961 research outputs found
Hyperplane Separation Technique for Multidimensional Mean-Payoff Games
We consider both finite-state game graphs and recursive game graphs (or
pushdown game graphs), that can model the control flow of sequential programs
with recursion, with multi-dimensional mean-payoff objectives. In pushdown
games two types of strategies are relevant: global strategies, that depend on
the entire global history; and modular strategies, that have only local memory
and thus do not depend on the context of invocation. We present solutions to
several fundamental algorithmic questions and our main contributions are as
follows: (1) We show that finite-state multi-dimensional mean-payoff games can
be solved in polynomial time if the number of dimensions and the maximal
absolute value of the weight is fixed; whereas if the number of dimensions is
arbitrary, then problem is already known to be coNP-complete. (2) We show that
pushdown graphs with multi-dimensional mean-payoff objectives can be solved in
polynomial time. (3) For pushdown games under global strategies both single and
multi-dimensional mean-payoff objectives problems are known to be undecidable,
and we show that under modular strategies the multi-dimensional problem is also
undecidable (whereas under modular strategies the single dimensional problem is
NP-complete). We show that if the number of modules, the number of exits, and
the maximal absolute value of the weight is fixed, then pushdown games under
modular strategies with single dimensional mean-payoff objectives can be solved
in polynomial time, and if either of the number of exits or the number of
modules is not bounded, then the problem is NP-hard. (4) Finally we show that a
fixed parameter tractable algorithm for finite-state multi-dimensional
mean-payoff games or pushdown games under modular strategies with
single-dimensional mean-payoff objectives would imply the solution of the
long-standing open problem of fixed parameter tractability of parity games.Comment: arXiv admin note: text overlap with arXiv:1201.282
Finitary languages
The class of omega-regular languages provides a robust specification language
in verification. Every omega-regular condition can be decomposed into a safety
part and a liveness part. The liveness part ensures that something good happens
"eventually". Finitary liveness was proposed by Alur and Henzinger as a
stronger formulation of liveness. It requires that there exists an unknown,
fixed bound b such that something good happens within b transitions. In this
work we consider automata with finitary acceptance conditions defined by
finitary Buchi, parity and Streett languages. We study languages expressible by
such automata: we give their topological complexity and present a
regular-expression characterization. We compare the expressive power of
finitary automata and give optimal algorithms for classical decisions
questions. We show that the finitary languages are Sigma 2-complete; we present
a complete picture of the expressive power of various classes of automata with
finitary and infinitary acceptance conditions; we show that the languages
defined by finitary parity automata exactly characterize the star-free fragment
of omega B-regular languages; and we show that emptiness is NLOGSPACE-complete
and universality as well as language inclusion are PSPACE-complete for finitary
parity and Streett automata
Energy Parity Games
Energy parity games are infinite two-player turn-based games played on
weighted graphs. The objective of the game combines a (qualitative) parity
condition with the (quantitative) requirement that the sum of the weights
(i.e., the level of energy in the game) must remain positive. Beside their own
interest in the design and synthesis of resource-constrained omega-regular
specifications, energy parity games provide one of the simplest model of games
with combined qualitative and quantitative objective. Our main results are as
follows: (a) exponential memory is necessary and sufficient for winning
strategies in energy parity games; (b) the problem of deciding the winner in
energy parity games can be solved in NP \cap coNP; and (c) we give an algorithm
to solve energy parity by reduction to energy games. We also show that the
problem of deciding the winner in energy parity games is polynomially
equivalent to the problem of deciding the winner in mean-payoff parity games,
while optimal strategies may require infinite memory in mean-payoff parity
games. As a consequence we obtain a conceptually simple algorithm to solve
mean-payoff parity games
POMDPs under Probabilistic Semantics
We consider partially observable Markov decision processes (POMDPs) with
limit-average payoff, where a reward value in the interval [0,1] is associated
to every transition, and the payoff of an infinite path is the long-run average
of the rewards. We consider two types of path constraints: (i) quantitative
constraint defines the set of paths where the payoff is at least a given
threshold lambda_1 in (0,1]; and (ii) qualitative constraint which is a special
case of quantitative constraint with lambda_1=1. We consider the computation of
the almost-sure winning set, where the controller needs to ensure that the path
constraint is satisfied with probability 1. Our main results for qualitative
path constraint are as follows: (i) the problem of deciding the existence of a
finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding
the existence of an infinite-memory controller is undecidable. For quantitative
path constraint we show that the problem of deciding the existence of a
finite-memory controller is undecidable.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Graph Planning with Expected Finite Horizon
Graph planning gives rise to fundamental algorithmic questions such as
shortest path, traveling salesman problem, etc. A classical problem in discrete
planning is to consider a weighted graph and construct a path that maximizes
the sum of weights for a given time horizon . However, in many scenarios,
the time horizon is not fixed, but the stopping time is chosen according to
some distribution such that the expected stopping time is . If the stopping
time distribution is not known, then to ensure robustness, the distribution is
chosen by an adversary, to represent the worst-case scenario.
A stationary plan for every vertex always chooses the same outgoing edge. For
fixed horizon or fixed stopping-time distribution, stationary plans are not
sufficient for optimality. Quite surprisingly we show that when an adversary
chooses the stopping-time distribution with expected stopping time , then
stationary plans are sufficient. While computing optimal stationary plans for
fixed horizon is NP-complete, we show that computing optimal stationary plans
under adversarial stopping-time distribution can be achieved in polynomial
time. Consequently, our polynomial-time algorithm for adversarial stopping time
also computes an optimal plan among all possible plans
Obligation Blackwell Games and p-Automata
We recently introduced p-automata, automata that read discrete-time Markov
chains. We used turn-based stochastic parity games to define acceptance of
Markov chains by a subclass of p-automata. Definition of acceptance required a
cumbersome and complicated reduction to a series of turn-based stochastic
parity games. The reduction could not support acceptance by general p-automata,
which was left undefined as there was no notion of games that supported it.
Here we generalize two-player games by adding a structural acceptance
condition called obligations. Obligations are orthogonal to the linear winning
conditions that define winning. Obligations are a declaration that player 0 can
achieve a certain value from a configuration. If the obligation is met, the
value of that configuration for player 0 is 1.
One cannot define value in obligation games by the standard mechanism of
considering the measure of winning paths on a Markov chain and taking the
supremum of the infimum of all strategies. Mainly because obligations need
definition even for Markov chains and the nature of obligations has the flavor
of an infinite nesting of supremum and infimum operators. We define value via a
reduction to turn-based games similar to Martin's proof of determinacy of
Blackwell games with Borel objectives. Based on this definition, we show that
games are determined. We show that for Markov chains with Borel objectives and
obligations, and finite turn-based stochastic parity games with obligations
there exists an alternative and simpler characterization of the value function.
Based on this simpler definition we give an exponential time algorithm to
analyze finite turn-based stochastic parity games with obligations. Finally, we
show that obligation games provide the necessary framework for reasoning about
p-automata and that they generalize the previous definition
Qualitative Analysis of Concurrent Mean-payoff Games
We consider concurrent games played by two-players on a finite-state graph,
where in every round the players simultaneously choose a move, and the current
state along with the joint moves determine the successor state. We study a
fundamental objective, namely, mean-payoff objective, where a reward is
associated to each transition, and the goal of player 1 is to maximize the
long-run average of the rewards, and the objective of player 2 is strictly the
opposite. The path constraint for player 1 could be qualitative, i.e., the
mean-payoff is the maximal reward, or arbitrarily close to it; or quantitative,
i.e., a given threshold between the minimal and maximal reward. We consider the
computation of the almost-sure (resp. positive) winning sets, where player 1
can ensure that the path constraint is satisfied with probability 1 (resp.
positive probability). Our main results for qualitative path constraints are as
follows: (1) we establish qualitative determinacy results that show that for
every state either player 1 has a strategy to ensure almost-sure (resp.
positive) winning against all player-2 strategies, or player 2 has a spoiling
strategy to falsify almost-sure (resp. positive) winning against all player-1
strategies; (2) we present optimal strategy complexity results that precisely
characterize the classes of strategies required for almost-sure and positive
winning for both players; and (3) we present quadratic time algorithms to
compute the almost-sure and the positive winning sets, matching the best known
bound of algorithms for much simpler problems (such as reachability
objectives). For quantitative constraints we show that a polynomial time
solution for the almost-sure or the positive winning set would imply a solution
to a long-standing open problem (the value problem for turn-based deterministic
mean-payoff games) that is not known to be solvable in polynomial time
- …