. In the more difficult and realistic setting where the robots have limited visibility, the existing algorithmic results are only for convergence (towards a common point, without ever reaching it) and only for synchronous environments, where robots’ movements are assumed to be performed instantaneously

This paper surveys recent studies of learning problems in which the learner faces restrictions on the amount of information he can extract from each example he encounters. Our main framework for the analysis of such scenarios is the RFA (Restricted Focus of Attention) model. While being a natural refinement of the PAC learning model, some of the fundamental PAC-learning results and techniques fail in the RFA paradigm; learnability in the RFA model is no longer characterized by the VC dimension, and many PAC learning algorithms are not applicable in the RFA setting. Hence, the RFA formulation reflects the need for new techniques and tools to cope with some fundamental constraints of realistic learning problems. We also present some paradigms and algorithms that may serve as a first step towards answering this need. Two main types of restrictions can be considered in the general RFA setting: In the more stringent one, called k-RFA, only k of the n attributes of each example are revealed ...

suffice for catching rabbits with such local visibility with high probability. We distinguish between reactive rabbits who move only when a hunter is visible and general rabbits who can employ more sophisticated strategies. We present polynomial time algorithms that decide whether a graph G is hunter

Abstract. We consider a collection of robots which are identical (anonymous), have limited visibility of the environment, and no memory of the past (oblivious); furthermore, they are totally asynchronous in their actions, computations, and movements. We show that, even in such a totally

Abstract — Minimum distance localization is the problem of finding the shortest possible path for a robot to eliminate ambiguity regarding its position in the environment. We consider the problem of minimum distance localization in self-similar environments, where the robot’s sensor has limited

We address the problem of multi-robot area coverage and present a new approach in the case where the map of the area and its static obstacles are known and the robots have a limited visibility range. The proposed method starts by locating a set of static guards on the map of the target area

.e., limited visibility- LV, high visibility- HV and dark prefixes). We show that the IPv6 LV prefixes (v6LVPs) are generally reachable, mostly due to a less-specific HV covering prefix (v6HVP). However, this is not the case of the dark address space, which, by not having a covering v6HVP is largely

uses emergent features that signal the state of the ACC. Two failure modes—exceedance of braking algorithm limits and sensor failures—were introduced in the driving contexts of traffic and rain, respectively. A medium-fidelity driving simulator was used to evaluate the effect of automation (manual, ACC

of the guards (or the mobile robot that visits the guard positions in some order) is equipped with a panoramic camera of 360 degrees field of view. However, the camera has limited visibility, in the sense that it can observe with su#cient detail objects that are not further than a predefined visibility range

Abstract — We address the problem of multi-robot area coverage and present a new approach in the case where the map of the area and its static obstacles are known and the robots have a limited visibility range. The proposed method starts by locating a set of static guards on the map of the target