functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

extend the usual notion of action in this framework to include options|closed-loop policies for taking action over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as muscle twitches and joint knowledge

tim ue he f inc rta the 2009 Elsevier Ltd. All rights reserved. ndustri evolv duct d n orde are d as a closed-loop optimizer that rejects disturbances affecting profit [45]. Nevertheless, an important limitation of RTO is that it is en-tirely reactive, in the sense that only current disturbances

correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving

depend upon this measure. We designate the NCE Principle as the property that the resulting scheme is consistent (or soluble), i.e. the prescribed control laws produce sample paths which produce the mass effect measure. By construction, the overall closed-loop behaviour is such that each agent’s

Feedback control with limited data rates is an emerging area which incorporates ideas from both control and information theory. A fundamental question it poses is how low the closed loop data rate can be made before a given dynamical system is impossible to stabilize by any coding and control law

This paper presents a closed economy dynamic stochastic general equilibrium model with mo-nopolistic competition and sticky prices. Two types of price setters are assumed to exist. One acts rationally given Calvo-type constraints on price setting. The other type sets prices accord-ing to a rule

systems, real-time systems, stochastic control, stochastic parameters, timing jitter. 1 Introduction Many real-time systems are implemented as distributed control systems, where the control loops are closed over a communication network or a field bus. There will inevitably be time delays

in that they are closed loop, uncertain, and of variable duration. Macro actions are needed to represent common-sense higher-level actions such as going to lunch, grasping an object, or traveling to a distant city. This paper generalizes prior work on temporally abstract models (Sutton 1995) and extends it from

In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical “Car on the Hill” control problem.