76 research outputs found
Winning Cores in Parity Games
We introduce the novel notion of winning cores in parity games and develop a
deterministic polynomial-time under-approximation algorithm for solving parity
games based on winning core approximation. Underlying this algorithm are a
number properties about winning cores which are interesting in their own right.
In particular, we show that the winning core and the winning region for a
player in a parity game are equivalently empty. Moreover, the winning core
contains all fatal attractors but is not necessarily a dominion itself.
Experimental results are very positive both with respect to quality of
approximation and running time. It outperforms existing state-of-the-art
algorithms significantly on most benchmarks
New Deterministic Algorithms for Solving Parity Games
We study parity games in which one of the two players controls only a small
number of nodes and the other player controls the other nodes of the
game. Our main result is a fixed-parameter algorithm that solves bipartite
parity games in time , and general parity games in
time , where is the number of distinct
priorities and is the number of edges. For all games with this
improves the previously fastest algorithm by Jurdzi{\'n}ski, Paterson, and
Zwick (SICOMP 2008). We also obtain novel kernelization results and an improved
deterministic algorithm for graphs with small average degree
The tropical shadow-vertex algorithm solves mean payoff games in polynomial time on average
We introduce an algorithm which solves mean payoff games in polynomial time
on average, assuming the distribution of the games satisfies a flip invariance
property on the set of actions associated with every state. The algorithm is a
tropical analogue of the shadow-vertex simplex algorithm, which solves mean
payoff games via linear feasibility problems over the tropical semiring
. The key ingredient in our approach is
that the shadow-vertex pivoting rule can be transferred to tropical polyhedra,
and that its computation reduces to optimal assignment problems through
Pl\"ucker relations.Comment: 17 pages, 7 figures, appears in 41st International Colloquium, ICALP
2014, Copenhagen, Denmark, July 8-11, 2014, Proceedings, Part
Symmetric Strategy Improvement
Symmetry is inherent in the definition of most of the two-player zero-sum
games, including parity, mean-payoff, and discounted-payoff games. It is
therefore quite surprising that no symmetric analysis techniques for these
games exist. We develop a novel symmetric strategy improvement algorithm where,
in each iteration, the strategies of both players are improved simultaneously.
We show that symmetric strategy improvement defies Friedmann's traps, which
shook the belief in the potential of classic strategy improvement to be
polynomial
A parallel algorithm for the enumeration of benzenoid hydrocarbons
We present an improved parallel algorithm for the enumeration of fixed
benzenoids B_h containing h hexagonal cells. We can thus extend the enumeration
of B_h from the previous best h=35 up to h=50. Analysis of the associated
generating function confirms to a very high degree of certainty that and we estimate that the growth constant and the amplitude .Comment: 14 pages, 6 figure
Local Strategy Improvement for Parity Game Solving
The problem of solving a parity game is at the core of many problems in model
checking, satisfiability checking and program synthesis. Some of the best
algorithms for solving parity game are strategy improvement algorithms. These
are global in nature since they require the entire parity game to be present at
the beginning. This is a distinct disadvantage because in many applications one
only needs to know which winning region a particular node belongs to, and a
witnessing winning strategy may cover only a fractional part of the entire game
graph.
We present a local strategy improvement algorithm which explores the game
graph on-the-fly whilst performing the improvement steps. We also compare it
empirically with existing global strategy improvement algorithms and the
currently only other local algorithm for solving parity games. It turns out
that local strategy improvement can outperform these others by several orders
of magnitude
Improving Strategies via SMT Solving
We consider the problem of computing numerical invariants of programs by
abstract interpretation. Our method eschews two traditional sources of
imprecision: (i) the use of widening operators for enforcing convergence within
a finite number of iterations (ii) the use of merge operations (often, convex
hulls) at the merge points of the control flow graph. It instead computes the
least inductive invariant expressible in the domain at a restricted set of
program points, and analyzes the rest of the code en bloc. We emphasize that we
compute this inductive invariant precisely. For that we extend the strategy
improvement algorithm of [Gawlitza and Seidl, 2007]. If we applied their method
directly, we would have to solve an exponentially sized system of abstract
semantic equations, resulting in memory exhaustion. Instead, we keep the system
implicit and discover strategy improvements using SAT modulo real linear
arithmetic (SMT). For evaluating strategies we use linear programming. Our
algorithm has low polynomial space complexity and performs for contrived
examples in the worst case exponentially many strategy improvement steps; this
is unsurprising, since we show that the associated abstract reachability
problem is Pi-p-2-complete
Oink: an Implementation and Evaluation of Modern Parity Game Solvers
Parity games have important practical applications in formal verification and
synthesis, especially to solve the model-checking problem of the modal
mu-calculus. They are also interesting from the theory perspective, as they are
widely believed to admit a polynomial solution, but so far no such algorithm is
known. In recent years, a number of new algorithms and improvements to existing
algorithms have been proposed. We implement a new and easy to extend tool Oink,
which is a high-performance implementation of modern parity game algorithms. We
further present a comprehensive empirical evaluation of modern parity game
algorithms and solvers, both on real world benchmarks and randomly generated
games. Our experiments show that our new tool Oink outperforms the current
state-of-the-art.Comment: Accepted at TACAS 201
Efficient Parallel Strategy Improvement for Parity Games
We study strategy improvement algorithms for solving parity games. While these algorithms are known to solve parity games using a very small number of iterations, experimental studies have found that a high step complexity causes them to perform poorly in practice. In this paper we seek to address this situation. Every iteration of the algorithm must compute a best response, and while the standard way of doing this uses the Bellman-Ford algorithm, we give experimental results that show that one-player strategy improvement significantly outperforms this technique in practice. We then study the best way to implement one-player strategy improvement, and we develop an efficient parallel algorithm for carrying out this task, by reducing the problem to computing prefix sums on a linked list. We report experimental results for these algorithms, and we find that a GPU implementation of this algorithm shows a significant speedup over single-core and multi-core CPU implementations
Semiperfect-Information Games
Abstract. Much recent research has focused on the applications of games with!-regular objectives in the control and verication of reactive systems. However, many of the game-based models are ill-suited for these applications, because they assume that each player has complete infor-mation about the state of the system (they are \perfect-information" games). This is because in many situations, a controller does not see the private state of the plant. Such scenarios are naturally modeled by \partial-information " games. On the other hand, these games are in-tractable; for example, partial-information games with simple reachabil-ity objectives are 2EXPTIME-complete. We study the intermediate case of \semiperfect-information " games, where one player has complete knowledge of the state, while the other player has only partial knowledge. This model is appropriate in con-trol situations where a controller must cope with plant behavior that is as adversarial as possible, i.e., the controller has partial informa-tion while the plant has perfect information. As is customary, we as-sume that the controller and plant take turns to make moves. We show that these semiperfect-information turn-based games are equiv-alent to perfect-information concurrent games, where the two play-ers choose their moves simultaneously and independently. Since the perfect-information concurrent games are well-understood, we obtain several results of how semiperfect-information turn-based games dif-fer from perfect-information turn-based games on one hand, and from partial-information turn-based games on the other hand. In particular, semiperfect-information turn-based games can benet from randomized strategies while the perfect-information variety cannot, and semiperfect-information turn-based games are in NP \ coNP for all parity objectives.
- …