18 research outputs found
IST Austria Technical Report
Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides efficient implementations of several reduction based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications
On computing fixpoints in well-structured regular model checking, with applications to lossy channel systems
We prove a general finite convergence theorem for "upward-guarded" fixpoint
expressions over a well-quasi-ordered set. This has immediate applications in
regular model checking of well-structured systems, where a main issue is the
eventual convergence of fixpoint computations. In particular, we are able to
directly obtain several new decidability results on lossy channel systems.Comment: 16 page
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
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
Gist: A Solver for Probabilistic Games
GIST is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications
Average Case Analysis of the Classical Algorithm for Markov Decision Processes with B\"uchi Objectives
We consider Markov decision processes (MDPs) with -regular
specifications given as parity objectives. We consider the problem of computing
the set of almost-sure winning vertices from where the objective can be ensured
with probability 1. The algorithms for the computation of the almost-sure
winning set for parity objectives iteratively use the solutions for the
almost-sure winning set for B\"uchi objectives (a special case of parity
objectives). We study for the first time the average case complexity of the
classical algorithm for computing almost-sure winning vertices for MDPs with
B\"uchi objectives. Our contributions are as follows: First, we show that for
MDPs with constant out-degree the expected number of iterations is at most
logarithmic and the average case running time is linear (as compared to the
worst case linear number of iterations and quadratic time complexity). Second,
we show that for general MDPs the expected number of iterations is constant and
the average case running time is linear (again as compared to the worst case
linear number of iterations and quadratic time complexity). Finally we also
show that given all graphs are equally likely, the probability that the
classical algorithm requires more than constant number of iterations is
exponentially small
Qualitative Reachability in Stochastic BPA Games
We consider a class of infinite-state stochastic games generated by stateless
pushdown automata (or, equivalently, 1-exit recursive state machines), where
the winning objective is specified by a regular set of target configurations
and a qualitative probability constraint `>0' or `=1'. The goal of one player
is to maximize the probability of reaching the target set so that the
constraint is satisfied, while the other player aims at the opposite. We show
that the winner in such games can be determined in PTIME for the `>0'
constraint, and both in NP and coNP for the `=1' constraint. Further, we prove
that the winning regions for both players are regular, and we design algorithms
which compute the associated finite-state automata. Finally, we show that
winning strategies can be synthesized effectively.Comment: Submitted to Information and Computation. 48 pages, 3 figure