9,447 research outputs found
Randomness for Free
We consider two-player zero-sum games on graphs. These games can be
classified on the basis of the information of the players and on the mode of
interaction between them. On the basis of information the classification is as
follows: (a) partial-observation (both players have partial view of the game);
(b) one-sided complete-observation (one player has complete observation); and
(c) complete-observation (both players have complete view of the game). On the
basis of mode of interaction we have the following classification: (a)
concurrent (both players interact simultaneously); and (b) turn-based (both
players interact in turn). The two sources of randomness in these games are
randomness in transition function and randomness in strategies. In general,
randomized strategies are more powerful than deterministic strategies, and
randomness in transitions gives more general classes of games. In this work we
present a complete characterization for the classes of games where randomness
is not helpful in: (a) the transition function probabilistic transition can be
simulated by deterministic transition); and (b) strategies (pure strategies are
as powerful as randomized strategies). As consequence of our characterization
we obtain new undecidability results for these games
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems
Development of robust dynamical systems and networks such as autonomous
aircraft systems capable of accomplishing complex missions faces challenges due
to the dynamically evolving uncertainties coming from model uncertainties,
necessity to operate in a hostile cluttered urban environment, and the
distributed and dynamic nature of the communication and computation resources.
Model-based robust design is difficult because of the complexity of the hybrid
dynamic models including continuous vehicle dynamics, the discrete models of
computations and communications, and the size of the problem. We will overview
recent advances in methodology and tools to model, analyze, and design robust
autonomous aerospace systems operating in uncertain environment, with stress on
efficient uncertainty quantification and robust design using the case studies
of the mission including model-based target tracking and search, and trajectory
planning in uncertain urban environment. To show that the methodology is
generally applicable to uncertain dynamical systems, we will also show examples
of application of the new methods to efficient uncertainty quantification of
energy usage in buildings, and stability assessment of interconnected power
networks
Robust Control of Uncertain Markov Decision Processes with Temporal Logic Specifications
We present a method for designing robust controllers for dynamical systems with linear temporal logic specifications. We abstract the original system by a finite Markov Decision Process (MDP) that has transition probabilities in a specified uncertainty set. A robust control policy for the MDP is generated that maximizes the worst-case probability of satisfying the specification over all transition probabilities in the uncertainty set. To do this, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming allows us to solve for a -suboptimal robust control policy with time complexity times that for the non-robust case. We then implement this control policy on the original dynamical system
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
This paper describes an architecture for robots that combines the
complementary strengths of probabilistic graphical models and declarative
programming to represent and reason with logic-based and probabilistic
descriptions of uncertainty and domain knowledge. An action language is
extended to support non-boolean fluents and non-deterministic causal laws. This
action language is used to describe tightly-coupled transition diagrams at two
levels of granularity, with a fine-resolution transition diagram defined as a
refinement of a coarse-resolution transition diagram of the domain. The
coarse-resolution system description, and a history that includes (prioritized)
defaults, are translated into an Answer Set Prolog (ASP) program. For any given
goal, inference in the ASP program provides a plan of abstract actions. To
implement each such abstract action, the robot automatically zooms to the part
of the fine-resolution transition diagram relevant to this action. A
probabilistic representation of the uncertainty in sensing and actuation is
then included in this zoomed fine-resolution system description, and used to
construct a partially observable Markov decision process (POMDP). The policy
obtained by solving the POMDP is invoked repeatedly to implement the abstract
action as a sequence of concrete actions, with the corresponding observations
being recorded in the coarse-resolution history and used for subsequent
reasoning. The architecture is evaluated in simulation and on a mobile robot
moving objects in an indoor domain, to show that it supports reasoning with
violation of defaults, noisy observations and unreliable actions, in complex
domains.Comment: 72 pages, 14 figure
Probabilistic modal {\mu}-calculus with independent product
The probabilistic modal {\mu}-calculus is a fixed-point logic designed for
expressing properties of probabilistic labeled transition systems (PLTS's). Two
equivalent semantics have been studied for this logic, both assigning to each
state a value in the interval [0,1] representing the probability that the
property expressed by the formula holds at the state. One semantics is
denotational and the other is a game semantics, specified in terms of
two-player stochastic parity games. A shortcoming of the probabilistic modal
{\mu}-calculus is the lack of expressiveness required to encode other important
temporal logics for PLTS's such as Probabilistic Computation Tree Logic (PCTL).
To address this limitation we extend the logic with a new pair of operators:
independent product and coproduct. The resulting logic, called probabilistic
modal {\mu}-calculus with independent product, can encode many properties of
interest and subsumes the qualitative fragment of PCTL. The main contribution
of this paper is the definition of an appropriate game semantics for this
extended probabilistic {\mu}-calculus. This relies on the definition of a new
class of games which generalize standard two-player stochastic (parity) games
by allowing a play to be split into concurrent subplays, each continuing their
evolution independently. Our main technical result is the equivalence of the
two semantics. The proof is carried out in ZFC set theory extended with
Martin's Axiom at an uncountable cardinal
Dependability Analysis of Control Systems using SystemC and Statistical Model Checking
Stochastic Petri nets are commonly used for modeling distributed systems in
order to study their performance and dependability. This paper proposes a
realization of stochastic Petri nets in SystemC for modeling large embedded
control systems. Then statistical model checking is used to analyze the
dependability of the constructed model. Our verification framework allows users
to express a wide range of useful properties to be verified which is
illustrated through a case study
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