Categorical models of logics

A few days ago I wrote about topological models of modal logic. If you find general topology more familiar than modal logic, this correspondence could be helpful. If you think the motivation for general topology is evident but the motivation for modal logic is suspect, the correspondence between the two fields might relieve some of your suspicion of the latter.

Topological models, however, are quite limited. You can’t model provability logic, for example, by the usual approach to constructing topological models, though there is an alternative construction.

Categorical models are less limited. You can model a wide variety of logics using categories via the Curry-Howard-Lambek correspondence. This may be cold comfort if you find category theory and logic equally mysterious. Even so, there’s something intriguing about a correspondence between two things you find mysterious.

Categorical semantics is deep and broad, but I’ll keep this post brief. The correspondence between logic [1] and categories [2] is summarized in the table below.

\begin{tabular}{ll} \hline \textbf{Logic} & \textbf{Category theory} \\ \hline Proposition / formula & Object \\ Proof of \(A\) from \(\Gamma\) & Morphism \(\Gamma\to A\) \\ Truth \(\top\) & Terminal object \(1\) \\ Falsehood \(\bot\) & Initial object \(0\) \\ Conjunction \(A\land B\) & Product \(A\times B\) \\ Disjunction \(A\lor B\) & Coproduct \(A+B\) \\ Implication \(A\to B\) & Exponential \(B^A\) \\ Negation \(\neg A\) & Exponential \(0^A\) \\ \hline \end{tabular}

It’s interesting how model logic fits into this picture. Axiom K, required by normal modal logics, says

\Box(A\to B)\;\longrightarrow\;(\Box A\to\Box B)

In categorial semantics, this says this says the box operator □ is a functor, mapping objects representing propositions to objects representing propositions, and mapping morphisms representing proofs to mapping morphisms representing proofs.

Related posts

[1] More precisely, intuitionistic propositional calculus.

[2] More precisely, bicartesian closed categories.

Privacy policies and modal logic

If you’ve followed this blog for a while, you may know that I do a lot of work with data privacy and that I have an interest in modal logic. Recently these two worlds collided: I became aware of work that uses modal logic to reason about data privacy.

The story begins with Helen Nissenbaum’s paper Privacy as Contextual Integrity [1]. Rather than simply classifying data as public or private, Nissenbaum looks at norms around the context in which data exists and moves.

… the benchmark of privacy is contextual integrity; that in any given situation, a complaint that privacy has been violated is sound in the event that one or the other types of the informational norms has been transgressed.

A couple years later Nissenbaum coauthored a paper [2] with three Stanford computer scientists using modal logic, specifically linear temporal logic (LTL), to reason about contextual integrity. The paper uses four modal operators: the usual box and diamond, plus past tense versions with a minus sign across the middle.

box, diamond, box minus, diamond minus

These four operators can be read as “henceforth”, “eventually”, “historically”, and “once.”

Henceforth means something will always be true into the future; historically means something was always true in the past.

Eventually means something will happen at some time in the future; once means something occurred at some time in the past.

You could imagine using these operators to formalize statements such as data sharing in some context is permissible (henceforth) if the data subject has (once) signed a consent form.

Understanding simple stand-alone policies does not require the machinery of formal logic, but analyzing large collections of interacting policies may. It may be, for example, that a collection of policies cannot all be satisfied, though this is not obvious from looking at the individual policies. Formalizing policies in the language modal logic makes their analysis amenable to well established algorithms.

Related posts

[1] H. Nissenbaum. Privacy as contextual integrity. Washington Law Review, 79(1):119–158, 2004.

[2] A. Barth, A. Datta, J. C. Mitchell, and H. Nissenbaum, “Privacy and contextual integrity: Framework and applications,” in Proc. 2006 IEEE Symp. Security and Privacy (S&P’06), Berkeley/Oakland, CA, USA, 2006, pp. 184–198, doi: 10.1109/SP.2006.32.

Modal logic and topology

You can’t say much about modal logic in general. You have to be more specific to get anywhere. You have to choose some axioms. Ideally the axioms you need for your application correspond to a named set of axioms that has been studied before.

The situation is similar in point-set topology. You can’t say very much about a general topological space. You have to specify some separation axioms to get going.

Bare bones

Modal logic

A modal logic is any set of formulas in the modal language that:

  1. contains all propositional tautologies,
  2. is closed under modus ponens, and
  3. is closed under uniform substitution.

In particular, this definition requires nothing of the modal operator □ (“box”). You just have propositional logic with a funny symbol added that could mean anything.

Topology

A topological space is a set X along with a set of subsets of X called open sets. The empty set and the full space X are open sets. Furthermore, the set of open sets is closed under finite intersections and arbitrary unions.

There’s not much you can say about topological spaces in general because, for example, the definition includes extreme cases such as the discrete topology (every subset of X is open) and the indiscrete topology (only the empty set and X are open).

Regular and normal

Like many areas of mathematics, logic and topology use the terms “regular” and “normal” to refer to systems with common choices of extra structure.

Modal logic

A regular modal logic is a normal modal logic with a second modal operator ◇ (“diamond”) that satisfies

◇ p ⇔ ¬ (□ ¬ p)

and has the inference rule (p ∧ q) → r implies (□p ∧ □q) → □r.

A modal logic is normal if it satisfies the axiom

□ (p → q) → (□ p → □ q)

and the inference rule that if p is a theorem, □p is also a theorem.

Topology

Topology also uses regular and normal to refer to adding a few axioms.

A regular topological space is one in which you can separate points from closed sets. Given a point x and a closed set F not containing x, there exist disjoint open sets U and V such that x is contained in U and F is contained in V. [1]

A normal topological space is one in which you can separate disjoint closed sets.

For many mathematicians, a metric space is the weakest topology they’re interested in, and metric spaces are normal. But weaker topologies come up. The Zariski topology in algebraic geometry is not regular, and the weak topology on an infinite dimensional Banach space is regular but not normal.

Related posts

[1] Why do we use F to denote a closed set? It’s a convention that goes back to the French word fermé for “closed.”

 

All pieces on a 6 by 5 board

I’ve written a couple posts lately on getting an LLM to generate code to solve chess problems. The first used Claude to generate Prolog and the second used ChatGPT to generate Prolog. This post will use Claude to generate Z3/Python code.

The puzzle is one I’ve written about before:

Place all the pieces—king, queen, two bishops, two knights, and two rooks—on a 6 × 5 chessboard, with the requirement that the two bishops be on opposite colored squares and no piece is attacking another.

Incidentally, it’s common for “piece” to exclude pawns, as above. But then what do you call all the things on a chessboard? You might call them “chess pieces,” in which case a pawn is a “chess piece” but not a “piece.” One convention is to use “chessmen” or simply “men” to include pieces and pawns.

This was the prompt I used.

Write Z3/Python code to find all solutions to the following chess puzzle.

Place all the pieces—king, queen, two bishops, two knights, and two rooks—on a 6 × 5 chessboard, with the requirement that the two bishops be on opposite colored squares and no piece is attacking another.

The code found 192 = 8 × 24 solutions. The factor of 8 comes from 23 ways of swapping the pairs of bishops, knights, and rooks. The script reports

Total raw solutions: 192
Unique solutions (deduplicating piece-pair swaps): 24

── Solution 1 ──
  0 1 2 3 4
0 . K . . N
1 . . . . B
2 . . R . .
3 Q . . . .
4 . . . R .
5 . B . . N
  King: (0,1)
  Queen: (3,0)
  Bishop1: (5,1) [light]
  Bishop2: (1,4) [dark]
  Knight1: (5,4)
  Knight2: (0,4)
  Rook1: (4,3)
  Rook2: (2,2)

── Solution 2 ──
  0 1 2 3 4
0 N . . K .
1 B . . . .
2 . . R . .
3 . . . . Q
4 . R . . .
5 N . . B .
  King: (0,3)
  Queen: (3,4)
  Bishop1: (5,3) [light]
  Bishop2: (1,0) [dark]
  Knight1: (5,0)
  Knight2: (0,0)
  Rook1: (4,1)
  Rook2: (2,2)

...

── Solution 24 ──
  0 1 2 3 4
0 B . . B .
1 N . . . .
2 . . . . Q
3 . . R . .
4 . R . . .
5 N . . K .
  King: (5,3)
  Queen: (2,4)
  Bishop1: (0,0) [light]
  Bishop2: (0,3) [dark]
  Knight1: (5,0)
  Knight2: (1,0)
  Rook1: (3,2)
  Rook2: (4,1)

Here’s the code.

"""
Chess Puzzle Solver using Z3
Place king, queen, 2 bishops, 2 knights, 2 rooks on a 6×5 board
such that:
  - The two bishops are on opposite-colored squares
  - No piece attacks any other piece
"""

from z3 import (
    Int, Solver, Or, And, Not, Distinct, If,
    sat, ForAll, Implies
)
import itertools

ROWS = 6
COLS = 5
TOTAL_CELLS = ROWS * COLS  # 30

PIECES = ["king", "queen", "bishop1", "bishop2", "knight1", "knight2", "rook1", "rook2"]
N = len(PIECES)  # 8 pieces

def cell(r, c):
    return r * COLS + c

def pos_to_rc(pos):
    return pos // COLS, pos % COLS

def color(r, c):
    """Square color: 0=light, 1=dark"""
    return (r + c) % 2

def attacks_king(r1, c1, r2, c2):
    return max(abs(r1 - r2), abs(c1 - c2)) == 1

def attacks_queen(r1, c1, r2, c2):
    return (r1 == r2 or c1 == c2 or
            abs(r1 - r2) == abs(c1 - c2))

def attacks_bishop(r1, c1, r2, c2):
    return abs(r1 - r2) == abs(c1 - c2)

def attacks_rook(r1, c1, r2, c2):
    return r1 == r2 or c1 == c2

def attacks_knight(r1, c1, r2, c2):
    dr, dc = abs(r1 - r2), abs(c1 - c2)
    return (dr == 1 and dc == 2) or (dr == 2 and dc == 1)

# Map piece index to its attack function
ATTACK_FN = {
    0: attacks_king,
    1: attacks_queen,
    2: attacks_bishop,
    3: attacks_bishop,
    4: attacks_knight,
    5: attacks_knight,
    6: attacks_rook,
    7: attacks_rook,
}

def solve_all():
    solutions = []

    # Each piece gets an integer position in [0, TOTAL_CELLS)
    pos = [Int(f"pos_{p}") for p in PIECES]

    s = Solver()

    # All positions in valid range
    for p in pos:
        s.add(p >= 0, p < TOTAL_CELLS)

    # All pieces on distinct squares
    s.add(Distinct(*pos))

    # Bishops on opposite colors
    # bishop1 = pos[2], bishop2 = pos[3]
    # color of pos = ((pos // COLS) + (pos % COLS)) % 2
    b1_color = (pos[2] / COLS + pos[2] % COLS) % 2  # Z3 integer arithmetic
    b2_color = (pos[3] / COLS + pos[3] % COLS) % 2

    # Z3 doesn't do Python //; use integer division carefully
    # We'll encode opposite colors: sum of colors == 1
    # color(pos) = (row + col) % 2 = (pos//COLS + pos%COLS) % 2
    # For Z3 int vars, use: (pos / COLS + pos % COLS) % 2
    s.add((pos[2] / COLS + pos[2] % COLS) % 2 != (pos[3] / COLS + pos[3] % COLS) % 2)

    # No piece attacks another
    # We enumerate all (i,j) pairs and for each possible (pos_i, pos_j) assignment,
    # assert that those pieces don't attack each other.
    # Since positions are Z3 vars, we use a constraint table approach:
    # For each pair (i,j), add constraints over all concrete (r1,c1,r2,c2) combos.

    # Pre-build attack lookup tables for each piece-type pair
    # This avoids slow Z3 symbolic reasoning over large disjunctions.

    # We'll encode: for all concrete assignments, if pos[i]==cell(r1,c1) and pos[j]==cell(r2,c2),
    # then piece i must not attack piece j.
    # Equivalently: NOT (pos[i]==cell(r1,c1) AND pos[j]==cell(r2,c2)) for all attacking pairs.

    # For efficiency, we build a set of "forbidden (cell_i, cell_j)" pairs per piece pair.

    for i, j in itertools.combinations(range(N), 2):
        fn_i = ATTACK_FN[i]
        fn_j = ATTACK_FN[j]
        forbidden = []
        for r1, c1 in itertools.product(range(ROWS), range(COLS)):
            for r2, c2 in itertools.product(range(ROWS), range(COLS)):
                if (r1, c1) == (r2, c2):
                    continue
                # i attacks j or j attacks i
                if fn_i(r1, c1, r2, c2) or fn_j(r2, c2, r1, c1):
                    forbidden.append((cell(r1, c1), cell(r2, c2)))

        if forbidden:
            # Add: NOT (pos[i] == ci AND pos[j] == cj) for each forbidden pair
            s.add(And([
                Not(And(pos[i] == ci, pos[j] == cj))
                for ci, cj in forbidden
            ]))

    # Enumerate all solutions
    solution_count = 0
    while s.check() == sat:
        m = s.model()
        sol = [m[pos[k]].as_long() for k in range(N)]
        solutions.append(sol)
        solution_count += 1

        # Block this solution (and symmetric duplicates via blocking exact assignment)
        s.add(Or([pos[k] != sol[k] for k in range(N)]))

        if solution_count % 10 == 0:
            print(f"  Found {solution_count} solutions so far...")

    return solutions

def print_board(sol):
    board = [["." for _ in range(COLS)] for _ in range(ROWS)]
    symbols = ["K", "Q", "B", "B", "N", "N", "R", "R"]
    for k, p in enumerate(sol):
        r, c = pos_to_rc(p)
        board[r][c] = symbols[k]
    print("  " + " ".join(str(c) for c in range(COLS)))
    for r in range(ROWS):
        print(f"{r} " + " ".join(board[r]))

def canonicalize(sol):
    """
    Produce a canonical form to deduplicate solutions where
    bishop1/bishop2, knight1/knight2, rook1/rook2 are interchangeable.
    Returns a frozenset-based key.
    """
    king_pos   = sol[0]
    queen_pos  = sol[1]
    bishops    = tuple(sorted([sol[2], sol[3]]))
    knights    = tuple(sorted([sol[4], sol[5]]))
    rooks      = tuple(sorted([sol[6], sol[7]]))
    return (king_pos, queen_pos, bishops, knights, rooks)

def main():
    print("Solving chess puzzle on 6×5 board...")
    print("Pieces: King, Queen, 2 Bishops (opposite colors), 2 Knights, 2 Rooks")
    print("Constraint: No piece attacks another\n")

    solutions = solve_all()

    # Deduplicate by canonical form
    seen = {}
    for sol in solutions:
        key = canonicalize(sol)
        if key not in seen:
            seen[key] = sol

    unique = list(seen.values())
    print(f"\nTotal raw solutions: {len(solutions)}")
    print(f"Unique solutions (deduplicating piece-pair swaps): {len(unique)}\n")

    for idx, sol in enumerate(unique, 1):
        print(f"── Solution {idx} ──")
        print_board(sol)
        labels = ["King", "Queen", "Bishop1", "Bishop2", "Knight1", "Knight2", "Rook1", "Rook2"]
        for k, p in enumerate(sol):
            r, c = pos_to_rc(p)
            col_name = "light" if color(r, c) == 0 else "dark"
            sq = f"({r},{c})"
            extra = f" [{col_name}]" if "Bishop" in labels[k] else ""
            print(f"  {labels[k]}: {sq}{extra}")
        print()

if __name__ == "__main__":
    main()

Related post: Lessons Learned With the Z3 SAT/SMT Solver

Writing Prolog with ChatGPT

A few days ago I wrote about using Claude to solve a chess puzzle by writing Prolog code. This morning I tried a similar chess puzzle with ChatGPT. The task is to place a queen, king, rook, bishop, and knight on a 4 by 4 chessboard so no piece attacks another.

Of course there’s not a lot of market demand for solving chess puzzles, but there is demand for solving real-world problems that amount to logic puzzles. Experiments with chess puzzles suggest writing Prolog with LLM help might be pleasant. The syntax of Prolog can be a little odd, but the LLM handles that. And because Prolog has been around so long without changing much, you’d expect LLMs to be good at generating code in it. [1]

I gave ChatGPT this prompt:

Write a SWI Prolog program to solve the following chess puzzle. Place a queen, king, rook, bishop, and knight on a 4 by 4 chessboard so no piece attacks another. Print all solutions.

It produced a file chess4.pl (below) that I ran with

swipl -q -s chess4.pl -g solve -t halt

It found all 16 solutions:

Solution 1:
  queen=1-2  king=2-4  rook=3-1  bishop=4-3  knight=4-4
. K . N
. . . B
Q . . .
. . R .

Solution 2:
  queen=1-2  king=4-3  rook=2-4  bishop=3-1  knight=4-1
. R . .
. . . K
Q . . .
. . B N

...

Here’s the code.

% chess4.pl
% Place queen, king, rook, bishop, and knight on a 4x4 board
% so that no piece attacks another.

:- use_module(library(lists)).

square(X-Y) :-
    between(1, 4, X),
    between(1, 4, Y).

solve :-
    findall(Board, solution(Board), Boards),
    print_solutions(Boards, 1),
    length(Boards, Count),
    format('Total solutions: ~d~n', [Count]).

solution(Board) :-
    Pieces = [queen, king, rook, bishop, knight],
    findall(S, square(S), Squares),
    place_pieces(Pieces, Squares, Board),
    safe(Board).

place_pieces([], _, []).
place_pieces([Piece | Pieces], Squares, [Piece-Square | Board]) :-
    select(Square, Squares, RemainingSquares),
    place_pieces(Pieces, RemainingSquares, Board).

safe(Board) :-
    safe(Board, Board).

safe([], _).
safe([Piece-Square | Rest], Board) :-
    forall(
        member(OtherPiece-OtherSquare, Rest),
        non_attacking(Piece-Square, OtherPiece-OtherSquare, Board)
    ),
    safe(Rest, Board).

non_attacking(P1-S1, P2-S2, Board) :-
    \+ attacks(P1, S1, S2, Board),
    \+ attacks(P2, S2, S1, Board).

% Attack rules

attacks(king, X1-Y1, X2-Y2, _) :-
    X1-Y1 \= X2-Y2,
    DX is abs(X1 - X2),
    DY is abs(Y1 - Y2),
    DX =< 1,
    DY =< 1. attacks(knight, X1-Y1, X2-Y2, _) :- DX is abs(X1 - X2), DY is abs(Y1 - Y2), ( DX =:= 1, DY =:= 2 ; DX =:= 2, DY =:= 1 ). attacks(rook, S1, S2, Board) :- same_rank_or_file(S1, S2), clear_line(S1, S2, Board). attacks(bishop, S1, S2, Board) :- same_diagonal(S1, S2), clear_line(S1, S2, Board). attacks(queen, S1, S2, Board) :- ( same_rank_or_file(S1, S2) ; same_diagonal(S1, S2) ), clear_line(S1, S2, Board). same_rank_or_file(X1-Y1, X2-Y2) :- ( X1 =:= X2 ; Y1 =:= Y2 ). same_diagonal(X1-Y1, X2-Y2) :- DX is abs(X1 - X2), DY is abs(Y1 - Y2), DX =:= DY. % For sliding pieces, make sure no piece blocks the path. clear_line(X1-Y1, X2-Y2, Board) :- step(X1, X2, SX), step(Y1, Y2, SY), NX is X1 + SX, NY is Y1 + SY, clear_path(NX-NY, X2-Y2, SX-SY, Board). clear_path(Target, Target, _, _) :- !. clear_path(X-Y, Target, SX-SY, Board) :- \+ occupied(X-Y, Board), NX is X + SX, NY is Y + SY, clear_path(NX-NY, Target, SX-SY, Board). step(A, B, 0) :- A =:= B. step(A, B, 1) :- B > A.
step(A, B, -1) :-
    B < A.

occupied(Square, Board) :-
    member(_-Square, Board).

% Printing

print_solutions([], _).
print_solutions([Board | Rest], N) :-
    format('Solution ~d:~n', [N]),
    print_coordinates(Board),
    print_board(Board),
    nl,
    N1 is N + 1,
    print_solutions(Rest, N1).

print_coordinates(Board) :-
    member(queen-Q, Board),
    member(king-K, Board),
    member(rook-R, Board),
    member(bishop-B, Board),
    member(knight-N, Board),
    format('  queen=~w  king=~w  rook=~w  bishop=~w  knight=~w~n',
           [Q, K, R, B, N]).

print_board(Board) :-
    forall(
        between(1, 4, Row0),
        (
            Y is 5 - Row0,
            forall(
                between(1, 4, X),
                (
                    piece_at(X-Y, Board, Symbol),
                    format('~w ', [Symbol])
                )
            ),
            nl
        )
    ).

piece_at(Square, Board, Symbol) :-
    member(Piece-Square, Board),
    !,
    piece_symbol(Piece, Symbol).
piece_at(_, _, '.').

piece_symbol(queen,  'Q').
piece_symbol(king,   'K').
piece_symbol(rook,   'R').
piece_symbol(bishop, 'B').
piece_symbol(knight, 'N').

[1] I’ve played around with generating Prolog and Lean this weekend, and I’ve had better results with Prolog. The problems with Lean haven’t been Lean per se but the Mathlib library. The library is frequently refactored, which makes sense for a young language, but this makes it harder to generate and debug code.

Solving a chess puzzle with Claude and Prolog

Prolog is the original logic programming language. The name comes from programming in logic. More specifically, the name comes from programmation en logique because the inventor of the language, Philippe Roussel, is French.

Prolog has its advantages and disadvantages. One of the advantages is that the language represents logical problems directly. One of the disadvantages is that the syntax can be quirky. But if an LLM is writing the code, or at least helping to write the code, the syntax doesn’t matter so much.

I wanted to see how well Claude (Sonnet 4.6, medium effort) could solve a chess puzzle by Martin Gardner that I wrote about a little over a year ago. I chose a relatively obscure problem rather than something like the Eight Queens puzzle because an LLM could simply quote one of countless articles on the puzzle.

The puzzle

As I stated in the post last year, the task is to place two rooks, two bishops, and two knights on a 4 by 4 chessboard so that no piece attacks any other.

There are two basic solutions, twelve if you count rotations and reflections as different solutions.

Prolog results

Claude wrote an SWI-Prolog program that I ran with

swipl -g "run, halt" chess_placement.pl

and it gave the following output.

Total solutions (raw): 12
Solutions up to D4 symmetry: 2
Solutions up to D4 symmetry: 2

Solution 1:
B . . N
N . . B
. R . .
. . R .

Solution 2:
. B N .
. . . R
R . . .
. N B .

Claude made a nice image of the solutions.

Note that you can make eight variations of the first solution by a combination of rotations and flips. There are only four variations of the second solution because two rotations put you back where you started.

Prolog source

Here’s the Prolog code that Claude generated.

% ============================================================
%  Non-attacking placement of 2 Rooks, 2 Bishops, 2 Knights
%  on a 4x4 chessboard
%
%  Run with SWI-Prolog:
%    swipl -g "run, halt" chess_placement.pl
%
%  Or interactively:
%    swipl chess_placement.pl
%    ?- run.
%    ?- count_solutions(N).
%    ?- count_unique(N).
% ============================================================

:- use_module(library(lists)).

% ---- Board squares: (Row, Col) with Row, Col in 0..3 ----------------

square(R, C) :- member(R, [0,1,2,3]), member(C, [0,1,2,3]).

% ---- Attack predicates ----------------------------------------------

rook_attacks(R,_,R,_).          % same row
rook_attacks(_,C,_,C).          % same column

bishop_attacks(R1,C1,R2,C2) :-
    DR is abs(R1-R2), DC is abs(C1-C2),
    DR =:= DC, DR > 0.

knight_attacks(R1,C1,R2,C2) :-
    DR is abs(R1-R2), DC is abs(C1-C2),
    ( DR =:= 1, DC =:= 2 ; DR =:= 2, DC =:= 1 ).

% attacks(+Type1, +R1, +C1, +Type2, +R2, +C2)
% True if either piece threatens the other's square.
attacks(rook,   R1,C1, _,     R2,C2) :- rook_attacks(R1,C1,R2,C2).
attacks(_,      R1,C1, rook,  R2,C2) :- rook_attacks(R1,C1,R2,C2).
attacks(bishop, R1,C1, _,     R2,C2) :- bishop_attacks(R1,C1,R2,C2).
attacks(_,      R1,C1, bishop,R2,C2) :- bishop_attacks(R1,C1,R2,C2).
attacks(knight, R1,C1, _,     R2,C2) :- knight_attacks(R1,C1,R2,C2).
attacks(_,      R1,C1, knight,R2,C2) :- knight_attacks(R1,C1,R2,C2).

% ---- All pairs safe -------------------------------------------------

% Check all unordered pairs in a list.
no_attack_pair(_, []).
no_attack_pair(T1-(R1,C1), [T2-(R2,C2)|Rest]) :-
    \+ attacks(T1,R1,C1,T2,R2,C2),
    no_attack_pair(T1-(R1,C1), Rest).

all_pairs_safe([]).
all_pairs_safe([P|Rest]) :-
    no_attack_pair(P, Rest),
    all_pairs_safe(Rest).

% ---- Generate a placement -------------------------------------------
%
%  Placement = [rook-(R1,C1), rook-(R2,C2),
%               bishop-(R3,C3), bishop-(R4,C4),
%               knight-(R5,C5), knight-(R6,C6)]
%
%  We enforce an ordering within each pair to avoid counting
%  same-type duplicates (rook1 vs rook2 swapped).
%  Squares are represented canonically: (R,C) with R*4+C as index.

sq_index(R, C, I) :- I is R*4 + C.

placement(Placement) :-
    % --- Rooks ---
    square(R1r, C1r), square(R2r, C2r),
    sq_index(R1r,C1r,I1r), sq_index(R2r,C2r,I2r),
    I1r < I2r,          % canonical order for the two rooks

    % --- Bishops ---
    square(R1b, C1b), square(R2b, C2b),
    sq_index(R1b,C1b,I1b), sq_index(R2b,C2b,I2b),
    I1b < I2b,          % canonical order for the two bishops

    % --- Knights ---
    square(R1n, C1n), square(R2n, C2n),
    sq_index(R1n,C1n,I1n), sq_index(R2n,C2n,I2n),
    I1n < I2n, % canonical order for the two knights % --- All six squares distinct --- Squares = [(R1r,C1r),(R2r,C2r),(R1b,C1b),(R2b,C2b),(R1n,C1n),(R2n,C2n)], all_distinct_squares(Squares), % --- Build placement list --- Placement = [ rook-(R1r,C1r), rook-(R2r,C2r), bishop-(R1b,C1b), bishop-(R2b,C2b), knight-(R1n,C1n), knight-(R2n,C2n) ], % --- No piece attacks any other --- all_pairs_safe(Placement). all_distinct_squares([]). all_distinct_squares([S|Rest]) :- \+ member(S, Rest), all_distinct_squares(Rest). % ---- Count all solutions -------------------------------------------- count_solutions(N) :- findall(P, placement(P), Ps), length(Ps, N), format("Total solutions (raw): ~w~n", [N]). % ---- D4 symmetry transformations on a 4x4 board --------------------- % Squares (R,C) in 0..3 transform(identity, R, C, R, C). transform(rot90, R, C, C, Nr) :- Nr is 3-R. transform(rot180, R, C, Nr, Nc) :- Nr is 3-R, Nc is 3-C. transform(rot270, R, C, Nc, R) :- Nc is 3-C. transform(flipH, R, C, R, Nc) :- Nc is 3-C. transform(flipV, R, C, Nr, C) :- Nr is 3-R. transform(flipD1, R, C, C, R). transform(flipD2, R, C, Nr, Nc) :- Nr is 3-C, Nc is 3-R. apply_transform(_, [], []). apply_transform(T, [Type-(R,C)|Rest], [Type-(NR,NC)|TRest]) :- transform(T, R, C, NR, NC), apply_transform(T, Rest, TRest). % Canonical form: sort pieces within same-type pairs, then sort the % whole list to get a unique representative. canonical_placement(Placement, Canonical) :- findall(T, member(T,[identity,rot90,rot180,rot270, flipH,flipV,flipD1,flipD2]), Ts), maplist(transform_and_sort(Placement), Ts, AllForms), msort(AllForms, Sorted), Sorted = [Canonical|_]. transform_and_sort(Placement, T, Sorted) :- apply_transform(T, Placement, TPl), msort(TPl, Sorted). % ---- Count solutions up to D4 symmetry ------------------------------ count_unique(N) :- findall(P, placement(P), Ps), maplist(canonical_placement, Ps, Canonicals), list_to_set(Canonicals, Unique), length(Unique, N), format("Solutions up to D4 symmetry: ~w~n", [N]). % ---- Pretty-print a board ------------------------------------------- print_board(Placement) :- forall(member(R, [0,1,2,3]), print_row(R, Placement)), nl. print_row(R, Placement) :- forall(member(C, [0,1,2,3]), print_cell(R, C, Placement)), nl. print_cell(R, C, Placement) :- ( member(rook-(R,C), Placement) -> write('R ')
    ;   member(bishop-(R,C), Placement) -> write('B ')
    ;   member(knight-(R,C), Placement) -> write('N ')
    ;   write('. ')
    ).

% ---- Print all unique solutions -------------------------------------

print_unique_solutions :-
    findall(P, placement(P), Ps),
    maplist(canonical_placement, Ps, Canonicals),
    list_to_set(Canonicals, Unique),
    length(Unique, N),
    format("~nSolutions up to D4 symmetry: ~w~n~n", [N]),
    forall(nth1(I, Unique, Sol),
           ( format("Solution ~w:~n", [I]),
             print_board(Sol) )).

% ---- Top-level entry point ------------------------------------------

run :-
    count_solutions(Raw),
    count_unique(Sym),
    format("~n"),
    print_unique_solutions,
    format("Summary: ~w raw solutions, ~w up to D4 symmetry.~n",
           [Raw, Sym]).

Automation and Validation

It’s been said whatever you can validate, you can automate. An AI that produces correct work 90% of the time could be very valuable, provided you have a way to identify the 10% of the cases where it is wrong. Often verifying a solution takes far less computation than finding a solution. Examples here.

Validating AI output can be tricky since the results are plausible by construction, though not always correct.

Consistency checks

One way to validate output is to apply consistency checks. Such checks are necessary, but not sufficient, and often easy to implement. An simple consistency check might be that inputs to a transaction equal outputs. A more sophisticated consistency check might be conservation of energy or something analogous to it.

Certificates

Some problems have certificates, ways of verifying that a calculation is correct that can be evaluated with far less effort than finding the solution that they verify. I’ve written about certificates in the context of optimization, solving equations, and finding prime numbers.

Formal methods

Correctness is more important in some contexts than others. If a recommendation engine makes a bad recommendation once in a while, the cost is a lower probability of conversion in a few instances. If an aircraft collision avoidance system makes an occasional error, the consequences could be catastrophic.

When the cost of errors is extremely high, formal verification may be worthwhile. Formal correctness proofs using something like Lean or Rocq are extremely tedious and expensive to create, and hence not economical. But if an AI can generate a result and a formal proof of correctness, hurrah!

Who watches the watchmen?

But if an AI result can be wrong, why couldn’t a formal proof generated to defend the result also be wrong? As the Roman poet Juvenal asked, Quis custodiet ipsos custodes? Who will watch the watchmen?

An AI could indeed generate an incorrect proof, but if it does, the proof assistant will reject it. So the answer to who will watch Claude, Gemini, and ChatGPT is Lean, Rocq, and Isabelle.

Who watches the watchers of the watchmen?

Isn’t it possible that a theorem prover like Rocq could have a bug? Of course it’s possible; there is no absolute certainty under the sun. But hundreds of PhD-years of work have gone into Rocq (formerly Coq) and so bugs in the kernel of that system are very unlikely. The rest of the system is bootstrapped, verified by the kernel.

Even so, an error in the theorem prover does not mean an error in the original result. For an incorrect result to slip through, the AI-generated proof would have to be wrong in a way that happens to exploit an unknown error in the theorem prover. It is far more likely that you’re trying to prove the wrong thing than that the theorem prover let you down.

I mentioned collision avoidance software above. I looked into collision avoidance software when I did some work for Amazon’s drone program. The software that was formally verified was also unrealistic in its assumptions. The software was guaranteed to work correctly, if two objects are flying at precisely constant velocity at precisely the same altitude etc. If everything were operating according to geometrically perfect assumptions, there would be no need for collision avoidance software.

Formulating eight queens as a SAT problem

The Boolean satisfiability problem is to determine whether there is a way to assign values to variables in a set of Boolean formulas to make the formulas hold [1]. If there is a solution, the next task would be to enumerate the solutions.

You can solve the famous eight queens problem, or its generalization to n-queens, by formulating the problem as a Boolean formula then using a SAT solver to find the solutions.

It’s pretty obvious how to start. A chessboard is an 8 by 8 grid, so you have a variable for each square on the board, representing whether or not that square holds a queen. Call the variables bij where i and j run from 1 to 8.

The requirement that every row contains exactly one queen can be turned into two subrequirements:

  1. Each row contains at least one queen.
  2. Each row contains at most one queen.

The first requirement is easy. For each row i, we have a clause

bi1 ∨ bi2 ∨ bi3 ∨ … ∨ bi8

The second requirement is harder. How do you express in terms of our boolean variables that there is no more than one queen in each row? This is the key difficulty. If we can solve this problem, then we’re essentially done. We just need to do the analogous thing for columns and diagonals. (We don’t require a queen on every diagonal, but we require that there be at most one queen on every diagonal.)

First approach

There are two ways to encode the requirement that every row contain at most one queen. The first is to use implication. If there’s a queen in the first column, then there is not a queen in the remaining columns. If there’s a queen in the second column, then there is not a queen in all but the second column, etc. We have an implication for each row in each column. Let’s just look at the first row and first column.

b11 ⇒ ¬ (b12 ∨ b13 ∨ … ∨ b18)

We can turn an implication of the form a ⇒ b into the clause ¬a ∨ b.

Second approach

The second way to encode the requirement that every row contain at most one queen is to say that for every pair of squares in a row (a, b) either a has no queen or b has no queen. So for the first row we would have 8C2 = 28 clauses because there are 28 ways to choose pairs from a set of 8 things.

(¬b11 ∨ ¬b12) ∧ (¬b11 ∨ ¬b13) ∧ … ∧ (¬b17 ∨ ¬b18)

An advantage of this approach is that it directly puts the problem into conjunctive normal form (CNF). That is, our formula is a conjunction of terms that contain only disjunctions, an AND of ORs.

Related posts

[1] You’ll see the SAT problem described as finding the solution to a Boolean formula. If you have multiple formulas, then the first holds, and the second, etc. So you can AND them all together to make one formula.

Decoupling formal theorem proving effort

Terence Tao has been experimenting with formal theorem proving using Lean and writing about his experience.

Here’s something Tao said on Mathstodon that I thought was interesting.

It is remarkable how much “decoupling” is achieved by the Lean+Blueprint combo. Contributors can work locally on proving a lemma, without necessarily fully understanding the global proof structure. Mathematicians who do understand the global proof can work on the blueprint, without necessarily understanding the mechanics of Lean. Lean experts can work on technical aspects of the implementation, such as optimizing the selection of classes and definitions, without needing expert domain knowledge. A theorem can be formalized, before, after, or concurrently with the lemmas it relies on, or the applications it has. Two participants who want to discuss some finer point of the argument can localize to a very specific and highly formalized step and have a constructive discussion even if they come from quite different backgrounds. It allows for (certain types of) high-level mathematical activity to be done at a far more atomized level than is usually possible.

Related posts

The 10th Dedekind number

The nth Dedekind number M(n) is the number of monotone Boolean functions of n variables. The 9th Dedekind number was recently computed to be

M(9) = 286386577668298411128469151667598498812366.

The previous post defines monotone Boolean functions and explicitly enumerates the functions for one, two, or three variables. As that post demonstrates, M(1) = 3, M(2) = 6, and M(3) = 20. But as n increases, M(n) increases rapidly, with M(9) being on the order of 1041.

Although computing the Dedekind numbers exactly is difficult—M(8) was computed in 1991 and M(9) now in 2023—there is an explicit formula for these numbers, and much is known about their asymptotic growth. This post speculates about what M(10) might be.

Write the number k in binary and let bik be its ith bit:

b_i^k=\left\lfloor\frac{k}{2^i}\right\rfloor - 2\left\lfloor\frac{k}{2^{i+1}}\right\rfloor

Then the nth Dedekind number is given by

M(n)=\sum_{k=1}^{2^{2^n}} \prod_{j=1}^{2^n-1} \prod_{i=0}^{j-1} \left(1-b_i^k b_j^k\prod_{m=0}^{\log_2 i} (1-b_m^i+b_m^i b_m^j)\right)

and so

M(10)=\sum_{k=1}^{2^{1024}} \prod_{j=1}^{1023} \prod_{i=0}^{j-1} \left(1-b_i^k b_j^k\prod_{m=0}^{\log_2 i} (1-b_m^i+b_m^i b_m^j)\right)

In principle, all you have to do to compute M(10) is evaluate the sum above. However, since this sum has more than 10308 terms, it would take a while.

What can we say about M(10) without computing it? The number of monotone Boolean functions of n variables is less than the total number of Boolean functions of n variables, which equals

2^{2^n}

That tells us M(10) < 1.8 × 10308.

There are more useful bounds. It has been proven that

{n\choose \lfloor n/2\rfloor}\le \log_2 M(n)\le {n\choose \lfloor n/2\rfloor}\left(1+O\left(\frac{\log n}{n}\right)\right)

This gives us a definite lower bound but not a definite upper bound. We know M(10) ≥ 2252 which is approximately 7.237 × 1075, but we don’t know what the big-O term is. All we know is that for sufficiently large n, this term is smaller than some multiple of log(n)/n. How large does n need to be and what is this constant? I don’t know. Maybe researchers in this area have some partial results.

Let’s take a guess at the upper bound by seeing what the big-O term was for M(9). Find k such that

\log_2 M(9) = \binom{9}{4}\left(1 + k \frac{\log 9}{9}\right)

We get

k = \left(\frac{\log_2M(9)}{126} - 1 \right)\frac{9}{\log 9} \approx 0.3809

and we can use this to guess that

\log_2 M(10) \stackrel{?}{=} \binom{10}{5}\left(1 + 0.3809 \frac{\log 10}{10}\right) \approx 274.1

which would imply M(10) = 3.253 × 1082.

So to recap, we know for certain that M(10) is between 7.237 × 1075 and 1.8 × 10308, and our guess based on the heuristic above is that M(10) = 3.253 × 1082.