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Stochastic Games and Their Complexities Public Defense

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Stochastic Games and Their Complexities Public Defense Stochastic games and their complexities public defense Marcin Przyby lko University of New Caledonia University of Warsaw 03.04.2019 Supervisors: Teodor Knapik & Damian Niwi´nski Auxiliary supervisor: Micha l Skrzypczak 0 / 22 Stochastic games Games with probabilistic transitions backgammon board 1 / 22 Stochastic games Games with probabilistic transitions ... ... ... ... ... ... graph of configurations 1 / 22 Formal model Games on graphs I move a single token along the lines of a graph e m 0:2 e 0:5 0:8 n 0:5 0:8 a m a 0:2 I decide the outcome based on the history of the token a word over the labels of the vertices 2 / 22 Stochastic branching games I study I two-player zero-sum games e.g. chess I of infinite duration e.g. parity games I with randomness e.g. simple stochastic games I and a form of concurrency. i.e. stochastic branching games by Mio Strong model: subsume many turn-based games extension of Gale-Stewart games and some concurrent games. can encode Blackwell games 3 / 22 Branching game Game G = hB; Φi is played by two players (Eve and Adam) and consists of I a board B the graph where we move the tokens I and an objective Φ: plays(B) ! [0; 1]. that can be seen as a rulebook ... 4 / 22 Branching games, an example Board { (unfolding of a) binary graph, consisting of Adam's, Eve's, Nature's and branching vertices. c n e1 0:8 0:2 e2 a f1 f2 The black token marks the initial vertex. 5 / 22 Branching play Board Play c c n e1 0:8 0:2 e2 a f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e2 a f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 a f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a a f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a a f1 f2 f1 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a a f1 f2 f1 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a a f1 f2 f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a a f1 f2 f1 f2 preplay { a labelled tree with tokens 6 / 22 Branching play Board Play c c n e1 n e1 0:8 0:2 e1 e2 e2 a a a f1 f2 f1 f2 play { an infinite labelled tree 6 / 22 Games and measure Fix a game G and a profile of strategies hσ; πi; σ; π : fL; Rg∗ ! fL; Rg σ,π the outcome generates a measure µ defined on a set TΓ; TΓ { a set of trees labelled with some finite setΓ. the objective is a functionΦ: TΓ ! [0; 1] Φ is called the pay-off function Eve wants to maximise the pay-off, Adam to minimise it. the value val (σ; π) of a profile is the expected pay-off EΦ wrt. the measure µσ,π 7 / 22 Two questions Problem (determinacy) Given a game G, is the game determined? pure and mixed determinacy Problem (game value) Given a game G, what is the value of the game G? six possible values 8 / 22 Game values and determinacy Let G be a branching game, I Eve's value: XE def val = supσ2ΣXE infπ2ΣA val (σ; π) I Adam's value: XA def val = infπ2ΣXA supσ2ΣE val (σ; π) X ranges over pure ("), behavioural (B), and mixed (M) strategies Strategies ∗ I pure σ : fL; Rg ! fL; Rg, ∗ I mixed σm 2 Dist(σ : fL; Rg ! fL; Rg). E BE ME ΣB ( ΣB ( ΣB A BA MA ΣB ( ΣB ( ΣB 9 / 22 Determinacy Holds valA ≥ valBA ≥ valMA ≥ valME ≥ valBE ≥ valE Game G is determined I under pure strategies if valA = valE , I under behavioural strategies if valBA = valBE , I under mixed strategies if valMA = valME . 10 / 22 valA valBA valMA valME valBE valE Game values: 3 1 1 1 1 4 2 2 4 0 Double matching pennies c c c x1 x2 x3 x4 0 1 f def L = t 2 plays(B) j (x3(t)=x4(t)) =) (x1(t)=x2(t)=x3(t)=x4(t)) example by Mio 11 / 22 Double matching pennies c c c x1 x2 x3 x4 0 1 f def L = t 2 plays(B) j (x3(t)=x4(t)) =) (x1(t)=x2(t)=x3(t)=x4(t)) example by Mio valA valBA valMA valME valBE valE Game values: 3 1 1 1 1 4 2 2 4 0 11 / 22 Regular branching games Fix a game G and a set of trees L ifΦ: TΓ ! [0; 1] of form 1 if t 2 L Φ(t) = 0 otherwise then L is called the winning set of G. L regular =) G regular branching game. regular means recoginsable by a finite automaton on trees 12 / 22 Regular pure branching games 12 / 22 Regular pure two-player games no Nature's vertices Main results: I games may not be determined, not even under mixed strategies; we provide an example I pure values are computable; winning strategies are enough I the determinacy under pure strategies can be decided. we can compute the values 13 / 22 Regular pure branching games Theorem (indeterminacy) There exists a regular finite branching pure game G such that valA valBA valMA valME valBE valE 1 1 1 0 0 0 the winning set is a difference of two open sets Theorem (computing the value) Let G be a regular finite pure branching game. Then the set of winning strategies SE (resp., SA) of Eve (resp., Adam) is regular and effectively computable. E A val = [SE 6= ;]; val = [SA 6= ;] 14 / 22 Regular stochastic branching games 14 / 22 Regular stochastic branching games Main results: I determined under mixed strategies, if the winning set is topologically closed; recall: difference of two closed sets can be indeterminate I the values are not computable, not even for one-player games; nor even mixed values of two-player pure games 15 / 22 Regular stochastic two-player games Theorem (determinacy) Let G be a regular stochastic branching game with a closed winning set. Then G is determined under mixed strategies. pay-off function is semicontinous =) apply Glicksberg's minimax theorem Theorem (computing the value) There is no algorithm that given a regular finitary branching game G computes the value of the game. reduction from probabilistic automata 16 / 22 Measures 16 / 22 Regular zero-player stochastic games Fix strategies σ; π and a board B, then µσ,π : 2TΓ * [0; 1] defined as σ,π µ (L) = valhB;Li(σ; π) is a Borel measure. We focus on the uniform measure on infinite binary trees µ∗ for any position u 2 fL; Rg∗ and any letter a 2 Γ ∗ −1 µ (ft 2 TΓ j t(u) = ag) = jΓj 17 / 22 Measures regular means recoginsable by a finite automaton Main result: the uniform measure of a regular set of trees L is computable, if L is defined by I a first-order formula with no descendant, I or a conjunctive query. both results utilise a form of locality 18 / 22 Measures Theorem (FO case) If ' is a first-order formula not using descendant, then µ∗(L(')) is rational and computable in three-fold exponential space. Theorem (CQs case) If ' is a Boolean combination of conjunctive queries, then µ∗(L(')) is rational and computable in exponential space. we reduce L(') to a clopen set having the same measure 19 / 22 Plantation game 19 / 22 Plantation game Goals: I practical part of the thesis; I presentation of potential use of discrete stochastic games; I beachhead for the future, more involved, cooperation. Problem to solve: I given a history of a fruit plantation a time series of measurements I produce a tool to predict and control the level of infestation. solution: stochastic games 20 / 22 Approach Utilise a standard framework: 1. convert raw data into series with finite number of values clustering 2. create a model simulating the changes partially observable Markov decision process 3. create a game-based reactive model a Gale-Stewart-like game with hidden states Result: simple and procedural framework creating reactive models for given time series of observations 21 / 22 Conclusions Determinacy: - mixed determinacy, if the winning set is closed; - mixed determinacy fails at the second level of Borel hierarchy; Values: - pure values of pure regular branching games are computable; - values of stochastic regular branching games are not. Measures: - uniform measure can be computed for some restricted first-order formulae. Applications: - a description of a roboust framework and a proposition of a possible implementation.
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