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Network Distributed POMDP with Communication

Network Distributed POMDP with Communication - …

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Networked distributed POMDPs: A synthesis of ..

The algorithm is suitable for computing optimal plans for acooperative group of agents that operate in a stochastic environmentsuch as multirobot coordination, network traffic control, `ordistributed resource allocation.

Networked distributed POMDPs: A synthesis of distributed constraint optimization and ..

In this talk, after a brief exposition of the main ideas behind Mean Field Game theory, we consider applications to a novel set of problems arising in energy systems. Due to increasing rates of penetration of intermittent renewable energy sources such as solar and wind energy in power systems, the fluctuations of instantaneous mismatches between generation and electricity demand have drastically augmented. Besides the attending network stability problems, this has deferred an increasing compensatory role to the so‐called spinning reserves in power systems; the latter typically rely on environmentally damaging fossil fuels. As an alternative, we aim at creating a control architecture that would allow the harnessing of the energy storage capability associated with millions of electrical devices such as electric water heaters, air conditioners, electric space heaters in dwellings and commercial buildings into a gigantic but distributed battery to smooth the generation‐load mismatch fluctuations, while maintaining local customer comfort and safety constraints. We develop novel formulations of the current mean field game theory that could help in achieving that goal. Numerical results are reported.

A network distributed POMDP (ND-POMDP) ..

Networked Distributed POMDPs: A Synthesis of Distributed Constraint ..

Abstract: Searching for objects in physical space has been one of the most important tasks for mobile robots. Transient targets refer to intermittent signal emitting objects such as cellphone users, airplane black boxes, and unknown sensor networks. Searching for such targets is difficult because targets are found only if both signal emission and sensing range conditions are simultaneously satisfied. This problem is inherently stochastic which makes the traditional coverage-based searching techniques less effective. Considering a large searching region, sparse target distribution, the expected searching time, multi-target signal correspondence, variable signal transmission power, and the efficient coordination of multiple robots, we report a series of algorithms developed over last decade that handle the cases from single-target-single-robot to decentralized multi-target-multi-robot with different sensing and communication constraints and explicit performance analyses. Extensive simulation and physical experiment results are also included in the talk.

Finally, unifying some previous approaches for solving Dec-POMDPs, we describe a family of algorithms for extracting policies from such Q-value functions, and perform an experimental evaluation on existing test problems, including a new firefighting benchmark problem.

(2005) Networked distributed POMDPs: a synthesis of ..

Networked distributed POMDPs: A synthesis of distributed constraint optimization ..

Abstract: This talk deals with a distributed optimal power supply-demand management method based on dynamic pricing in the deregulated electricity market. Since power consumers and generators determine their own power demand or supply selfishly in the deregulated electricity market trading, some distributed power management methods are required to maintain the power supply-demand balance in a power grid. For this problem, the proposed method integrates two different time periods deregulated electricity market, "Day-ahead market" and "Real-time market", and solves this management problem in a distributed manner using electricity prices through market trading. Specifically, the proposed method, first, derives the optimal locational electricity prices which maximize social welfare of the entire power network in the day-ahead market based on alternating decision makings of market players. Then, the proposed method compensates the power imbalance caused by some problems such as prediction errors via negawatt trading in the real- time market, in which power consumers reduce their demand, while they receive monetary incentives from the market operator. The proposed method shows the optimal incentive design method using the day-ahead prices to minimize the power adjustment cost in real-time market trading. Finally, numerical simulation results are shown to demonstrate the effectiveness of the proposed method.

Abstract: The energy-aware airborne dynamic, data-driven application system (EA-DDDAS) performs persistent sampling in complex atmospheric conditions by exploiting wind energy using the dynamic data-driven application system paradigm. The main challenge for future airborne sampling missions is operation with tight integration of physical and computational resources over wireless communication networks, in complex atmospheric conditions. The physical resources considered here include sensor platforms, particularly mobile Doppler radar and unmanned aircraft, the complex conditions in which they operate, and the region of interest. Autonomous operation requires distributed computational effort connected by layered wireless communication. Onboard decision-making and coordination algorithms can be enhanced by atmospheric models that assimilate input from physics-based models and wind fields derived from multiple sources. These models are generally too complex to be run onboard the aircraft, so they need to be executed in ground vehicles in the field, and connected over broadband or other wireless links back to the field. Finally, the wind field environment drives strong interaction between the computational and physical systems, both as a challenge to autonomous path planning algorithms and as a novel energy source that can be exploited to improve system range and endurance. This seminar will describe a collaborative effort to implementation a complete EA-DDDAS. Results will be presented from previous field deployments of unmanned aircraft to show the evolution of the EA-DDDAS concept, and from recent deployments validating the EA-DDDAS.

Networked Distributed POMDPs: A Synthesis of Distributed Constraint Optimization and POMDPs
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  • 19/12/2017 · While distributed POMDPs capture ..

    Networked distributed POMDPs: a synthesis of distributed constraint ..

  • A synthesis of distributed constraint optimization ..

    complex with a large number of interacting partners often distributed over a network.

  • While distributed POMDPs capture the real ..

    Networked Distributed POMDPs: A Synthesis of Distributed Constraint Optimization and POMDPs.

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A Synthesis of Distributed Constraint Optimization and ..

Abstract: The successful deployment of complex, multi-agent systems requires well-designed, agent-level control strategies that accommodate sensing, communication, and computational limitations on individual agents. Indeed, many applications demand system-level dynamics to be robust to disturbance and adaptive in the face of changes in the environment. Remarkably, animal groups, from bird flocks to fish schools, exhibit just such robust and adaptive behaviors, even as individual animals have their own limitations. To better understand and leverage the parallels between networks in nature and design, a principled examination of collective dynamics is warranted. I will describe an analytical framework based on nonlinear dynamical systems theory for the realization of collective decision-making that allows for the rigorous study of the mechanisms of observed collective animal behavior together with the design of distributed strategies for collective dynamics with provable performance.

International Journal of Robotics Research

The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, or distributed resource allocation.

Deep learning in neural networks: An overview - …

We present multi-agent A * (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partiallyobservable Markov decision problems (DEC-POMDPs) with finite horizon.

Eletronic Proceedings of Neural Information Processing Systems ..

...DUCTION Many current and proposed applications of networks of agents, including mobile sensor networks, autonomous underwater vehicles, involve 100s of agents acting collaboratively under uncertainty =-=[8, 10]-=-. Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are ideally suited to plan for such agent networks, given their ability to plan in the presence of transitional and obs...

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