connections:
flight(toronto, london).
flight(london,rome).
flight(chicago,london).
flight(X,Y) : flight(X,Z) , flight(Z,Y).
The clause flight(toronto, london) is a fact while flight(X,Y) : flight(X,Z) , flight(Z,Y) is a rule, written by convention as a reversed conditional (the symbol “:“ means “if”; the comma means “and”; terms starting in uppercase are variables). The former states that there is flight connection between Toronto and London; the latter states that there is a flight between cities X and Y if, for some city Z, there is a flight between X and Z and one between Z and Y. Clauses in Prolog programs are a special type of Horn clauses having precisely one positive literal: Facts are program clauses with no negative literals while rules have at least one negative literal. (Note that in standard clause notation the program rule in the previous example would be written as flight(X,Y) ~flight(X,Z) ~flight(Z,Y).) The specific form of the program rules is to effectively express statements of the form: “If these conditions over here are jointly met then this other fact will follow”. Finally, a goal is a Horn clause with no positive literals. The idea is that, once a Prolog program has been written, we can then try to determine if a new clause , the goal, is entailed by , ; the Prolog prover does this by attempting to derive a contradiction from {~}. We should remark that program facts and rules alone cannot produce a contradiction; a goal must enter into the process. Like input resolution, SLDresolution is not refutation complete for firstorder logic but it is complete for the Horn logic of Prolog programs. The fundamental theorem: If is a Prolog program and is the goal clause then iff {~} [ ] by SLDresolution (Lloyd 1984).
For instance, to find out if there is a flight from Toronto to Rome one asks the Prolog prover to see if the clause flight(toronto, rome) follows from the given program. To do this, the prover adds ~flight(toronto, rome) to the program clauses and attempts to derive the empty clause, [ ], by SLDresolution:
1 flight(toronto,london) Program clause 2 flight(london,rome) Program clause 3 flight(chicago,london) Program clause 4 flight(X,Y) ~flight(X,Z) ~flight(Z,Y) Program clause 5 ~flight(toronto,rome) Negation of the conclusion 6 ~flight(toronto,Z) ~flight(Z,rome) Res 5 4 7 ~flight(london,rome) Res 6 1 8 [ ] Res 7 2
The conditional form of rules in Prolog programs adds to their readability and also allows reasoning about the underlying refutations in a more friendly way: To prove that there is a flight between Toronto and Rome, flight(toronto,rome), unify this clause with the consequent flight(X,Y) of the fourth clause in the program which itself becomes provable if both flight(toronto,Z) and flight(Z,rome) can be proved. This can be seen to be the case under the substitution {Z london} since both flight(toronto,london) and flight(london,rome) are themselves provable. Note that the theorem prover not only establishes that there is a flight between Toronto and Rome but it can also come up with an actual itinerary, TorontoLondonRome, by extracting it from the unifications used in the proof.
There are at least two broad problems that Prolog must address in order to achieve the ideal of a logic programming language. Logic programs consist of facts and rules describing what is true; anything that is not provable from a program is deemed to be false. In regards to our previous example, flight(toronto, boston) is not true since this literal cannot be deduced from the program. The identification of falsity with nonprovability is further exploited in most Prolog implementations by incorporating an operator, not, that allows programmers to explicitly express the negation of literals (or even subclauses) within a program. By definition, not l succeeds if the literal l itself fails to be deduced. This mechanism, known as negationbyfailure, has been the target of criticism. Negationbyfailure does not fully capture the standard notion of negation and there are significant logical differences between the two. Standard logic, including Horn logic, is monotonic which means that enlarging an axiom set by adding new axioms simply enlarges the set of theorems derivable from it; negationbyfailure, however, is nonmonotonic and the addition of new program clauses to an existing Prolog program may cause some goals to cease from being theorems. A second issue is the control problem. Currently, programmers need to provide a fair amount of control information if a program is to achieve acceptable levels of efficiency. For example, a programmer must be careful with the order in which the clauses are listed within a program, or how the literals are ordered within a clause. Failure to do a proper job can result in an inefficient or, worse, nonterminating program. Programmers must also embed hints within the program clauses to prevent the prover from revisiting certain paths in the search space (by using the cut operator) or to prune them altogether (by using fail). Last but not least, in order to improve their efficiency, many implementations of Prolog do not implement unification fully and bypass a timeconsuming yet critical test  the socalled occurscheck  responsible for checking the suitability of the unifiers being computed. This results in an unsound calculus and may cause a goal to be entailed by a Prolog program (from a computational point of view) when in fact it should not (from a logical point of view).
There are variations of Prolog intended to extend its scope. By implementing a model elimination procedure, the Prolog Technology Theorem Prover (PPTP) (Stickel 1992) extends Prolog into full firstorder logic. The implementation achieves both soundness and completeness. Moving beyond firstorder logic, Prolog (Miller and Nadathur 1988) bases the language on higherorder constructive logic.
Automated reasoning is a growing field that provides a healthy interplay between basic research and application. Automated deduction is being conducted using a multiplicity of theoremproving methods, including resolution, sequent calculi, natural deduction, matrix connection methods, term rewriting, mathematical induction, and others. These methods are implemented using a variety of logic formalisms such as firstorder logic, type theory and higherorder logic, clause and Horn logic, nonclassical logics, and so on. Automated reasoning programs are being applied to solve a growing number of problems in formal logic, mathematics and computer science, logic programming, software and hardware verification, circuit design, and many others. One of the results of this variety of formalisms and automated deduction methods has been the proliferation of a large number of theorem proving programs. To test the capabilities of these different programs, selections of problems has been proposed against which their performance can be measured (McCharen, Overbeek and Wos 1976, Pelletier 1986). The TPTP (Sutcliffe and Suttner 1998) is a library of such problems that is updated on a regular basis. There is also a competition among automated theorem provers held regularly at the CADE conference; the problems for the competition are selected from the TPTP library.
Initially, computers were used to aid scientists with their complex and often tedious numerical calculations. The power of the machines was then extended from the numeric into the symbolic domain where infiniteprecision computations performed by computer algebra programs have become an everyday affair. The goal of automated reasoning has been to further extend the machine's reach into the realm of deduction where they can be used as reasoning assistants in helping their users establish truth through proof.
First published: July 18, 2001
Content last modified: July 18, 2001