12,702 research outputs found
A Multi-Core Solver for Parity Games
We describe a parallel algorithm for solving parity games,\ud
with applications in, e.g., modal mu-calculus model\ud
checking with arbitrary alternations, and (branching) bisimulation\ud
checking. The algorithm is based on Jurdzinski's Small Progress\ud
Measures. Actually, this is a class of algorithms, depending on\ud
a selection heuristics.\ud
\ud
Our algorithm operates lock-free, and mostly wait-free (except for\ud
infrequent termination detection), and thus allows maximum\ud
parallelism. Additionally, we conserve memory by avoiding storage\ud
of predecessor edges for the parity graph through strictly\ud
forward-looking heuristics.\ud
\ud
We evaluate our multi-core implementation's behaviour on parity games\ud
obtained from mu-calculus model checking problems for a set of\ud
communication protocols, randomly generated problem instances, and\ud
parametric problem instances from the literature.\ud
\u
Distributed Synthesis in Continuous Time
We introduce a formalism modelling communication of distributed agents
strictly in continuous-time. Within this framework, we study the problem of
synthesising local strategies for individual agents such that a specified set
of goal states is reached, or reached with at least a given probability. The
flow of time is modelled explicitly based on continuous-time randomness, with
two natural implications: First, the non-determinism stemming from interleaving
disappears. Second, when we restrict to a subclass of non-urgent models, the
quantitative value problem for two players can be solved in EXPTIME. Indeed,
the explicit continuous time enables players to communicate their states by
delaying synchronisation (which is unrestricted for non-urgent models). In
general, the problems are undecidable already for two players in the
quantitative case and three players in the qualitative case. The qualitative
undecidability is shown by a reduction to decentralized POMDPs for which we
provide the strongest (and rather surprising) undecidability result so far
Learning Equilibria with Partial Information in Decentralized Wireless Networks
In this article, a survey of several important equilibrium concepts for
decentralized networks is presented. The term decentralized is used here to
refer to scenarios where decisions (e.g., choosing a power allocation policy)
are taken autonomously by devices interacting with each other (e.g., through
mutual interference). The iterative long-term interaction is characterized by
stable points of the wireless network called equilibria. The interest in these
equilibria stems from the relevance of network stability and the fact that they
can be achieved by letting radio devices to repeatedly interact over time. To
achieve these equilibria, several learning techniques, namely, the best
response dynamics, fictitious play, smoothed fictitious play, reinforcement
learning algorithms, and regret matching, are discussed in terms of information
requirements and convergence properties. Most of the notions introduced here,
for both equilibria and learning schemes, are illustrated by a simple case
study, namely, an interference channel with two transmitter-receiver pairs.Comment: 16 pages, 5 figures, 1 table. To appear in IEEE Communication
Magazine, special Issue on Game Theor
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents
A satisfactory multiagent learning algorithm should, {\em at a minimum},
learn to play optimally against stationary opponents and converge to a Nash
equilibrium in self-play. The algorithm that has come closest, WoLF-IGA, has
been proven to have these two properties in 2-player 2-action repeated
games--assuming that the opponent's (mixed) strategy is observable. In this
paper we present AWESOME, the first algorithm that is guaranteed to have these
two properties in {\em all} repeated (finite) games. It requires only that the
other players' actual actions (not their strategies) can be observed at each
step. It also learns to play optimally against opponents that {\em eventually
become} stationary. The basic idea behind AWESOME ({\em Adapt When Everybody is
Stationary, Otherwise Move to Equilibrium}) is to try to adapt to the others'
strategies when they appear stationary, but otherwise to retreat to a
precomputed equilibrium strategy. The techniques used to prove the properties
of AWESOME are fundamentally different from those used for previous algorithms,
and may help in analyzing other multiagent learning algorithms also
Endogenously-Timed Herding And The Synchronization Of Investment Cycles
This paper combines the recent garne theoretic approach of endogenous timing of entry to herding models with a rnacroeconornic model of investrnent cycles. The integrated description embodies the qualitative resuits of the rnyopic herding model in a medium run investment objective of smooth ing the capital stock adjustment process. lt features a completely disaggregated structure and bears the potential to synchronize individual cyclic investing be haviors. This synchronization via nonlinear feedback from the aggregate ac tivity can serve as an explanation of the inexistent cancelling of heterogeneous sectoral quasi-cycles. The model others an explanatory base for the constitu tion of the observed strong cyclicality of the aggregate investment series by a multitude of different periodicities and phases on the individual level. Finally, based on recent ndings of the herding literature, the stabilization potential of third parties' information revelation is conjectured
A Coevolutionary Particle Swarm Algorithm for Bi-Level Variational Inequalities: Applications to Competition in Highway Transportation Networks
A climate of increasing deregulation in traditional highway transportation,
where the private sector has an expanded role in the provision of traditional
transportation services, provides a background for practical policy issues to be investigated.
One of the key issues of interest, and the focus of this chapter, would
be the equilibrium decision variables offered by participants in this market. By assuming
that the private sector participants play a Nash game, the above problem can
be described as a Bi-Level Variational Inequality (BLVI). Our problem differs from
the classical Cournot-Nash game because each and every playerâs actions is constrained
by another variational inequality describing the equilibrium route choice of
users on the network. In this chapter, we discuss this BLVI and suggest a heuristic
coevolutionary particle swarm algorithm for its resolution. Our proposed algorithm
is subsequently tested on example problems drawn from the literature. The numerical
experiments suggest that the proposed algorithm is a viable solution method for
this problem
Sequentiality vs. Concurrency in Games and Logic
Connections between the sequentiality/concurrency distinction and the
semantics of proofs are investigated, with particular reference to games and
Linear Logic.Comment: 35 pages, appeared in Mathematical Structures in Computer Scienc
Learning-Based Synthesis of Safety Controllers
We propose a machine learning framework to synthesize reactive controllers
for systems whose interactions with their adversarial environment are modeled
by infinite-duration, two-player games over (potentially) infinite graphs. Our
framework targets safety games with infinitely many vertices, but it is also
applicable to safety games over finite graphs whose size is too prohibitive for
conventional synthesis techniques. The learning takes place in a feedback loop
between a teacher component, which can reason symbolically about the safety
game, and a learning algorithm, which successively learns an overapproximation
of the winning region from various kinds of examples provided by the teacher.
We develop a novel decision tree learning algorithm for this setting and show
that our algorithm is guaranteed to converge to a reactive safety controller if
a suitable overapproximation of the winning region can be expressed as a
decision tree. Finally, we empirically compare the performance of a prototype
implementation to existing approaches, which are based on constraint solving
and automata learning, respectively
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