Probabilistic game structures combine both nondeterminism and stochasticity, where players repeatedly take actions simultaneously to move to the next state of the concurrent game. Probabilistic alternating simulation is an important tool to compare the behaviour of different probabilistic game structures. In this paper, we present a sound and complete modal characterisation of this simulation relation by proposing a new logic based on probabilistic distributions. The logic enables a player to enforce a property in the next state or distribution. Its extension with fixpoints, which also characterises the simulation relation, can express a lot of interesting properties in practical applications.
A method of producing long life precision abrasive articles for use in met al removal operations. In this method abrasive particles are impinged against the inner surfaces of a rotating cylindrical mold by centrifugal force. During rotation of the mold a met allic matrix is deposited electrolytically on the inner surfaces of the mold until a matrix supporting the abrasive particles is formed. The matrix is then removed to receive core material and the core is then machined to the finished dimensions. The method is carried out at low temperatures, thereby avoiding heat distortion of the end product. We consider two-player games played over finite state spaces for an infinite number of rounds. At each state, the players simultaneously choose moves; the moves determine a successor state. It is often advantageous for players to choose probability distributions over moves, rather than single moves. Given a goal, for example, reach a target state, the question of winning is thus a probabilistic one: what is the maximal probability of winning from a given state? On these game structures, two fundamental notions are those of equivalences and metrics. Given a set of winning conditions, two states are equivalent if the players can win the same games with the same probability from both states. Metrics provide a bound on the difference in the probabilities of winning across states, capturing a quantitative notion of state similarity. We introduce equivalences and metrics for two-player game structures, and we show that they characterize the difference in probability of winning games whose goals are expressed in the quantitative mu-calculus. The quantitative mu-calculus can express a large set of goals, including reachability, safety, and omega-regular properties. Thus, we claim that our relations and metrics provide the canonical extensions to games, of the classical notion of bisimulation for transition systems. We develop our results both for equivalences and metrics, which generalize bisimulation, and for asymmetrical versions, which generalize simulation. The μ-calculus is a powerful tool for specifying and verifying transition systems, including those with both demonic (universal) and angelic (existential) choice; its quantitative generalization qMμ extends to include probabilistic choice.We make two major contributions to the theory of such systems. The first is to show that for a finite-state system, the logical interpretation of qMμ, via fixed points in a domain of real-valued functions into [0, 1], is equivalent to an operational interpretation given as a turn-based gambling game between two players.The second contribution is to show that each player in the gambling game has an optimal memoryless strategy---that is, a strategy which is independent of the game's history, and with which a player can achieve his optimal expected reward however his opponent chooses to play. Moreover, since qMμ is expressive enough to encode stochastic parity games, our result implies the existence of memoryless strategies in that framework, as well.As an additional feature, we include an extensive case study demonstrating the aforementioned duality between games and logic. Among other things, it shows that the use of algorithmic verification techniques is mathematically justified in the practical computation of probabilistic system properties.