Modelling and Solving English Peg Solitaire Chris Jefferson
- Slides: 29
Modelling and Solving English Peg Solitaire Chris Jefferson, Angela Miguel, Ian Miguel, Armagan Tarim. AI Group Department of Computer Science University of York
English Peg Solitaire 0 1 2 3 4 5 6 Initial: 0 1 2 3 4 5 6 • Horizontal or vertical moves: 0 1 2 3 4 5 6 Goal: Before 0 1 2 3 4 5 6 After • The French variant has a slightly larger board, and is considerably more difficult.
Solitaire: Interesting Features • A challenging search problem. • Highly symmetric. • Symmetries of the board, symmetries of moves. • Planning-style problem. • Not usually tackled directly with constraint satisfaction/integer programming.
Model A: IP • 31 moves required to solve a single-peg reversal. • Exploit this in the modelling. • b. State[i, j, t] {0, 1}. • describes the state of the board at time-step t = 0, …, 31. • M[i, j, t, d] {0, 1}. 1. denotes whether a move was made from location i, j at time-step t. d in {N, S, E, W}.
Model A: IP Move Conditions: `1’ means move made. Connecting board states. Consider all moves affecting a position
Model A: IP One move at a time: Objective function. Minimise:
Model B: CSP • Rather than record the board state, model B records the sequence of moves required: moves[t] • Each transition is assigned a unique number: No. 0 1 2 Trans. 2, 0 4, 0 2, 2 3, 0 3, 2 No. 3 4 5 Trans. 4, 0 2, 0 4, 2 2, 1 4, 1 No. 6 7 8… Trans. 2, 1 2, 3 3, 1 3, 3 4, 1 2, 1
Model B: CSP • Problem constraints can be stated on moves[] alone. • Consider transition 0: 2, 0 4, 0 at time-step t. The following must hold at time-step t-1. 0 1 2 3 4 5 6 • There must be pegs at 2, 0 and 3, 0. • There must be a hole at 4, 0. 0 1 • Ensure by imposing constraints on moves[1. . t-1]: Drawback: many such constraints needed. Some of very large size.
Model C = A + B: CSP • Combines models A and B to remove some of the problems of both. • Maintains: b. State[i, j, t], moves[t]. • Discards (A): M[], board state connection constraints. • Discards (B): Large arity constraints on moves[]. • Channelling constraints are added to maintain consistency between the two representations. • These connect b. State[i, j, t], moves[t], b. State[i, j, t+1]. b. State[t] b. State[t+1] constrains moves[] t constrains
Model C Channelling Constraints Changes(i, j): set of transitions that change the state of i, j peg. In(i, j): set of transitions that place a peg at i, j peg. Out(i, j): set of transitions that remove a peg from i, j • These constraints closely resemble pre- and postconditions of an AI Planning-style operator.
Results: Central Solitaire • Model A (IP): No solution in 12 hours. • Several alternative formulations also failed. • Reason: artificial objective function, hence no tight bounds to exploit. • Model B (CP): Exhausts memory. • Model C (A+B, CP solver): 16 seconds. • So: • Develop model C further. • Apply to other variations of Solitaire.
Pagoda Functions • Used to spot dead-ends early. • Value assigned to each board position such that: • Given positions a, b, c in a horizontal/vertical line: a+b c. • Pagoda value of a board state: • Sum of values at positions where there is a peg. • Monotonically decreasing as moves made: • Pagoda condition: a b c • If pagoda value for an intermediate position is less than that of final position, backtrack.
Pagoda Functions: Examples • For a single-peg Solitaire reversal at position i, j, want pagoda functions with non-zero entries at i, j. • Otherwise no pruning. • A rotation of one of these three gives a useful pagoda function for every board position: 1 1 1 1 1 1 1
Board Symmetries • Rotation. • Reflection. • Break rotational symmetry by selecting 1 st move: • Reflection symmetry persists. Remove 5, 2 3, 2:
Board Symmetries • Further into the search are both broken and reestablished, depending on the moves made. • Breaking this symmetry is a possible application for SBDS or SBDD.
Symmetries of Independent Moves • Many pairs of moves can be performed in any order without affecting the rest of the solutions. • Two transitions are independent iff: • The set of pegs upon which they operate do not intersect. • Break this symmetry by ordering adjacent entries in moves[]: • independent(moves[i], moves[i+1]) moves[i] moves[i+1] • This problem extends to larger sets of transitions. • If 2 is independent of {3, 1}, can have 2, 3, 1 and 3, 1, 2.
Results: Solitaire Reversals • Compared Model C against state of the art AI planning systems: • Blackbox 4. 2, Fast. Forward 2. 3, HSP 2. 0, and Stan 4. • Experiments on the full set of single-peg reversals. • Although many board positions symmetrical, these positions are distinguished by the transition ordering. • Transitions chosen in ascending order. No. 0 1 2 Trans. 2, 0 4, 0 2, 2 3, 0 3, 2 No. 3 4 5 Trans. 4, 0 2, 0 4, 2 2, 1 4, 1 No. 6 7 8… Trans. 2, 1 2, 3 3, 1 3, 3 4, 1 2, 1
Solitaire Reversals via AI Planning BBox 4. 2 FF 2. 3 HSP 2. 0 Stan 4 - memory exhausted. 19 0. 2 30 19 276 - Bbox, FF most successful, achieve a high percentage of coverage. 25 0. 7 1126 862 >1 hr 14 0. 05 97 >1 hr 13 49 25 121 >1 hr 47 1 28 0. 1 125 42 0. 15 16 553 >1 hr 18 >1 hr 298 - 148 543 48 3544 86 57 44 0. 05 313 >1 hr - 14 622 17 1 >1 hr 38 0. 6 27 30 48 49 0. 05 60 27 1521 >1 hr 21 9. 8 154 - 14 273 48 >1 hr 620 >1 hr 574 >1 hr 16 1564 >1 hr 21 0. 6 32 >1 hr 19 125 - Note how symmetric positions differ.
Solitaire Reversals via Model C Basic Pair Sym Breaking Pagoda Functions Pagoda+Sym 2903 221. 5 2730 54. 6 Blank: all >1 hr 439 61. 1 443 64. 9 >1 hr 1891. 2 >1 hr 1036 Less robust. Bad value ordering? Sym breaking, pagoda help. 17 3. 5 18 4. 9 116 19. 1 102 22, 3 16 4. 1 7. 9 5 >1 hr 337. 8 >1 hr 349. 6 7 2. 7 8 2. 7 1700 197 1712 199. 9
Model C + Corner Bias Value Ordering Basic Pair Sym Breaking Pagoda Functions Pagoda+Sym 2903 221. 5 2730 54. 6 Blank: all >1 hr 439 61. 1 443 64. 9 >1 hr 1891. 2 >1 hr 1036 Taking symmetry back into account, can now cover all but one reversal 17 3. 5 18 1. 2 4. 9 116 19. 1 102 22, 3 16 4. 1 7. 9 1. 3 5 >1 hr 337. 8 >1 hr 349. 6 7 2. 7 0. 7 8 2. 7 1700 197 1712 199. 9 0. 7 1. 2 4. 6
Symmetric Paths • There are often multiple ways of arriving at the same board state. • Some are due to independent moves. Others are not:
Symmetric Paths • Find all solutions to a given depth. • Group the transition sequences that lead to identical positions. • Insert constraints that allow one representative per group. Depth 4 Solutions Found 328 Solutions Pruned 32 Constraints Time (s) Added 32 <1 5 1572 234 205 1. 5 6 7152 1504 1256 10 7 29953 8111 6167 116 Total 50600 28818 7660
Fool’s Solitaire • An optimisation variant. • Reach a position where no further moves are possible in the shortest sequence of moves. • Not easily stated as an AI planning problem. • Shows the flexibility of the CP and IP approaches.
Fool’s Solitaire: IP Model • Moves, connection of board states same as model A. C[i, j, t]=1 iff there is a peg at position i, j with a legal move. New objective function. Minimise:
Fool’s Solitaire: CP Model • Modified version of model C. • An extra transition, dead. End, is added to the domain of the moves[] variables. • Assigned when no other move is possible. • dead. End transition is only allowed when no other transitions are possible. • Preconditions based on b. State[]. • If dead. End at moves[t], then also at all following time-steps:
Fool’s Solitaire: Results • CP, reverse instantiation order: 20 s • IP, iterative approach: 27 s
Conclusions • Basic, and ineffective CP and IP models combined into a superior CP model. • Another instance of the utility of channelling between two complementary models. • Each allows easy statement of different aspects of the problem: • Model A: preconditions on state changes without considering entire move history. • Model B: one move at once, combines 3 state changes into a single token.
Conclusions • Encouraging results versus dedicated AI planning systems. • Lessons learned should generalise to other sequential planning-style problems. • Channelling constraints specify action pre- and post-conditions. • Breaking symmetry of independent actions/paths.
Future Work • Further configurations of English Solitaire. • Other optimisation variants: • Minimise number of draughts-like multiple moves using a single peg. • Proving unsolvability. • Large search space to explore. • Will need improved symmetry breaking. • French Solitaire.
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