790 research outputs found
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
Linear and Branching System Metrics
We extend the classical system relations of trace\ud
inclusion, trace equivalence, simulation, and bisimulation to a quantitative setting in which propositions are interpreted not as boolean values, but as elements of arbitrary metric spaces.\ud
\ud
Trace inclusion and equivalence give rise to asymmetrical and symmetrical linear distances, while simulation and bisimulation give rise to asymmetrical and symmetrical branching distances. We study the relationships among these distances, and we provide a full logical characterization of the distances in terms of quantitative versions of LTL and μ-calculus. We show that, while trace inclusion (resp. equivalence) coincides with simulation (resp. bisimulation) for deterministic boolean transition systems, linear\ud
and branching distances do not coincide for deterministic metric transition systems. Finally, we provide algorithms for computing the distances over finite systems, together with a matching lower complexity bound
Termination Criteria for Solving Concurrent Safety and Reachability Games
We consider concurrent games played on graphs. At every round of a game, each
player simultaneously and independently selects a move; the moves jointly
determine the transition to a successor state. Two basic objectives are the
safety objective to stay forever in a given set of states, and its dual, the
reachability objective to reach a given set of states. We present in this paper
a strategy improvement algorithm for computing the value of a concurrent safety
game, that is, the maximal probability with which player~1 can enforce the
safety objective. The algorithm yields a sequence of player-1 strategies which
ensure probabilities of winning that converge monotonically to the value of the
safety game.
Our result is significant because the strategy improvement algorithm
provides, for the first time, a way to approximate the value of a concurrent
safety game from below. Since a value iteration algorithm, or a strategy
improvement algorithm for reachability games, can be used to approximate the
same value from above, the combination of both algorithms yields a method for
computing a converging sequence of upper and lower bounds for the values of
concurrent reachability and safety games. Previous methods could approximate
the values of these games only from one direction, and as no rates of
convergence are known, they did not provide a practical way to solve these
games
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Approaching Fair Collision-Free Channel Access with Slotted ALOHA Using Collaborative Policy-Based Reinforcement Learning
A Few Considerations on Structural and Logical Composition in Specification Theories
Over the last 20 years a large number of automata-based specification
theories have been proposed for modeling of discrete,real-time and
probabilistic systems. We have observed a lot of shared algebraic structure
between these formalisms. In this short abstract, we collect results of our
work in progress on describing and systematizing the algebraic assumptions in
specification theories.Comment: In Proceedings FIT 2010, arXiv:1101.426
Conjugate Natural Selection: Fisher-Rao Natural Gradient Descent Optimally Approximates Evolutionary Dynamics and Continuous Bayesian Inference
Rather than refining individual candidate solutions for a general non-convex
optimization problem, by analogy to evolution, we consider minimizing the
average loss for a parametric distribution over hypotheses. In this setting, we
prove that Fisher-Rao natural gradient descent (FR-NGD) optimally approximates
the continuous-time replicator equation (an essential model of evolutionary
dynamics) by minimizing the mean-squared error for the relative fitness of
competing hypotheses. We term this finding "conjugate natural selection" and
demonstrate its utility by numerically solving an example non-convex
optimization problem over a continuous strategy space. Next, by developing
known connections between discrete-time replicator dynamics and Bayes's rule,
we show that when absolute fitness corresponds to the negative KL-divergence of
a hypothesis's predictions from actual observations, FR-NGD provides the
optimal approximation of continuous Bayesian inference. We use this result to
demonstrate a novel method for estimating the parameters of stochastic
processes.Comment: 13 pages, 3 figure
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