Foundations of Constraint Processing, Fall 2012 Odds and Ends: Modeling Examples & Graphical Representations 1Odds & Ends Foundations of Constraint Processing CSCE421/821, Fall Berthe Y. Choueiry (Shu-we-ri) Avery Hall, Room 360 Tel: +1(402) Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Outline Modeling examples – Minesweeper, Game of Set Graphical representations Odds & Ends2Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Minesweeper Variables? Domains? Constraints? Odds & Ends3Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Minesweeper as a CSP demo demo Variables are the cells Domains are {0,1} (i.e., safe or mined) One constraint for each cell with a number (arity 1...8) Odds & Ends4 Exactly two mines: , etc. Exactly three mines: , etc. Joint work with R. Woodward, K. Bayer & J. Snyder Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Game of Set[Falco 74] Odds & Ends5 Joint work with Amanda Swearngin and Eugene C. Freuder Deck of 81(=3 4 ) cards, each card with a unique combination of 4 attributes values 1.Number  {1,2,3} 2.Color  { green,purple,red } 3.Filling  { empty,stripes, full } 4.Shape  { diamond,squiggle,oval } Solution set: 3 cards  attribute, the 3 cards have either the same value or all different values 12 cards are dealt, on table [3,21] Recreational game, favorite of children & CS/Math students New toy problem for AI: a typical multi-dimensional CSP Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Set: Constraint Model I Odds & Ends6 Model I – Three variables – Same domain (12 cards) – One ‘physical’ constraints – Four 1-dimensional constraints Size of model? c 1,c 2,c 3,…,c 12 C=⊕C≠C=⊕C≠ F=⊕F≠F=⊕F≠ S=⊕S≠S=⊕S≠ N=⊕N≠N=⊕N≠ id ≠ c 1,c 2,c 3,…,c 12 c1c1 c2c2 c3c3 c4c4 c5c5 c6c6 c7c7 c8c8 c9c9 Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Set: Constraint Model II Model II – 12 variables (as many as on table) – Boolean domains {0,1} – Constraints: much harder to express Exactly 3 cards: Sum(assigned values)=3? AllEqual/AllDiff constraints? Size of model? Odds & Ends7Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Graphical Representations Always specify V,E for a graph as G=(V,E) Main representations – Binary CSPs Graph (for binary CSPs) Microstructure (supports) Co-microstructure (conflicts) – Non-binary CSPs Hypergraph Primal graph – Dual graph Odds & Ends8Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Binary CSPs Odds & Ends9 Macrostructure G(P)=(V,E) V= E= Micro-structure  (P)=(V,E) V= E= Co-microstructure co-  (P) =(V,E) V= E= a, b a, c b, c    V1 V2 V3 (V1, a ) (V1, b) (V2, a ) (V2, c) (V3, b ) (V3, c) (V1, a ) (V1, b) (V2, a ) (V2, c) (V3, b ) (V3, c) No goods Supports Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Non-binary CSPs: Hypergraph Hypergraph (non-binary CSP) – V= – E= Odds & Ends10 R3R3 A B C D E F R1R1 R4R4 R2R2 R5R5 R6R6 R3R3 A B C D E F R1R1 R4R4 R2R2 R5R5 R6R6 Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Non-binary CSPs: Primal Graph Primal graph – V= – E= Odds & Ends11 R3R3 A B C D E F R1R1 R4R4 R2R2 R5R5 R6R6 A B C D E F Sep 21, 2011

Foundations of Constraint Processing, Fall 2012 Dual Graph V= G= Odds & Ends12 R4R4 BCD ABDE CF EF AB R3R3 R1R1 R2R2 C F E BD AB D AD A B R5R5 R6R6 R3R3 A B C D E F R1R1 R4R4 R2R2 R5R5 R6R6 HypergraphDual graph Sep 21, 2011