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Playing games
Time
Board games
Egypt, 3000 BC
Ball games
Mesoamerica, 1000 BC
Card games
China, 9th centuryEurope, 14th century
Computer games
20th century
Combinatorial games• Combinatorial games: perfect and complete information
•Outcome can in principle be predicted byexhaustively enumerating all possibleways the game may evolve
• Positional games: Players alternately claim elements of some board
• Probabilistic methods very powerful in dealing with‘combinatorial chaos’ in positional games
• The probabilistic intuition in positional games[Chvátal, Erdős ‘78], [Beck ‘93, …]:
Chess
Nim Tic-Tac-Toe
Hex
gametree
‘Combinatorial chaos’
huge!!!
clever vs. clever = random vs. random
Main contribution of this thesis
• Main contribution of this thesis:Build a similar bridge between two previously disconnected worlds
•Positional games where the goal is toavoid some given local substructure
•Benefit: Transfer insights and techniques between the two worlds and derive new results in each of them
clever vs. random
Probabilisticone-playergames
Deterministictwo-player
games
clever vs. clever
• The probabilistic intuition in positional games[Chvátal, Erdős ‘78], [Beck ‘93, …]:
clever vs. clever = random vs. random
• Probabilistic one-player avoidance games (clever vs. random)
• Edge-coloring game[Friedgut, Kohayakawa, Rödl, Ruciński, Tetali ‘03]
The probabilistic world
Painter vs.random graph processn Goal: Avoid monochromatic
copies of F for as long aspossible
F = K3
• Probabilistic one-player avoidance games (clever vs. random)
• Edge-coloring game[Friedgut, Kohayakawa, Rödl, Ruciński, Tetali ‘03]
• Vertex-coloring game[Marciniszyn, Spöhel ‘10]
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1
3
4
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The probabilistic world
Painter vs.random graph processGoal: Avoid monochromatic
copies of F for as long aspossible
n
F = K3
• Probabilistic one-player avoidance games (clever vs. random)
• Edge-coloring game[Friedgut, Kohayakawa, Rödl, Ruciński, Tetali ‘03]
• Vertex-coloring game[Marciniszyn, Spöhel ‘10]
• Achlioptas game[Krivelevich, Loh, Sudakov ‘09]
The probabilistic world
Chooser vs.random graph process
Goal: Avoid the appearanceof F for as long as possible
n
Painter vs.random graph processGoal: Avoid monochromatic
copies of F for as long aspossible
F = K3
2
1
3
4
5
6
7
8
The deterministic world• Deterministic two-player avoidance games (clever vs. clever)
• Ramsey-game[Beck ‘83][Kurek, Ruciński ‘05]
• Impose restrictions on Builder [Grytczuk, Haluszczak, Kierstead
‘04]:
•Restrict to graphs with chromatic number at most Builder can still enforce a monochromatic copy of
•Restrict to forests Builder can still enforce a monochromatic copy of any forest
Painter vs. BuilderGoals: Avoid / enforce
monochromatic copies of Ffor as long / as quickly as possible
? Yes for infinitely many values of [Conlon ‘10]
Online size-Ramsey number := minimum number of stepsnecessary for Builder to win
A bridge between the two worlds• Idea: Replace the random graph process by an adversary
with a suitable density restriction
• Observation: Winning strategies for adversary in the deterministic game yield upper bounds for the duration of the probabilistic game
• Corresponding lower bound statement much harder to prove
Density restriction:Builder must adhere to
for all subgraphs H
Concrete resultsfor these games later!
Painter vs. random graph process
Builder
Edge-coloring gameVertex-coloring game
H H
Achlioptas game• Complete solution, i.e., threshold functions for arbitrary fixed F,
presented in [M., Spöhel, Thomas ‘10], disproving a conjecture from[Krivelevich, Loh, Sudakov ‘09]
• Implicit in the analysis:Chooser vs.random graph process
Presenter
A bridge between the two worlds• Idea: Replace the random graph process by an adversary
with a suitable density restriction
• Observation: Winning strategies for adversary in the deterministic game yield upper bounds for the duration of the probabilistic game
• Corresponding lower bound statement much harder to prove
Density restriction:Presenter must adhere to
for all subgraphs H
A bridge between the two worlds• Idea: Replace the random graph process by an adversary
with a suitable density restriction
• Observation: Winning strategies for adversary in the deterministic game yield upper bounds for the duration of the probabilistic game
• Corresponding lower bound statement much harder to prove
• This upper bound technique extends straightforwardly to similar avoidance games played on random hypergraphs or random subsets of integers
The edge-coloring game• [Friedgut, Kohayakawa, Rödl, Ruciński, Tetali ‘03]:
The threshold (typical duration) of thegame with F = K3 and r=2 colorsis
• [Marciniszyn, Spöhel, Steger ‘05]: Explicit threshold functions for F (e.g.) a clique or a cycle and r=2 colors
n
For any :there is a Painter strategy that succeeds whp.
For any : every Painter strategy fails whp.
N = number of stepsThreshold
Painter vs.random graph process
The edge-coloring game• [Belfrage, M., Spöhel ‘11+]:
New upper bound approach…
• Successfully applied by [Balogh, Butterfield ‘10] to derive the first nontrivial upper bounds for F = K3 and rR3 colors
Density restriction:Builder must adhere to
for all subgraphs H
Painter vs.random graph process
n
Builder
H
Theorem: If Builder can enforce a monochromatic copy of F inthe deterministic game with r colors and density restriction d, then the threshold of the probabilistic game is bounded by
• Proof idea:
•Well-known: If F is a fixed graph with for all ,then for any , whp. after N steps the evolvingrandom graph contains many copies of F.
•Can be adapted to:If T is a fixed Builder strategy respecting a density restrictionof d, then for any , whp. after N steps the evolving random graph behaves exactly like T in many places on the board.
The edge-coloring game
Theorem: If Builder can enforce a monochromatic copy of F inthe deterministic game with r colors and density restriction d, then the threshold of the probabilistic game is bounded by
The edge-coloring game
Theorem: If Builder can enforce a monochromatic copy of F inthe deterministic game with r colors and density restriction d, then the threshold of the probabilistic game is bounded by
• Upper bound technique translates straightforwardly to othersettings (vertex-coloring game, Achlioptas game, random hypergraphs, random subsets of integers etc.)
• Corresponding lower bound statements require problem-specific work (if provable at all)
• Open problem: Define the online Ramsey density as
Is it true that ?
The vertex-coloring gamePainter vs.random graph
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1
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p = edge probabilityThreshold
For any :there is a Painter strategy that succeeds whp.
For any : every Painter strategy fails whp.
• [Marciniszyn, Spöhel ‘10]:Explicit threshold functions for F (e.g.) a cliqueor a cycle and rR2 colors
?• For these graphs, a simple
greedy strategy is optimal
• The greedy strategy is not optimalfor every graph, the general case remained open
The vertex-coloring game• [M., Rast, Spöhel ‘11+]:
For any fixed F and r, we cancompute a rational numbersuch that the threshold is
Painter vs.random graph
!• For these graphs, a simple
greedy strategy is optimal
• We solve the problem in full generality
Builder
Density restriction:Builder must adhere to
for all subgraphs H
H
The vertex-coloring gameThe vertex-coloring gamePainter vs.random graphBuilder
H
d
Builder can enforceF monochromaticallyin finitely many steps
Painter can avoidmonochromaticcopies of Findefinitely
• Define the online vertex-Ramsey density as
Density restriction:Builder must adhere to
for all subgraphs H
Painter vs. Builder
Painter vs. random graph
Theorem 1: For any F and r
• is computable
• is rational
• infimum attained as minimum
Theorem 2: For any fixed F and r,the threshold of the probabilistic one-player game is
focus for next few slides
focus for next few slides
Painter vs. Builder – Remarks
Theorem 1: For any F and r
• is computable
• is rational
• infimum attained as minimum
• …nor for the two edge-coloring analogues[Rödl, Ruciński ‘93], [Kurek, Ruciński ‘05], [Belfrage, M., Spöhel ‘11+]
• 400.000 zloty prize money for
[Rödl, Ruciński ‘93]
• None of those three statements is known for the offline quantity
[Kurek, Ruciński ‘94]
Painter vs. Builder – Remarks
• The running time of our procedure for computing is doubly exponential in v(F )…
• With the help of a computer we determined exactly
• for all graphs F on up to 9 vertices
• for F a path on up to 45 vertices
Theorem 1: For any F and r
• is computable
• is rational
• infimum attained as minimum
Painter vs. Builder
Painter vs. random graph
Theorem 1: For any F and r
• is computable
• is rational
• infimum attained as minimum
Theorem 2: For any fixed F and r,the threshold of the probabilistic one-player game is
focus for nextfew slides
focus for nextfew slides
Painter vs. random graph – Remarks
• In the asymptotic setting of Theorem 2, computing is a constant-size computation!
• So is computing the optimal Painter and Builder strategies for the deterministic game
• For some of Painter’s optimal strategies in the deterministic game, we can show that they also work in the probabilistic game polynomial-time coloring algorithms that succeed whp. in coloring Gn, p online for any
Theorem 2: For any fixed F and r,the threshold of the probabilistic one-player game is
Painter vs. random graph – Proof ideas
• Upper bound: Use our general approach to translate an optimal Builder strategy from the deterministic game to an upper bound of for the probabilistic game
Theorem 2: For any fixed F and r,the threshold of the probabilistic one-player game is
Painter vs. random graph – Proof ideas
Theorem 2: For any fixed F and r,the threshold of the probabilistic one-player game is
• Lower bound : Much more involved…• Playing ‘just as in the deterministic game’ does not
necessarily work for Painter!
• Reason: the probabilistic process with p ¿ n-1/d respects a density restriction of d only locally (the entire random graph has an expected density of £(np) )
• To overcome this issue, we need to really understand the deterministic game and the structure of Painter’s and Builder’s optimal strategies.
The path-avoidance vertex-coloring game
• So is computable, but whatis its value for natural families of graphsF like , , , , , , ?
• [M., Spöhel ’11]:
• exhibits a surprisingly complicated behavior
•Greedy strategy fails quite badly
•Evidence that a general closed formula fordoes not exist
• Simple closed formulas([Marciniszyn, Spöhel ‘10])
• Reason: Greedy strategyis optimal
The path-avoidance vertex-coloring game
• Forests F :
greedy lower bound
Theorem:
• Asymptotics for large ?
vertices•
smallest k s.t. Builder can enforce F while not buildingcycles and only trees with at most k vertices