The Hidden Logic of Sudoku


Denis Berthier



book cover


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The concept of a resolution rule




© Denis Berthier. All the material in this page and the pages it gives access to are the property of the author and may not be re-published or re-posted without his prior written permission.


1) INTRODUCTION: GENERAL FRAMEWORK


The mathematical backgound is fisrt order logic (FOL). For a logic game, that should not be too surprising.
As there are other approaches to sudoku solving, there can exist different frameworks (graph theory and colouring problems, exact cover sets, ...) but I think FOL is the most natural one, the one closest to what players are used to do. This was already the inspiring idea in the first edition of my book and it has never changed: keeping the formal concepts as close as possible to the natural ones.

In this framework, there is a very limited number of basic concepts: cell, row, column, block, number, value, candidate. They should not sound too unfamiliar.
All the other concepts are defined from the above basic ones: bivalue, bilocation, naked pair, hidden pair, swordfish, ...., nrc-link, chain, many specific types of chains, .... This allows to give very clear and non ambiguous definitions (e.g. a degenerated quad consisting of 2 pairs is not called a quad).
A major aspect of the framework is that all the concepts used are purely factual: they describe a situation on a grid in terms of what can be observed. Patterns are observables, they are not defined in terms of what wants to do with them (as is the case with views centred on a supposed "inference level").


Axioms: there are only the four axioms that have always been the Sudoku axioms everywhere in the world since the beginning of Sudoku and that remain the constraints you can see in any newspaper or any puzzles book: only one value in each cell, only one occurrence of each number in each row, column, block.
These axioms satisfy symmetry and super-symmetry relations that can be exploited in the definition of the resolution rules.
Symmetry can also be exploited if one chooses to use the extended Sudoku board I have designed with the four rc, rn, cn and bn spaces.

Adopting rules based on the assumption of uniqueness is an option. For a player, uniqueness can only be an assumption: he has no control on it. Either he accepts this oracle or he doens't.



2) REMARKS ON FIRST ORDER LOGIC (FOL)


First order logic (FOL), also called "predicate calculus", is the background of the framework described here. More precisely, I use a notational variant of FOL, MS-FOL (Multi-Sorted First Order Logic).
If you don't know anything about it, an elementary reference is:  http://en.wikipedia.org/wiki/First-order_logic

First order logic should not be confused with common sense logic. One thing you should keep in mind is that if something is not illogical in the commonsense of this word, it doesn't entail it can be expressed in FOL.
The reason is that FOL has a strict formalism.
A first order theory is restricted to a well defined topic, which you may discuss only in a well defined language specifically defined for and adapted to this topic.
In particular, it doesn't include other mathematical theories, such as number theory or set theory, and FOL doesn't allow arbitrary mathematical operations.

As Algebra, Differential Geometry or lots of other topics, Mathematical Logic is a specialised field of mathematics. Most of the mathematicians are very specialised in their own field and they don't know as much about other fields. Moreover, generally, they don't know much about FOL.
The reason is that they work within the framework of "naive set theory", where they can build sets of subsets, sets of functions and so on, they can use quantifiers on functions, subsets, subsets of subsets and so on, and they don't want to be restricted in these possibilities.
But FOL doesn't allow such constructions.

FOL appears naturally as the proper formalism for Sudoku because most players are looking for solutions using only "pure logic". FOL provides the kind of restrictions able to give a precise meaning to the idea that a solution has to be obtained by "pure logic".



3) RESOLUTION RULES AND RESOLUTION PATHS


Formal definition: A resolution rule is a formula of first order logic (MS-FOL - or typed predicate calculus), in language of Sudoku, that satisfies the two conditions:
- it is a logical consequence of the Sudoku axioms,
- it is written in the"condition => action" form, where "action" can only be the assertion of value(s) and the negation of candidate(s) in some cells associated with the pattern (the target cells).
The condition is also called the pattern of the rule.
Quantifiers can appear in the rule. Any variable appearing in the action part must already appear in the condition part.

The language of Sudoku is built on a very limited number of concepts with immediate intuitive meaning: row, column, block, number.
It has a very restricted number of primary predicates and associated atomic formulæ:
n1 = n2
r1 = r2
c1 = c2
b1 = b2
value(n, r, c)
candidate(n, r, c)
where n, r, c,… are variables (and = is the usual equality sign).

This is a natural modelling choice, justified by the fact that they constitute the minimum common to all players.
According to Occam's razor principle, any addition to these basic predicates should be justified by some necessity.

Auxiliary predicates can of course be defined. Formally, they are just formulæ with free variables.
For instance, share-a-unit(r1, c1, b1, r2, c2, b2) is defined as: r1 = r2 or c1 = c2 or b1 = b2
Other predicates that can be easily defined are:
- bivalue
- bilocation (or conjugate)
- nrc-linked
- nrc-conjugate,…

If the above definition, the condition part (which is also a FOL formula in itself) describes a possible situation on a grid, e.g. a Naked-Triplets, an xy4-chain, an nrczt5-chain, … Patterns describe purely factual situations, they are the observables of Sudoku.


A resolution rule is proven once and for all, and the way it has been proven, by a valid logical method, has no impact of any kind on its validity.
In particular, whether or not reasoning by cases has been used in the proof is totally irrelevant to its validity.
Being logically valid doesn't prevent a rule from being more or less easy to apply, but this has nothing to do with the way it has been proven. The problems
- of proving a rule once and for all (generally these proofs are very straightforward)
- and of spotting the occurences (instantiations) of its defining pattern on a real grid
are totally independent.


Definition: A resolution path is a sequence of applications of resolution rules, starting from a given puzzle and ending, hopefully, in a solution (or in a sate where no rule can be applied) - with no additional ad hoc pieces of reasoning. A resolution path is thus a logical, constructive proof of the solution.
Unfortunately, examples given in forums very rarely satisfy this definition, although they often could.
This is mainly due to:
- ambiguous examples with incomplete resolution paths and inadapted notations,
- a generalised confusion between the description of a purely factual situation (e.g. the existence of a general link in my sense, i.e. a unit being shared between two cells, or of aconjugacy link) and the way this can be used in an inference step. In this regard, the notions of strong and weak links are certainly the most confusing ones that can have been introduced - leading e.g. to hyper-realistic debates on whether a strong link is also a weak link.

Definition: a resolution theory is a set of resolution rules.

Definition: a puzzle is solvable by a resolution theory T if there is a resolution path using only rules in T and leading to a solution.


Remarks.
The above definitions don't have to be understood in an integrist sense - which doesn't mean they must be relaxed:

1) A resolution rule can be written formally in MS-FOL, but it can also be written in plain English. In this case, to be sure there is a real unambiguously defind resolution rule behind it, the only three things you have to be careful about are:
- respect the "condition => action" form;
- in the condition part, or when you define new auxiliary predicates from the elementary ones (e.g. a new type of chain), use only boolean combinations and quantifications; always use only precise factual conditions (and not undefined concepts such as "weak" or "strong" link);
- in the action part, put only assertion of values and deletion of candidates; there can be no "or".

2) Logical formulæ can use auxiliary predicates, which can be given any convenient intuitive form.
The graphical representations I use for chains ARE logical formulæ - and they are very short.

As an elementary example, here is the formula for an xy-chain[4]:
 xy-chain[4]  r1c1{1 2} - r1c4{2 3} - r3c5{3 4} - r3c2{4 1}


3) Basic AICs or NLs (what I call nrc-chains) are resolution rules.
Of course, xy, xyt, xyzt, xhy, hxyt, hxyzt, nrc, nrct, nrcz and nrczt chains are resolution rules.

4) As soon as you consider that you can shift the rows or columns, you have variables and you are already doing FOL (and not propositional calculus). There is no difference between Math and Logic in the use of variables. A variable in a formula is the same thing as a variable in a polynomial.

5) There is no reason to broaden the definition of a resolution rule (e.g. by accepting "or statements" in the action part; that would radically change the framework).
The problem is not to extend the definition but to find additional, not necessarily formal, conditions a valid resolution rule should satisfy in order to be useful in practice. See e.g. the next section about chains.

6) Due to the natural elementary symmetries of Sudoku, this is mere common sense, but it may be useful to state it once formally: the statement of a resolution rule should rely on no particular row, column, block or number. Practical statement: the formula expressing a chain rule should only have symbols for variables, not for constants.



4) A FEW OBJECTIVE PROPERTIES OF CHAINS


The definitions below will be clearer if you have first a quick look on chains or whips.

Warning: chains should not be confused with chain rules. A chain rule is merely valid or not valid, which depends neither on the way it has been proven nor on the properties of the underlying chain that will be defined below. There's nevertheless an obvious (syntactic) property of chain rules that should be respected. Due to the natural elementary symmetries of Sudoku, this is mere common sense, but it may be useful to state it once formally: the statement of a chain rule should rely on no particular row, column, block or number. Practical statement: the formula expressing a chain rule should only have symbols for variables, not for constants.

A non valid chain rule is merely useless. But, independently of its logical validity, a valid chain rule can be more a less useful, more or less easy to apply, more or less acceptable. As these are purely subjective criteria, they can only lead to confusion or wars of words.

Here, I shall be concerned only with purely objective properties of chains (and not of chain rules) that may be relevant to estimate their usefulness.
Notice that these objective properties can be the subject of much debate when it comes to subjectively evaluating their impact on usefulness or acceptability.


4.1 Reversibility

Definitions:
- given a (2D or 3D) chain C, the reversed chain is the chain obtained by reversing the order of the candidates; in the process, left (resp. right) linking candidates become right (resp. left) linking candidates.
- a chain type is called reversible if for any chain of this type, the reversed chain is of this type.

Theorem: xy-chains, xyz-chains, nrc-chains, nrcz-chains are reversible.
Proof: obvious.



4.2 Non-anticipativeness

Definition: a given type of (2D or 3D) chain is called non-anticipative if, when you build a chain of this type from left to right, all that you have to check to add the next candidate depends only on the previous candidates (and not on the potential future ones).

Comments:
- this is a very strong criterion for acceptability, from the points of view of both human players and programmers;
- non-anticipativeness could also be called no-look-ahead;
- the definition does not imply that adding a candidate will always allow you to finally get a complete chain, but it guarantees that, up to the new candidate, the partial chain is of the given type.

Theorem: all the (h)xy(z)(t) and nrc(z)(t) chains/lassos/whips are non-anticipative.
Proof: obvious. Indeed they were defined so as to be non-anticipative.

Theorem: any reversible chain is non-anticipative.
Proof: obvious


4.3 Extendability

Definition: a given type of (2D or 3D) chain is called left-extendable if, when you already have a partial chain of this type, you can add cells or candidates or another partial chain of the same type not only to its right but also to its left (of course, respecting the linking conditions at the junction).

Theorem: (h)xy(z)(t)-chains and nrc(z)(t)-chains are left-extendable.
Proof: obvious. Indeed they were defined so as to be non-anticipative.

Theorem: any reversible chain is left-extendable.
Proof: obvious

Theorem: any non-anticipative chain is left-extendable.
Proof: obvious


4.3 Composability

Definition: a given type of (2D or 3D) chain is called composable if, when two partial chains of this type are given, they can be combined into a single chain of this type (of course, respecting the linking conditions on left- and right- linking candidates for chains of this type at the junction and having the same target in case they are built around a target).

Theorem: all the (h)xy(z)(t) and nrc(z)(t) chains are composable.


This is a very important property of chains. It can be used to build long chains from shorter ones.
Its practical impact is mainly for chains with the t-extension: when additional t-candidates are justified by previous right-linking candidates of a partial chain, they will still be justified by the same candidates if another partial chain of the same type is added to its left. Of course, not all chains with the t-extension can be obtained by combining shorter chains of the same type, but looking first for chains with short distance t-interactions may be a valuable strategy.



5) CONFLUENCE, A MAJOR PROPERTY OF RESOLUTION THEORIES


This section is now in a separate page.

Theorem: Braid resolution theories have the confluence property. Braids are defined here and the confluence property of their resolution theories is proven here.




6) RESOLUTION RULE vs RESOLUTION TECHNIQUE vs REPRESENTATION TECHNIQUE



Whereas I have given the phrase "resolution rule" a precise, purely logical definition, it is not yet the case for the word "technique". First, I think two different kinds of techniques should be defined.

A "resolution technique" is a procedure that can help get closer to a solution by eliminating candidates or asserting values.

Some colouring techniques, depending on what one means by this, can be resolution techniques in this sense.

A "representation technique" is a representation that can be the support for several resolution techniques.
Marking conjugate candidates with upper and lower case letters is a technique in this sense. It can be used for finding Nice Loops or AICs.
The Extended Sudoku Board introduced in HLS, with its 2D-spaces (rc-, rn-, cn- and bn- spaces), is a representation techniques in this sense.


One important notion is that of a resolution technique being associated with or being the implementation of a resolution rule.
A given resolution rule can, in practice, be applied in various ways. Said otherwise, different resolution techniques can sometimes be attached to a single resolution rule
This distinction is important. Forgetting it may lead to the confusion between an algorithm and a logical formula - two notions that do not belong to the same universe of discourse (Computer Science vs Logic).


A formal definition is the following:

A resolution technique is the implementation of a resolution rule if for any partially filled grid with candidates, applying the technique to this grid has the same final effect (values asserted and candidates deleted) as what can be logically concluded by applying the resolution rule.


Validity of a resolution rule is based on logic. Validity of a resolution technique is based either on its conformity with the underlying resolution rule if there is one OR on the algorithm describing it.
For a technique that is not based on a resolution rule, it may be very difficult to guarantee that it doesn't amount to a form of trial and error.

Most, but not all, of the usual techniques could be re-written as resolution rules.



7) RECURSIVE-TRIAL-AND-ERROR-WITH GUESSING (RTEG)


Definition: RTEG is the any structured search procedure (e.g. depth-first or breadth-first): recursively make ad hoc hypotheses on values that you do not know to be true at the time you make them, write all their consequences (candidates eliminated and values added), with the possibility of retracting each of these hypotheses and their consequences if they lead to a contradiction; accept the solution if one is found (this is the "guessing" part of this procedure).
RTEG is a resolution technique in the above sense.
In fact, RTEG is a family of resolution techniques, depending on how you write "all the consequences" of an hypothesis, i.e. on which resolution theory T (i.e. on which family T of resolution rules) you adopt for this. This is not really important, because such consequences are only used to prune the tree of possibilities, i.e. to restrict the search.
There may also be variants of RTEG depending on how many solutions you want in case the puzzle has several: this will be defined by the exit conditions of the algorithm.

Being an algorithm, Recursive-Trial-and-Error-with-guessing can obviously not be a resolution rule: the two pertain to different domains of speech (algorithmic vs logic). But we have the stronger:

RTEG theorem: RTEG is a resolution technique that cannot be the implementation of any resolution rule.

Proof: since RTEG can be applied any time a cell has at least two candidates, if it was expressible as a resolution rule, the condition of this rule could only be:
"if a cell has at least two candidates".
But it is obvious that, from such a condition, no general conclusion can be obtained (no value can be asserted and no candidate can be eliminated).


Here is a stronger version and an alternative proof.

RTEG theorem (stronger version): RTEG is a resolution technique that cannot be the implementation of any set of resolution rules.

Proof: if a puzzle has more than one solution, RTEG is guaranteed to find one (or several or all, depending on the exit condition we put on the RTEG algorithm - again, RTEG is a family of algorithms, and the theorem applies to any variant).
On the contrary, as a set S of resolution rules can only lead to conclusions that are logical consequences of the axioms and the entries (i.e. that are true in all its models - all the solutions), if a puzzle has several solutions, S cannot find any.
E.g. if there are two solutions such that r1c1 is 1 in the first and 2 in the second, S cannot prove that r1c1=1 (nor that r1c1=2). It can therefore find none of these solutions.
q.e.d.

Remark: Notice that another procedure (non-recursive Trial-and-Error with no guessing) corresponding to the standard notion of T&E is defined here and shown to be representable by braids.



8) FAMILIES OF RESOLUTION RULES


First, let me state that I am only speaking here of resolution rules and not of resolution techniques.
Moreover, I am considering only rules that can be considered as first order, in conformance with my general framework, i.e. whose logical formulation does not require quantifiers on subsets (such as the cover set approach).

Resolution rules can be classified into various large families. This classification is not exhaustive, new families might be discovered. But it is useful to understand that there are several very different types.

In my book and in SudoRules, I have mainly been considering only four large families of resolution rules:
1) the elementary constraints propagation rules or ECP (direct contradictions along rows, columns and blocks),
2) the subset rules: Naked, Hidden and Super-Hidden (i.e. Fish) versions of Singles, Pairs, Triplets, Quads,
3) the elementary interaction rules (between rows and blocks and between columns and blocks): BI,
4) the xy-to-nrczt (xy, hxy, xyt, hxyt, xyz, hxyz, xyzt, hxyzt, nrc, nrct, nrcz, nrczt) family of chain rules.

I have shown the unity of the subset rules through symmetry and super-symmetry.
I have also shown why all the rules in the xy-to-nrczt family can be considered as generalisations of the basic xy-chain rule. This includes nrc-chains, equivalent to the basic NLs or AICs (basic meaning "with no ALSs"). Although nrczt-chains subsume the whole family, the other chains are easier to find and should therefore not be forgotten. This family is thus organised in a pedagogical hierarchy.

In my book,  I have also mentionned that the subset rules are extensions of the xy2 to xy4 patterns.
Moreover, most (but not all) cases of the subset patterns are special cases of nrczt-chains (see the subsumption page)
This shows that families 2 and 4 are very closely related.

What I want to stress now is that, in spite of being so close, families 2 and 4 require very different forms of reasoning:
- the standard proofs of the subset rules require considering the pattern globally; they don't proceed from one cell to the next;
- the proofs of the xy-to-nrczt rules follow the linear chain structure in such a way that each step doesn't anticipate on the future elements of the chain; I consider this non-anticipativeness as a key property of chains; it is what makes them useable in practice.


As they correspond to different forms of reasoning, the subsets and the nrczt-chains could be subsumed under a common pattern only if we accepted chains that anticipe on their future elements (which is an abomination from the point of view of information theory) OR if we define a new type of pattern (see the next section).


The rules in the above four families are enough to solve almost any randomly generated puzzle (indeed, in several tens of thousands of randomly generated puzzles, I have met none that they could not solve).
From experiments with hundreds of puzzles taken from various forums, it appears that they are enough to solve puzzles upto SER = 9.3. In particular, they were used daily by human players on the French sudoku-factory.com forum (before it suddenly disappeared) to solve puzzles at SER 9.0 to 9.3. They can solve any puzzle that any expert human player can solve and probably even much more.
All this shows that, in spite of the simplicity of the general framework, it allows to define simple and very powerful resolution rules.

It is important to recall this, because the next section will discuss more complex patterns and I want to make it clear that such patterns, especially braids, are not necessary but for exceptionally hard puzzles.



9) MORE COMPLEX FAMILIES OF RESOLUTION RULES



Considering the current interest for exceptional puzzles, I have extended my set of resolution rules in two directions.

1) Firstly, I have generalised my idea of additional z- and t- candidates and I have introduced a very general principle, zt-ing, that allows to define new patterns and associated new chain rules from any family FP of basic patterns: zt-whip(FP) and zt-braid(FP). zt-whipping or zt-braiding is a general method (more general than the classical almost-ing) for including in chains/whips/braids any pattern having an associated resolution rule.
In essence, the generalisation to zt-whip(FP) consists of allowing the right-linking candidates of a whip to be whole patterns instead of mere candidates.

If one takes FP=ECP+NS+HS, one gets the ordinary nrczt-whips.
If one takes FP=ECP+NS+HS+BI, one gets the grouped or hinged zt-whips.
If one takes FP = ECP+NS+HS+BI+SubsetRules, one gets a new family of chain patterns, whip(ECP+NS+HS+BI+SubsetRules), more general than nrczt-chains, AICs with ALSs and than their grouped or hinged counterparts. Indeed, these whips contain any AAAALS, AHHHHS and any AAAAAA-Fish, with as many A's or H's as one wants.
These patterns are chains, in exactly the same sense as any nrczt-chain or any AIC. They can be considered as defining a new set of levels in the hierarchy defined by family 4.
Such generalisations of nrczt-chains have also already been used in the late sudoku-factory forum - although in these puzzles ordinary nrczt-whips were enough.


2) Secondly, with the introduction of nrczt-braids, I have added a fifth family, composed of a (relatively mild) kind of nets, still with some linear structure. See the page on braids.


3) The above two extensions can be combined, leading to zt-braids(FP) for any family of patterns with associated resolution rules.
I have also shown the close relationship between braids and Trial-and-Error (T&E) - the standard T&E procedure (which is not itself a resolution rule but only a "resolution technique") with no guessing and no recursion, not to be confused with the RTE procedure:
Theorem: for any resolution theory T based on a family FP of elementary patterns, any elimination that can be done by T&E(T) can be done by some zt-braid(FP). See the page on braids.
For players who use T&E for hard puzzles, this should soften their pain when they read that T&E is the abomination. The only thing that remains the abomination in this framework is guessing.

All the above patterns still have two very important properties, helpful for finding them:
- they are non-anticipative, i.e. the validity of a partial whip/braid depends only on the previous candidates (and it can therefore be checked on-the-fly),
- they are composable, i.e. partial whips/braids can be linkd together to make longer ones, with obvious compatibility conditions at the junction.



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