41,681 research outputs found
Quantitative games with interval objectives
Traditionally quantitative games such as mean-payoff games and discount sum
games have two players -- one trying to maximize the payoff, the other trying
to minimize it. The associated decision problem, "Can Eve (the maximizer)
achieve, for example, a positive payoff?" can be thought of as one player
trying to attain a payoff in the interval . In this paper we
consider the more general problem of determining if a player can attain a
payoff in a finite union of arbitrary intervals for various payoff functions
(liminf, mean-payoff, discount sum, total sum). In particular this includes the
interesting exact-value problem, "Can Eve achieve a payoff of exactly (e.g.)
0?"Comment: Full version of CONCUR submissio
Magnifying Lens Abstraction for Stochastic Games with Discounted and Long-run Average Objectives
Turn-based stochastic games and its important subclass Markov decision
processes (MDPs) provide models for systems with both probabilistic and
nondeterministic behaviors. We consider turn-based stochastic games with two
classical quantitative objectives: discounted-sum and long-run average
objectives. The game models and the quantitative objectives are widely used in
probabilistic verification, planning, optimal inventory control, network
protocol and performance analysis. Games and MDPs that model realistic systems
often have very large state spaces, and probabilistic abstraction techniques
are necessary to handle the state-space explosion. The commonly used
full-abstraction techniques do not yield space-savings for systems that have
many states with similar value, but does not necessarily have similar
transition structure. A semi-abstraction technique, namely Magnifying-lens
abstractions (MLA), that clusters states based on value only, disregarding
differences in their transition relation was proposed for qualitative
objectives (reachability and safety objectives). In this paper we extend the
MLA technique to solve stochastic games with discounted-sum and long-run
average objectives. We present the MLA technique based abstraction-refinement
algorithm for stochastic games and MDPs with discounted-sum objectives. For
long-run average objectives, our solution works for all MDPs and a sub-class of
stochastic games where every state has the same value
Incentive Stackelberg Mean-payoff Games
We introduce and study incentive equilibria for multi-player meanpayoff
games. Incentive equilibria generalise well-studied solution concepts such as
Nash equilibria and leader equilibria (also known as Stackelberg equilibria).
Recall that a strategy profile is a Nash equilibrium if no player can improve
his payoff by changing his strategy unilaterally. In the setting of incentive
and leader equilibria, there is a distinguished player called the leader who
can assign strategies to all other players, referred to as her followers. A
strategy profile is a leader strategy profile if no player, except for the
leader, can improve his payoff by changing his strategy unilaterally, and a
leader equilibrium is a leader strategy profile with a maximal return for the
leader. In the proposed case of incentive equilibria, the leader can
additionally influence the behaviour of her followers by transferring parts of
her payoff to her followers. The ability to incentivise her followers provides
the leader with more freedom in selecting strategy profiles, and we show that
this can indeed improve the payoff for the leader in such games. The key
fundamental result of the paper is the existence of incentive equilibria in
mean-payoff games. We further show that the decision problem related to
constructing incentive equilibria is NP-complete. On a positive note, we show
that, when the number of players is fixed, the complexity of the problem falls
in the same class as two-player mean-payoff games. We also present an
implementation of the proposed algorithms, and discuss experimental results
that demonstrate the feasibility of the analysis of medium sized games.Comment: 15 pages, references, appendix, 5 figure
Decision Problems for Nash Equilibria in Stochastic Games
We analyse the computational complexity of finding Nash equilibria in
stochastic multiplayer games with -regular objectives. While the
existence of an equilibrium whose payoff falls into a certain interval may be
undecidable, we single out several decidable restrictions of the problem.
First, restricting the search space to stationary, or pure stationary,
equilibria results in problems that are typically contained in PSPACE and NP,
respectively. Second, we show that the existence of an equilibrium with a
binary payoff (i.e. an equilibrium where each player either wins or loses with
probability 1) is decidable. We also establish that the existence of a Nash
equilibrium with a certain binary payoff entails the existence of an
equilibrium with the same payoff in pure, finite-state strategies.Comment: 22 pages, revised versio
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
Computer aided synthesis: a game theoretic approach
In this invited contribution, we propose a comprehensive introduction to game
theory applied in computer aided synthesis. In this context, we give some
classical results on two-player zero-sum games and then on multi-player non
zero-sum games. The simple case of one-player games is strongly related to
automata theory on infinite words. All along the article, we focus on general
approaches to solve the studied problems, and we provide several illustrative
examples as well as intuitions on the proofs.Comment: Invitation contribution for conference "Developments in Language
Theory" (DLT 2017
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Interval Markov decision processes (IMDPs) generalise classical MDPs by
having interval-valued transition probabilities. They provide a powerful
modelling tool for probabilistic systems with an additional variation or
uncertainty that prevents the knowledge of the exact transition probabilities.
In this paper, we consider the problem of multi-objective robust strategy
synthesis for interval MDPs, where the aim is to find a robust strategy that
guarantees the satisfaction of multiple properties at the same time in face of
the transition probability uncertainty. We first show that this problem is
PSPACE-hard. Then, we provide a value iteration-based decision algorithm to
approximate the Pareto set of achievable points. We finally demonstrate the
practical effectiveness of our proposed approaches by applying them on several
case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
Model Checking the Quantitative mu-Calculus on Linear Hybrid Systems
We study the model-checking problem for a quantitative extension of the modal
mu-calculus on a class of hybrid systems. Qualitative model checking has been
proved decidable and implemented for several classes of systems, but this is
not the case for quantitative questions that arise naturally in this context.
Recently, quantitative formalisms that subsume classical temporal logics and
allow the measurement of interesting quantitative phenomena were introduced. We
show how a powerful quantitative logic, the quantitative mu-calculus, can be
model checked with arbitrary precision on initialised linear hybrid systems. To
this end, we develop new techniques for the discretisation of continuous state
spaces based on a special class of strategies in model-checking games and
present a reduction to a class of counter parity games.Comment: LMCS submissio
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