13,173 research outputs found
Non-Zero Sum Games for Reactive Synthesis
In this invited contribution, we summarize new solution concepts useful for
the synthesis of reactive systems that we have introduced in several recent
publications. These solution concepts are developed in the context of non-zero
sum games played on graphs. They are part of the contributions obtained in the
inVEST project funded by the European Research Council.Comment: LATA'16 invited pape
LNCS
We study turn-based stochastic zero-sum games with lexicographic preferences over reachability and safety objectives. Stochastic games are standard models in control, verification, and synthesis of stochastic reactive systems that exhibit both randomness as well as angelic and demonic non-determinism. Lexicographic order allows to consider multiple objectives with a strict preference order over the satisfaction of the objectives. To the best of our knowledge, stochastic games with lexicographic objectives have not been studied before. We establish determinacy of such games and present strategy and computational complexity results. For strategy complexity, we show that lexicographically optimal strategies exist that are deterministic and memory is only required to remember the already satisfied and violated objectives. For a constant number of objectives, we show that the relevant decision problem is in NPâ©coNP , matching the current known bound for single objectives; and in general the decision problem is PSPACE -hard and can be solved in NEXPTIMEâ©coNEXPTIME . We present an algorithm that computes the lexicographically optimal strategies via a reduction to computation of optimal strategies in a sequence of single-objectives games. We have implemented our algorithm and report experimental results on various case studies
Stochastic Games with Lexicographic Reachability-Safety Objectives
We study turn-based stochastic zero-sum games with lexicographic preferences
over reachability and safety objectives. Stochastic games are standard models
in control, verification, and synthesis of stochastic reactive systems that
exhibit both randomness as well as angelic and demonic non-determinism.
Lexicographic order allows to consider multiple objectives with a strict
preference order over the satisfaction of the objectives. To the best of our
knowledge, stochastic games with lexicographic objectives have not been studied
before. We establish determinacy of such games and present strategy and
computational complexity results. For strategy complexity, we show that
lexicographically optimal strategies exist that are deterministic and memory is
only required to remember the already satisfied and violated objectives. For a
constant number of objectives, we show that the relevant decision problem is in
NP coNP, matching the current known bound for single objectives; and in
general the decision problem is PSPACE-hard and can be solved in NEXPTIME
coNEXPTIME. We present an algorithm that computes the lexicographically
optimal strategies via a reduction to computation of optimal strategies in a
sequence of single-objectives games. We have implemented our algorithm and
report experimental results on various case studies.Comment: Full version (33 pages) of CAV20 conference paper; including an
appendix with technical proof
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
Quantitative Games under Failures
We study a generalisation of sabotage games, a model of dynamic network games
introduced by van Benthem. The original definition of the game is inherently
finite and therefore does not allow one to model infinite processes. We propose
an extension of the sabotage games in which the first player (Runner) traverses
an arena with dynamic weights determined by the second player (Saboteur). In
our model of quantitative sabotage games, Saboteur is now given a budget that
he can distribute amongst the edges of the graph, whilst Runner attempts to
minimise the quantity of budget witnessed while completing his task. We show
that, on the one hand, for most of the classical cost functions considered in
the literature, the problem of determining if Runner has a strategy to ensure a
cost below some threshold is EXPTIME-complete. On the other hand, if the budget
of Saboteur is fixed a priori, then the problem is in PTIME for most cost
functions. Finally, we show that restricting the dynamics of the game also
leads to better complexity
Games with Delays. A Frankenstein Approach
We investigate infinite games on finite graphs where the information flow is
perturbed by nondeterministic signalling delays. It is known that such
perturbations make synthesis problems virtually unsolvable, in the general
case. On the classical model where signals are attached to states, tractable
cases are rare and difficult to identify.
Here, we propose a model where signals are detached from control states, and
we identify a subclass on which equilibrium outcomes can be preserved, even if
signals are delivered with a delay that is finitely bounded. To offset the
perturbation, our solution procedure combines responses from a collection of
virtual plays following an equilibrium strategy in the instant- signalling game
to synthesise, in a Frankenstein manner, an equivalent equilibrium strategy for
the delayed-signalling game
Average-energy games
Two-player quantitative zero-sum games provide a natural framework to
synthesize controllers with performance guarantees for reactive systems within
an uncontrollable environment. Classical settings include mean-payoff games,
where the objective is to optimize the long-run average gain per action, and
energy games, where the system has to avoid running out of energy.
We study average-energy games, where the goal is to optimize the long-run
average of the accumulated energy. We show that this objective arises naturally
in several applications, and that it yields interesting connections with
previous concepts in the literature. We prove that deciding the winner in such
games is in NP inter coNP and at least as hard as solving mean-payoff games,
and we establish that memoryless strategies suffice to win. We also consider
the case where the system has to minimize the average-energy while maintaining
the accumulated energy within predefined bounds at all times: this corresponds
to operating with a finite-capacity storage for energy. We give results for
one-player and two-player games, and establish complexity bounds and memory
requirements.Comment: In Proceedings GandALF 2015, arXiv:1509.0685
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