17,542 research outputs found
Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studiesâoften clustered under the collective term of
geocomputation, as they apply to geographyâare ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale
-Learning: A Collaborative Distributed Strategy for Multi-Agent Reinforcement Learning Through Consensus + Innovations
The paper considers a class of multi-agent Markov decision processes (MDPs),
in which the network agents respond differently (as manifested by the
instantaneous one-stage random costs) to a global controlled state and the
control actions of a remote controller. The paper investigates a distributed
reinforcement learning setup with no prior information on the global state
transition and local agent cost statistics. Specifically, with the agents'
objective consisting of minimizing a network-averaged infinite horizon
discounted cost, the paper proposes a distributed version of -learning,
-learning, in which the network agents collaborate by means of
local processing and mutual information exchange over a sparse (possibly
stochastic) communication network to achieve the network goal. Under the
assumption that each agent is only aware of its local online cost data and the
inter-agent communication network is \emph{weakly} connected, the proposed
distributed scheme is almost surely (a.s.) shown to yield asymptotically the
desired value function and the optimal stationary control policy at each
network agent. The analytical techniques developed in the paper to address the
mixed time-scale stochastic dynamics of the \emph{consensus + innovations}
form, which arise as a result of the proposed interactive distributed scheme,
are of independent interest.Comment: Submitted to the IEEE Transactions on Signal Processing, 33 page
On the genericity properties in networked estimation: Topology design and sensor placement
In this paper, we consider networked estimation of linear, discrete-time
dynamical systems monitored by a network of agents. In order to minimize the
power requirement at the (possibly, battery-operated) agents, we require that
the agents can exchange information with their neighbors only \emph{once per
dynamical system time-step}; in contrast to consensus-based estimation where
the agents exchange information until they reach a consensus. It can be
verified that with this restriction on information exchange, measurement fusion
alone results in an unbounded estimation error at every such agent that does
not have an observable set of measurements in its neighborhood. To over come
this challenge, state-estimate fusion has been proposed to recover the system
observability. However, we show that adding state-estimate fusion may not
recover observability when the system matrix is structured-rank (-rank)
deficient.
In this context, we characterize the state-estimate fusion and measurement
fusion under both full -rank and -rank deficient system matrices.Comment: submitted for IEEE journal publicatio
Recommended from our members
Between Scylla and Charybdis: Environmental governance and illegibility in the American West
In The Odyssey, Odysseus and his crew must navigate the Strait of Messina between two great hazards: the six-headed monster Scylla on one side, and the whirlpool Charybdis on the other. This conceit here guides a critical engagement with scientific knowledge and state power, grounded in the positionality and practices of government agents charged with the management of controversial species and processes in the American West. Based in ethnographic and archival research on wolf-livestock conflict and public lands grazing in Central Idaho, I relate how agents with the U.S. Forest Service and Idaho Department of Fish and Game navigate conditions not of their own choosing. Sailing the âchoppy seasâ of complex systems and multiple-use mandates, with the âwhirlpoolâ of cuts to capacity on one side and the âmonsterâ of political controversy and litigation on the other, agents appear to collect less or more ambiguous information on their charges, resulting in a partial âblindnessâ or illegibility. Although a rational adaptation to unrealistic expectations, this ignorance is not bliss but rather symptom and source of dysfunction, limiting agentsâ ability to carry out monitoring, collaboration, and effectively conduct on-the-ground management. Understanding patterns of illegibility requires that we attend both to broader contextual pressures and situated motivations. In so doing, we might account for the seeming disconnect between agenciesâ stated aims and practices, complicate traditional assumptions of evidence-based scientific management and analyses of bureaucratic rationality and state power, and make sense of the apparent dysfunction around environmental governance in the American West today
Design of Ad Hoc Wireless Mesh Networks Formed by Unmanned Aerial Vehicles with Advanced Mechanical Automation
Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs)
equipped with wireless transceivers (access points (APs)) are increasingly
being touted as being able to provide a flexible "on-the-fly" communications
infrastructure that can collect and transmit sensor data from sensors in
remote, wilderness, or disaster-hit areas. Recent advances in the mechanical
automation of UAVs have resulted in separable APs and replaceable batteries
that can be carried by UAVs and placed at arbitrary locations in the field.
These advanced mechanized UAV mesh networks pose interesting questions in terms
of the design of the network architecture and the optimal UAV scheduling
algorithms. This paper studies a range of network architectures that depend on
the mechanized automation (AP separation and battery replacement) capabilities
of UAVs and proposes heuristic UAV scheduling algorithms for each network
architecture, which are benchmarked against optimal designs.Comment: 12 page
Actively deployable mobile services for adaptive web access
2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
MLDS: A Flexible Location Directory Service for Tiered Sensor Networks
Many emergent distributed sensing applications need to keep track of mobile entities across multiple sensor networks connected via an IP network. To simplify the realization of such applications, we present MLDS, a Multi-resolution Location Directory Service for tiered sensor networks. MLDS provides a rich set of spatial query services ranging from simple queries about entity location, to complex nearest neighbor queries. Furthermore, MLDS supports multiple query granularities which allow an application to achieve the desired tradeoff between query accuracy and communication cost. We implemented MLDS on Agimone, a unified middleware for sensor and IP networks. We then deployed and evaluated the service on a tiered testbed consisting of tmote nodes and base stations. Our experimental results show that, when compared to a centralized approach, MLDS achieves significant savings in communication cost while still providing a high degree of accuracy, both within a single sensor network and across multiple sensor networks
- âŠ