## Universal graphs for parity and mean payoff games

### Main objectives

• introduce the notion of universal graphs and their applications to games
• give a simple and unified presentation of all three quasipolynomial time algorithms for parity games
• construct new algorithms for mean payoff games
• show tight lower bounds for the construction of algorithms in this framework for both parity and mean payoff games
Reports on

## Parity games Parity: the maximal priority appearing infinitely often is even

### Solving parity games

INPUT: A parity game and initial vertex $v_0$
QUESTION: Does Eve have a winning strategy from $v_0$?

Parameters: $n$ (number of vertices) and $d$ (number of priorities)

Best algorithms (Calude, Jain, Khoussainov, Li, and Stephan STOC best paper award 2017, then Jurdziński and Lazić 2017, then Lehtinen 2018) $$O(n^{\log(d)}) = O(d^{\log(n)})$$

### Why might you care?

Parity games play a crucial role in:
• LTL synthesis
• automata and logic over infinite trees (emptiness games)
• modal mu-calculus (model-checking games)

But also complexity: in $\textrm{NP} \cap \textrm{coNP}$, not known to be in $\textrm{P}$!

## Mean payoff games Mean payoff: the average limit is non negative

### Solving mean payoff games

INPUT: A mean payoff game and initial vertex $v_0$
QUESTION: Does Eve have a winning strategy from $v_0$?

Parameters: $n$ (number of vertices), $m$ (number of edges), $B$ (largest weight), $k$ (number of different weights)

Best algorithms (Zwick and Paterson 1996, Brim et al 2011) $$O(n m B)$$

### Parity games as mean payoff games

Parity: the maximal priority appearing infinitely often is even Mean payoff: the average limit is non negative

Replace priority $p$ by $(-n)^p$:
• priority $1$ becomes weight $-n$
• priority $2$ becomes weight $n^2$
• priority $3$ becomes weight $-n^3$
Key idea: in a cycle (of length $n$) only the largest weight matters
Positional strategy $$\sigma : V \to E$$ Theorem: If Eve has a winning strategy in a mean payoff game, she also has a positional winning strategy. The same holds for Adam. ## Separating automata

Definition: A graph satisfies parity if all paths in the graph satisfy parity (eq: all cycles have maximal even priority) $$\text{Parity}_n = \bigcup \left\{ \text{Paths}(G) : \begin{array}{c} G \text{ is an } (n,d)\text{-graph} \\ \text{satisfying parity}\end{array} \right\}.$$ Definition: A deterministic safety automaton $\mathcal{A}$ reading $[1,d]^\omega$ is parity separating if $$\text{Parity}_n \subseteq L(\mathcal{A}) \subseteq \text{Parity}$$

## Reduction to safety games

Let $G$ parity game, $A$ parity separating automaton recognising $L$.

Lemma: Eve has strategy ensuring parity if and only if she has a strategy ensuring $L$

Algorithm: Solve the safety game $G \times A$, complexity $m \cdot |A|$
Lemma: Eve has strategy ensuring parity if and only if she has a strategy ensuring $L$

Proof:
If Eve has a strategy ensuring parity, she has a positional one. Then $G_{\sigma}$ satisfies parity, so $\sigma$ ensures $L$ because $\text{Parity}_n \subseteq L$.

If Eve has a strategy ensuring $L$ then this strategy ensures parity because $L \subseteq \text{Parity}$.

## Equivalence

Theorem: (Colcombet, F.)
• A universal graph induces a separating automaton of the same size
• A separating automaton induces a universal graph of the same size

This holds for any positional condition (ex: parity and mean payoff)!

## Results

Theorem: (Czerwiński, Daviaud, F., Jurdziński, Lazić, and Parys)
• There exists a quasipolynomial parity separating automaton (three constructions, one for each quasipolynomial time algorithm)
• All parity separating automata have at least quasipolynomial size
Equivalently: universal graphs

## Universal trees

Definition: A tree is $(n,h)$-universal if it embeds all trees of height $h$ with $n$ leaves $\qquad$ Fact: Universal trees are exactly maximal universal graphs!

## Universal graphs

Definition: A graph satisfies mean payoff if all paths in the graph satisfy mean payoff (eq: no negative cycles)
Definition: A (graph) homomorphism is $\phi : V \to V'$ st $$(v,w,v') \in E \Longrightarrow (\phi(v),w,\phi(v')) \in E$$
Definition: A graph $U$ is $(n,W)$-MP universal if
• it satisfies mean payoff
• all graphs of size $n$ with weights in $W$ satisfying mean payoff can be homomorphically mapped into $U$ Lemma: (parameter: maximal weight $B$)
This graph U is $(n,[-B,B])$-MP universal: $$\phi : G \to U \text{ defined by } \phi(v) = \text{dist}(v_0,v)$$ Key idea: if $(v,w,v') \in E$, then $\text{dist}(v_0,v') \le \text{dist}(v_0,v) + w$
Corollary: Algorithm $O(n m B)$ (matches best known so far)

## Results

Theorem: (parameter: maximal weight $B$)
• There exists a universal MP graph of size $2n (nB)^{1 - 1/n}$
• All universal MP graphs have size at least $B^{1 - 1/n}$
Corollary: Algorithm $O(n m (nB)^{1 - 1/n})$

Theorem: (parameter: number of weights $k$)
• There exists a universal MP graph of size $n^k$
• All universal MP graphs have size at least $n^{k - 2}$
Corollary: Algorithm $O(m n^k)$