157,229 research outputs found
Building multi-layer social knowledge maps with google maps API
Google Maps is an intuitive online-map service which changes people's way of navigation on Geo-maps. People can explore the maps in a multi-layer fashion in order to avoid information overloading. This paper reports an innovative approach to extend the "power" of Google Maps to adaptive learning. We have designed and implemented a navigator for multi-layer social knowledge maps, namely ProgressiveZoom, with Google Maps API. In our demonstration, the knowledge maps are built from the Interactive System Design (ISD) course at the School of Information Science, University of Pittsburgh. Students can read the textbooks and reflect their individual and social learning progress in a context of pedagogical hierarchical structure
Strategy Logic with Imperfect Information
We introduce an extension of Strategy Logic for the imperfect-information
setting, called SLii, and study its model-checking problem. As this logic
naturally captures multi-player games with imperfect information, the problem
turns out to be undecidable. We introduce a syntactical class of "hierarchical
instances" for which, intuitively, as one goes down the syntactic tree of the
formula, strategy quantifications are concerned with finer observations of the
model. We prove that model-checking SLii restricted to hierarchical instances
is decidable. This result, because it allows for complex patterns of
existential and universal quantification on strategies, greatly generalises
previous ones, such as decidability of multi-player games with imperfect
information and hierarchical observations, and decidability of distributed
synthesis for hierarchical systems. To establish the decidability result, we
introduce and study QCTL*ii, an extension of QCTL* (itself an extension of CTL*
with second-order quantification over atomic propositions) by parameterising
its quantifiers with observations. The simple syntax of QCTL* ii allows us to
provide a conceptually neat reduction of SLii to QCTL*ii that separates
concerns, allowing one to forget about strategies and players and focus solely
on second-order quantification. While the model-checking problem of QCTL*ii is,
in general, undecidable, we identify a syntactic fragment of hierarchical
formulas and prove, using an automata-theoretic approach, that it is decidable.
The decidability result for SLii follows since the reduction maps hierarchical
instances of SLii to hierarchical formulas of QCTL*ii
The Price of Anarchy in Active Signal Landscape Map Building
Multiple receivers with a priori knowledge about
their own initial states are assumed to be dropped in an unknown
environment comprising multiple signals of opportunity (SOPs)
transmitters. The receivers draw pseudorange observations from
the SOPs. The receivers’ objective is to build a high-fidelity
signal landscape map of the environment, which would enable
the receivers to navigate accurately with the aid of the SOPs.
The receivers could command their own maneuvers and such
commands are computed so to maximize the information gathered
about the SOPs in a greedy fashion. Several information
fusion and decision making architectures are possible. This
paper studies the price of anarchy in building signal landscape
maps to assess the degradation in the map quality should the
receivers produce their own maps and make their own maneuver
decisions versus a completely centralized approach. In addition,
a hierarchical architecture is proposed in which the receivers
build their own maps and make their own decisions, but share
relevant information. Such architecture is shown to produce maps
of comparable quality to the completely centralized approach.Aerospace Engineering and Engineering Mechanic
Towards hierarchical blackboard mapping on a whiskered robot
The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem for a mobile robot, CrunchBot, where only sparse, local tactile information from whisker sensors is available. To compensate for such weak likelihood information, we make use of low-level signal processing and strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting as a mapping algorithm. The hierarchical models require reports of whisker distance to contact and of surface orientation at contact, and we demonstrate that this information can be retrieved by classifiers from strain data collected by CrunchBot's physical whiskers. We then provide a demonstration in simulation of how this information can be used to build maps (but not yet full SLAM) in an zero-odometry-noise environment containing walls and table-like hierarchical objects. © 2012 Elsevier B.V. All rights reserved
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