Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)

From Duels to Battleﬁelds: Computing Equilibria of Blotto and Other Games∗

AmirMahdi Ahmadinejad,∗ Sina Dehghani,† MohammadTaghi Hajiaghayi,† Brendan Lucier,‡ Hamid Mahini,†, Saeed Seddighin† ∗Stanford University †Univeristy of Maryland, ‡Microsoft Research [email protected], [email protected], {dehghani, hajiagha, hmahini, sseddigh}@umd.edu

Abstract ily obvious how to ﬁnd an equilibrium. Indeed, the conclu- sions to date have been largely negative: computing a Nash We study the problem of computing Nash equilibria of zero- equilibrium of a normal-form game is known to be PPAD- sum games. Many natural zero-sum games have exponen- complete (Daskalakis, Goldberg, and Papadimitriou 2009; tially many strategies, but highly structured payoffs. For ex- ample, in the well-studied Colonel Blotto game (introduced Goldberg and Papadimitriou 2006), even for two-player games (Chen and Deng 2006). In fact, it is PPAD-complete by Borel in 1921), players must divide a pool of troops among 1 a set of battleﬁelds with the goal of winning (i.e., having more to ﬁnd an nO(1) approximation to a Nash equilibrium (Chen, troops in) a majority. The Colonel Blotto game is commonly Deng, and Teng 2006). These results call into question the used for analyzing a wide range of applications from the U.S predictiveness of Nash equilibrium as a solution concept. presidential election, to innovative technology competitions, This motivates the study of classes of games for which to advertisement, to sports. However, because of the size of equilibria can be computed efﬁciently. It has been found that the strategy space, standard methods for computing equilib- many natural and important classes of games have struc- ria of zero-sum games fail to be computationally feasible. In- ture that can be exploited to admit computational results deed, despite its importance, only few solutions for special (Dantzig 1963; Garg, Jiang, and Mehta 2011; Kontogian- variants of the problem are known. nis and Spirakis 2010; Lipton, Markakis, and Mehta 2003; In this paper we show how to compute equilibria of Colonel Alon et al. 2013; Demaine et al. 2007; 2009; Ahmadinejad et Blotto games. Moreover, our approach takes the form of a al. 2015). Perhaps the most well-known example is the class general reduction: to ﬁnd a Nash equilibrium of a zero-sum 1 game, it sufﬁces to design a separation oracle for the strat- of zero-sum two-player games , where player 2’s payoff is egy polytope of any bilinear game that is payoff-equivalent. the negation of player 1’s payoff. The normal-form repre- We then apply this technique to obtain the ﬁrst polytime algo- sentation of a zero-sum game is a matrix A, which speciﬁes rithms for a variety of games. In addition to Colonel Blotto, the game payoffs for player 1. This is a very natural class we also show how to compute equilibria in an inﬁnite-strategy of games, as it models perfect competition between two par- variant called the General Lotto game; this involves showing ties. Given the payoff matrix for a zero-sum game as input, how to prune the strategy space to a ﬁnite subset before ap- a Nash equilibrium can be computed in polynomial time, plying our reduction. We also consider the class of dueling and hence time polynomial in the number of pure strategies games, ﬁrst introduced by Immorlica et al. (2011). We show available to each player (Dantzig 1963). Yet even for zero- that our approach provably extends the class of dueling games for which equilibria can be computed: we introduce a new du- sum games, this algorithmic result is often unsatisfactory. eling game, the matching duel, on which prior methods fail to The issue is that for many games the most natural represen- be computationally feasible but upon which our reduction can tation is more succinct than simply listing a payoff matrix, be applied. so that the number of strategies is actually exponential in the most natural input size. In this case the algorithm described above fails to guarantee efﬁcient computation of equilibria, Introduction and alternative approaches are required. Computing a Nash equilibrium of a given game is a cen- tral problem in algorithmic game theory. It is known that The Colonel Blotto Game A classical and important ex- every ﬁnite game admits a Nash equilibrium (that is, a pro- ample illustrating these issues is the Colonel Blotto game, ﬁle of strategies from which no player can beneﬁt from a ﬁrst introduced by Borel in 1921 (Borel 1921; 1953; Frechet´ unilateral deviation) (Nash 1951). But it is not necessar- 1953a; 1953b; von Neumann 1953). In the Colonel Blotto ∗Supported in part by NSF CAREER award 1053605, game, two colonels each have a pool of troops and must ﬁght NSF grant CCF-1161626, ONR YIP award N000141110662, against each other over a set of battleﬁelds. The colonels DARPA/AFOSR grant FA9550-12-1-0423, and a Google faculty simultaneously divide their troops between the battleﬁelds. research award. A colonel wins a battleﬁeld if the number of his troops Copyright c 2016, Association for the Advancement of Artiﬁcial Intelligence (www.aaai.org). All rights reserved. 1Or, equivalently, constant-sum games.

369 dominates the number of troops of his opponent. The ﬁnal been observed that such bilinear games can be solved efﬁ- payoff of each colonel is the (weighted) number of battle- ciently when the strategy polytope has polynomially many ﬁelds won. An equilibrium of the game is a pair of colonels’ constraints (Charnes 1953; Koller, Megiddo, and Von Sten- strategies, which is a (potentially randomized) distribution gel 1994). In each of the examples described above, it is of troops across battleﬁelds, such that no colonel has in- shown how to map strategies from the original games to ap- centive to change his strategy. Although the Colonel Blotto propriate payoff-equivalent bilinear games, in which strate- game was initially proposed to study a war situation, it has gies are choices of marginal probabilities from the original found applications in the analysis of different forms of com- game. If one can also map a strategy in the bilinear game petition: from sports, to advertisement, to politics (Myer- back to the original game, then one has a polytime reduction son 1993; Kovenock and Roberson 2010; 2012; Roberson to the (solved) problem of ﬁnding equilibria of the bilinear and Kvasov 2012; Bachrach, Syrgkanis, and Vojnovic 2011; game. In each of these prior works it is this latter step – map- Polevoy, Trajanovski, and de Weerdt 2014), and has thus be- ping back to the original game – that is the most demanding; come one of the most well-known games in classic game this generally requires a problem-speciﬁc way to convert a theory. proﬁle of marginals into a corresponding mixed strategy in Colonel Blotto is a zero-sum game. However, the num- the original game. ber of strategies in the Colonel Blotto game is exponential n+k−1 Our Contribution in its natural representation. There are k−1 ways to par- tition n troops among k battleﬁelds. The classical methods We ﬁrst show how to compute equilibria of the Colonel for computing the equilibra of a zero-sum game therefore Blotto game. Like the works described above, our method is do not yield computationally efﬁcient results. Moreover, sig- to consider a payoff-equivalent bilinear game deﬁned over niﬁcant effort has been made in the economics literature to a space of appropriately-selected marginals (in this case, understand the structure of equilibria of the Colonel Blotto the distribution of soldiers to a given battleﬁeld). However, game, i.e., by solving for equilibrium explicitly (Tukey unlike those works, we do not explicitly construct a game- 1949; Kvasov 2007; Hart 2007; Golman and Page 2009; speciﬁc mapping to and from a polynomially-sized bilinear Kovenock and Roberson 2012). Despite this effort, progress game. We instead use a more general reduction, based on remains sparse. Much of the existing work considers a con- the idea that it sufﬁces to solve linear optimization queries tinuous relaxation of the problem where troops are divisible, over strategy proﬁles in a (potentially exponentially-sized) and for this relaxation a signiﬁcant breakthrough came only bilinear game. In other words, equilibrium computation re- quite recently in the seminal work of Roberson (Roberson duces to the problem of ﬁnding a strategy that optimizes a 2006), 85 years after the introduction of the game. Rober- given linear function over its marginal components. We ap- son ﬁnds an equilibrium solution for the continuous version ply our reduction to the Colonel Blotto game by showing of the game, in the special case that all battleﬁelds have how to solve these requisite optimization queries, which can the same weight. The more general weighted version of the be done via dynamic programming. problem remains open, as does the original non-relaxed ver- The reduction described above follows from a repeated sion with discrete strategies. Given the apparent difﬁculty of application of the classic equivalence of separation and opti- solving for equilibrium explicitly, it is natural to revisit the mization (Grotschel,¨ Lovasz,´ and Schrijver 1981). In more equilibrium computation problem for Colonel Blotto games. detail, we formulate the equilibrium conditions as an LP whose feasibility region is the intersection of two polytopes: the ﬁrst corresponding to the set of strategies of player 1, An Approach: Bilinear Games How should one ap- and the second encoding payoff constraints for player 2. To proach equilibrium computation in such a game? The ex- ﬁnd a solution of the LP via Ellipsoid method, it sufﬁces to ponential size of the strategy set is not an impassable bar- design a separation oracle for each polytope. However, as rier; in certain cases, games with exponentially many strate- we show, separation oracles for the second polytope reduce gies have an underlying structure that can be used to ap- to (and from) separation oracles for the set of strategies of proach the equilibrium computation problem. For exam- player 2. It therefore sufﬁces to design separation oracles for ple, Koller, Megiddo and von Stengel (Koller, Megiddo, the polytope of strategies for each player, and for this it is and Von Stengel 1994) show how to compute equilibria enough to perform linear optimization over those polytopes for zero-sum extensive-form games with perfect recall. Im- (Grotschel,¨ Lovasz,´ and Schrijver 1981). Finally, to convert morlica et al. (Immorlica et al. 2011) give an approach for back to an equilibrium of the original game, we make use solving algorithmically-motivated “dueling games” with un- of a result from combinatorial optimization: the solution of certainty. Letchford and Conitzer (Letchford and Conitzer an LP with polynomially many variables can always be ex- 2013) compute equilibria for a variety of graphical secu- pressed as a mixed strategy with a polynomial-size support, rity games. Each of these cases involve games with expo- and such a mixed strategy can be computed using the sepa- nentially many strategies. In each case, a similar approach ration oracles described previously (Grotschel,¨ Lovasz,´ and is employed: reformulating the original game as a payoff- Schrijver 1981). equivalent bilinear game. In a bilinear game, the space of The reduction described above is not speciﬁc to the strategies forms a polytope in Rn, and payoffs are speci- Colonel Blotto game: it applies to any zero-sum game, and ﬁed by a matrix M: if the players play strategies x and y any payoff-equivalent bilinear form thereof. To the best respectively, then the payoff to player 1 is xT My. It has of our knowledge, this general reduction from equilibrium

370 computation to linear optimization has not previously been non-negative real numbers, subject to the constraint that its stated explicitly, although it has been alluded to in the se- expectation must equal a certain ﬁxed value. A value is curity games literature2 and similar ideas have been used to then drawn from each player’s chosen distribution; the play- compute correlated equilibria in compact games (Jiang and ers’ payoffs are then functions of these values. What is in- Leyton-Brown 2015). In particular, it is notable that one re- teresting about this game is that there are inﬁnitely many quires only a single linear optimization oracle, over the set pure strategies, which complicates equilibrium computation. of pure strategies, to both ﬁnd an equilibrium of the bilinear Nevertheless, we show that our techniques can be applied game and convert this to a mixed equilibrium in the orig- to this class of games as well, yielding a polynomial-time inal game. We demonstrate the generality of this approach algorithm for computing Nash equilibria. It is worth men- by considering notable examples of games to which it can tioning that the General Lotto game is an important problem be applied. In each case, our approach either results in the by itself, and its continuous variant has been well studied ﬁrst known polytime algorithm for computing equilibria, or in the literature (see, for example, (Bell and Cover 1980; else signiﬁcantly simpliﬁes prior analysis. Finally, we note Sahuguet and Persico 2006; Hart 2007; Dziubinski´ 2011)). that our approach also extends to approximations: given the ability to approximately answer separation oracle queries to Results and Techniques within any ﬁxed error >0, one can compute a correspond- ing approximation to the equilibrium payoffs. We present a general method for computing Nash equilibria of a broad class of zero-sum games. Our approach is to re- duce the problem of computing equilibria of a given game Dueling Games In a dueling game, introduced by Im- to the problem of optimizing linear functions over the space morlica et al. (Immorlica et al. 2011), two competitors of strategies in a payoff-equivalent bilinear game. each try to design an algorithm for an optimization prob- Before presenting our general reduction, we will ﬁrst il- lem with an element of uncertainty, and each player’s pay- lustrate our techniques by considering the Colonel Blotto off is the probability of obtaining a better solution. This game as a speciﬁc example. In the following section we de- framework falls within a natural class of ranking or so- scribe our approach in detail for the Colonel Blotto game, cial context games (Ashlagi, Krysta, and Tennenholtz 2008; explaining the process by which equilibria can be computed. Brandt et al. 2009), in which players separately play a base Then we will present the general reduction. Further applica- game and then receive ultimate payoffs determined by both tions of this technique are provided in the full version of the their own outcomes and the outcomes of others. Immorlica paper (for the General Lotto game). et al. argue that this class of games models a variety of sce- narios of competitions between algorithm designers: for ex- Colonel Blotto ample, competition between search engines who must rank search results, or competition between hiring managers who Here, we propose a polynomial-time algorithm for ﬁnding must choose from a pool of candidates in the style of the an equilibrium of discrete Colonel Blotto in its general form. secretary problem. We allow the game to be asymmetric across both the battle- Immorlica et al. (Immorlica et al. 2011) show how to com- ﬁelds and the players. A game is asymmetric across the bat- pute a Nash equilibrium for certain dueling games, by de- tleﬁelds when different battleﬁelds have different contribu- veloping mappings to and from bilinear games with com- tions to the outcome of the game, and a game is asymmetric pact representations. We extend their method, and show how across the players when two players have different number to expand the class of dueling games for which equilib- of troops. A B ria can be efﬁciently computed. As one particular example, In the Colonel Blotto game, two players and si- a b we introduce and solve the matching duel. In this game, multaneously distribute and troops, respectively, over k battleﬁelds. A pure strategy of player A is a k-partition two players each select a matching in a weighted graph, k and each player’s payoff is the probability that a randomly x = x1,x2,...,xk where i=1 xi = a, and a pure strat- B k y y ,y ,...,y selected node would have a higher-weight match in that egy of player is a -partition = 1 2 k where k A B player’s matching than in the opponent’s. Notably, since the i=1 yi = b. Let ui (xi,yi) and ui (xi,yi) be the pay- matching polytope does not have a compact representation off of player A and player B from the i-th battleﬁeld, re- (Rothvoss 2014), the original method of (Immorlica et al. spectively. Note that the payoff functions of the i-th bat- A B 2011) is not sufﬁcient to ﬁnd equilibria of this game. We also tleﬁeld, ui and ui ,have(a +1)× (b +1)entries. This illustrate that our approach admits a signiﬁcantly simpliﬁed means the size of input is Θ(kab). Since Colonel Blotto A B 3 analysis for some other dueling games previously analyzed is a zero-sum game, we have ui (xi,yi)=−ui (xi,yi) . by Immorlica et al. Note that we do not need to put any constraint on the payoff functions, and our result works for all payoff functions. We General Lotto Game Hart (Hart 2007) considers a vari- also represent the total payoff of player A and player B by hA x, y uA x ,y hB x, y uB x ,y ant of the Colonel Blotto game, namely the General Lotto B ( )= i i ( i i) and B ( )= i i ( i i), game. In this game, each player chooses a distribution over 3 A Note that in the Colonel Blotto game if ui (xi,yi) is not nec- 2 B Independently and in parallel with an earlier version of this essarily equal to −ui (xi,yi) then a special case of this game with work, (Xu et al. 2014) implicitly used a similar idea to solve a class two battleﬁelds can model an arbitrary 2-person normal-form game of Stackleberg security games. and thus ﬁnding a Nash Equilibrium would be PPAD-complete.

371 n(B) respectively. A mixed strategy of each player would be a and deﬁne IB = {ˆy ∈{0, 1} |∃y ∈Y, GB(y)=ˆy} and n(B) probability distribution over his pure strategies. SB = {ˆy ∈ [0, 1] |∃y ∈M(Y), GB(y)=ˆy}. Theorem 1 One can compute an equilibrium of any Lemma 0.1 The set SA forms a convex polyhedron with an Colonel Blotto game in polynomial time. exponential number of vertices and facets. Proof: Let X and Y be the set of all pure strategies of play- Now, we are ready to rewrite linear program 1 in the new ers A and B respectively, i.e., each member of X is a k- space as follows. partition of a troops and each member of Y is a k-partition max U (2) b A of troops. We represent a mixed strategy of player with s.t. ˆx ∈ SA (Membership constraint) function p : X→[0, 1] such that x∈X p(x)=1. Sim- A hB (ˆx, ˆy) ≥ U, ∀ˆy ∈ IB (Payoff constraints) ilarly, let function q : Y→[0, 1] be a mixed strategy of player B. We may also use x and y, instead of p and q, for where referring to a mixed strategy of player A and B respectively. k a b Since Colonel Blotto is a zero-sum game, we leverage the A A hB (ˆx, ˆy)= xˆi,t yˆi,t ui (ta,tb) MinMax theorem for ﬁnding an NE of the game. This the- a b i=1 ta=0 tb=0 orem says that pair (p∗,q∗) is an NE of the Colonel Blotto game if and only if strategies p∗ and q∗ maximize the guar- is the expected payoff of player A. anteed payoff of players A and B respectively (Sion 1957). Step 2: Solving LP 2. The modiﬁed LP above, LP 2, has ex- Now, we are going to ﬁnd strategy p∗ of player A which ponentially many constraints, but only polynomially many maximizes his guaranteed payoff. The same technique can variables. One can therefore apply the Ellipsoid method be used for ﬁnding q∗. For each mixed strategy p, at least to solve the LP, given a separation oracle that runs in one of the best-response strategies of p is a pure strategy. polynomial time (Grotschel,¨ Lovasz,´ and Schrijver 1988; Therefore, a solution to the following program characterizes Papadimitriou and Steiglitz 1998). By the equivalence of strategy p∗. separation and optimization (Grotschel,¨ Lovasz,´ and Schri- jver 1981), one can implement such a separation oracle U max (1) given the ability to optimize linear functions over the poly- s.t. px =1, topes SA (for the membership constraints) and SB (for the x∈X A payoff constraints). pxh (x, y) ≥ U, ∀y ∈Y, x∈X B Stated more explicitly, given a sequence of real numbers Unfortunately, LP 1 has |X | variables and |Y|+1 constraints c0,c1,...,ck(m+1), where k is the number of battleﬁelds where |X | and |Y| +1are exponential. We therefore cannot and m is the number of troops for a player, the required ora- solve LP 1 directly. cle must ﬁnd a pure strategy x =(x1,x2,...,xk) ∈Xsuch k Step 1: Transferring to a new space. We address this is- that i=1 xi = m, and ˆx = G(x) minimizes the following sue by transforming the solution space to a new space in equation: which an LP equivalent to LP 1 becomes tractable (See, e.g., k(m+1) (Azar et al. 2003), for similar technique). This new space c0 + cixˆi, (3) will project mixed strategies onto the marginal probabilities i=1 for each (battleﬁeld, troop count) pair. For each pure strategy Y n(A) and similarly for polytope . The following lemma shows x ∈Xof player A, we map it to a point in {0, 1} where that one can indeed ﬁnd a minimizer of Equation (3) in poly- n(A)=k ×(a+1). For convenience, we may abuse the no- nomial time. tation, and index each point ˆx ∈{0, 1}n(A) by two indices Lemma 0.2 Given two integers m and k and a sequence i and j such that xˆi,j represents xˆ(i−1)(a+1)+j+1.Nowwe c0,c1,...,ck(m+1), one can ﬁnd (in polynomial time) an op- x G x ˆx ∈{ , }n(A) map a pure strategy to A( )= 0 1 such that x x ,x ,...,x k x x x j A timal pure strategy =( 1 2 k) where i=1 i = ˆi,j =1if and only if i = . In other words, if player k(m+1) puts j troops in the i-th battleﬁeld then xˆi,j =1. Let IA = m, ˆx = G(x) and ˆx minimizes c0 + i=1 cixˆi. n(A) {ˆx ∈{0, 1} |∃x ∈X, GA(x)=ˆx} be the set of points Proof: We employ a dynamic programming approach. in {0, 1}n(A) which represent pure strategies of player A. d i, t c Deﬁne [ ] as the minimum possible value of 0 + Let M(X ) and M(Y) be the set of mixed strategies of play- i(t+1) i i=1 ci xˆi where i=1 xi = t. Hence, d[k, m] denotes ers A and B, respectively. Similarly, we map mixed strategy k(m+1) n(A) the minimum possible value of c0 + i=1 cixˆi.Wehave x to point GA(x)=ˆx ∈ [0, 1] such that xˆi,j represents that d[0,j] is equal to c0 for all j. For an arbitrary i>0 and the probability that mixed strategy x puts j troops in the i-th t, the optimal strategy x puts 0 ≤ t ≤ t units in the i-th battleﬁeld. Note that mapping GA is not necessarily one-to- battleﬁeld and the applied cost in the equation 3 is equal to one nor onto, i.e., each point in [0, 1]n(A) may be mapped c(i−1)(m+1)+t+1. Thus, we can express d[i, t] as to zero, one, or more than one strategies. Let SA = {ˆx ∈ n(A) [0, 1] |∃x ∈M(X ), GA(x)=ˆx} be the set of points d[i, t]= min {d[i − 1,t− t ]+c(i−1)(m+1)+t+1}. 0≤t≤t in [0, 1]n(A) which represent at least one mixed strategy of player A. Similarly, we use function GB to map each strategy Solving this dynamic program, we can ﬁnd the allocation n(B) of player B to a point in [0, 1] where n(B)=k×(b+1), that minimizes αiˆxi in polynomial time, as required.

372 Step 3: Transferring to the original space. At last we should Theorem 3 Given an oracle function for the -separation transfer the solution of LP 2 to the original space. In partic- problem, one can ﬁnd an -approximation to the equilib- ular, we are given a point ˆx ∈ SA and our goal is to ﬁnd rium payoffs of a polynomially separable game in polyno- a strategy x ∈M(X ) such that GA(x)=ˆx. To achieve mial time. this, we invoke a classic result of (Grotschel,¨ Lovasz,´ and Schrijver 1981) which states that an interior point of an n- General Lotto dimensional polytope P can be decomposed as a convex The General Lotto game is a relaxation of the Colonel combination of at most n +1extreme points of P , in poly- Lotto game (See (Hart 2007) for details). In this game each nomial time, given an oracle that optimizes linear functions player’s strategy is a distribution of a nonnegative integer- over P . Note that this is precisely the oracle required for valued random variable with a given expectation. In partic- Step 2, above. Applying this result to the solution of LP 2 ular, players A and B simultaneously determine (two distri- in polytope SA, we obtain a convex decomposition of ˆx into butions of) two nonnegative integer-valued random variables extreme points of SA, say ˆx = i αiˆxi. Since each ˆxi cor- X and Y , respectively, such that E[X]=a and E[Y ]=b. responds to a pure strategy in X , it is trivial to ﬁnd point The payoff of player A is xi with GA(xi)=ˆxi, since the marginals of each ˆxi lie ∞ ∞ in {0, 1}. We then have that x = αixi is the required A i h (X, Y )= Pr(X = i)Pr(Y = j)u(i, j), mixed strategy proﬁle. Γ (4) i=0 j=0 Combining these three steps, we ﬁnd a Nash Equilibrium of the Colonel Blotto game in polynomial time. and again the payoff of player B is the negative of the pay- B A off of player A, i.e., hΓ (X, Y )=−hΓ (X, Y ). Hart (Hart A general framework for bilinear games 2007) presents a solution for the General Lotto game when u(i, j) = sign(i − j) In our method for ﬁnding a Nash Equilibrium of the Colonel . Here, we generalize this result and Blotto game, the main steps were to express the game as a present a polynomial-time algorithm for ﬁnding an equi- librium when u is a bounded distance function. Function bilinear game of polynomial dimension, solve for an equi- u librium of the bilinear game, then express that point as an is a bounded distance function, if one can write it as u(i, j)=fu(i − j) such that fu is a monotone function equilibrium of the original game. To implement the ﬁnal M T u = fu(u ) two steps, it sufﬁced to show how to optimize linear func- and reaches its maximum value at where uT ∈ O(poly(a, b)) u(i, j) = sign(i − j) tions over the polytope of strategies in the bilinear game. . Note that is This suggests a general reduction, where the equilibrium a bounded distance function where it reaches its maximum i − j =1 computation problem is reduced to ﬁnding the appropriate value at . Now, we are ready to present our main bilinear game and implementing the required optimization result regarding the General Lotto game. algorithm. In other words, the method for computing Nash Theorem 4 There is a polynomial-time algorithm which equilibria applies to a zero-sum game when: ﬁnds an equilibrium of a General Lotto game where the pay- off function is a bounded distance function. 1. One can transfer each strategy x of player A to GA(x)= n(A) Main challenge Note that in the General Lotto game, each ˆx ∈ R , and each strategy y of player B to GB(y)= n(B) player has an inﬁnite number of pure strategies, and thus ˆy ∈ R such that the payoff of strategies ˆx and ˆy can one cannot use either our proposed algorithm for the Colonel be represented in a bilinear form based on ˆx and ˆy. Blotto game or the technique of (Immorlica et al. 2011) for 2. For any given vector α and real number α0 we can ﬁnd, solving the problem. We should prune strategies such that in polynomial time, whether there is a pure strategy ˆx in the problem becomes tractable. Therefore, we characterize the transferred space such that α0 + αixˆi ≥ 0. i the extreme point of the polytope of all strategies, and use We refer to such a game as polynomially separable. A di- this characterization for pruning possible strategies. rect extension of the proof of Theorem 1 implies that Nash To the best of our knowledge, our algorithm is the ﬁrst equilibria can be found for polynomially separable games. algorithm of this kind which computes an NE of a game with inﬁnite number of pure strategies. Theorem 2 There is a polytime algorithm which ﬁnds a Nash Equilibrium of a given polynomially separable game. Application to Dueling Games This general methodology can be used for ﬁnding a NE Immorlica et al. (Immorlica et al. 2011) introduced the class in many zero-sum games. In subsequent sections, we show of dueling games. In these games, an optimization problem how our framework can be used to ﬁnd Nash equilibria for with an element of uncertainty is considered as a competi- a generalization of Blotto games, known as General Lotto tion between two players. They also provide a technique for games, and for a class of dueling games introduced by Im- ﬁnding Nash equilibria for a set of games in this class. In morlica et al. (Immorlica et al. 2011). this section, we formally deﬁne the dueling games and bi- We also show one can use similar techniques to com- linear duels. Then, we describe our method and show that pute the approximate equilibrium payoffs of a dueling game our technique solves a more general class of dueling games. when we are not able to answer the separation problem in Furthermore, we provide examples to show how our method polynomial time but instead we can polynomially solve the can be a simpler tool for solving bilinear duel games com- -separation problem for any >0. pared to (Immorlica et al. 2011) method. Finally, in Section

373 , we examine the matching duel game to provide an example In the following theorem, we show that every polynomi- where the method of (Immorlica et al. 2011) does not work, ally representable bilinear duel is also polynomially separa- but our presented method can yet be applied. ble. This implies we can use the general reduction to solve polynomially representable bilinear dueling games as well. Dueling games Theorem 5 Every polynomially-representable bilinear du- Formally, dueling games are two player zero-sum games eling game is polynomially separable. with a set of strategies X, a set of possible situations Ω, a probability distribution p over Ω, and a cost function Matching duel c X × → R : Ω that deﬁnes the cost measure for each player In a matching duel we are given a weighted graph G = based on her strategy and the element of uncertainty. The (V,E,W) which is not necessarily bipartite. In a matching payoff of each player is deﬁned as the probability that she duel each pure strategy of players is a perfect matching, set beats her opponent minus the probability that she is beaten. of possible situations Ω is the same as the set of nodes in G, More precisely, the utility function is deﬁned as and probability distribution p over Ω determines the prob- x hA(x, y)=−hB(x, y)= ability of selection of each node. In this game, strategy beats strategy y for element ω ∈ Ω if ω is matched to a Pr [c(x, ω)

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