Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, each time an agent generates a new...
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ISBN:
(纸本)9781450356497
Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent's locally perceived demand. In this paper, we extend this basic coordination protocol to additionally incorporate the complementary flow of information reflective of an intersection's current congestion level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and congestion level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic protocol, we are able to approach network wide optimality and reduce inefficiency due to myopic intersection control decisions.
Current Stackelberg security game models primarily focus on isolated systems in which only one defender is present, despite being part of a more complex system with multiple players. However, many real systems such as...
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Current Stackelberg security game models primarily focus on isolated systems in which only one defender is present, despite being part of a more complex system with multiple players. However, many real systems such as transportation networks and the power grid exhibit interdependencies among targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security scenarios, the authors investigate security games with multiple defenders. Unlike most prior analyses, they focus on situations in which each defender must protect multiple targets, so even a single defender"s best response decision is, in general, nontrivial. Considering interdependencies among targets, the authors develop a novel mixed-integer linear programming formulation to compute a defender"s best response, and approximate Nash equilibria of the game using this formulation. Their analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or underinvest in security.
Large scale biological systems often exhibit emergent properties that are attractive in an engineering context. In this paper, the context is a class of wireless sensor networks for emergency environmental monitoring....
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Large scale biological systems often exhibit emergent properties that are attractive in an engineering context. In this paper, the context is a class of wireless sensor networks for emergency environmental monitoring. The attractive properties are simplicity, self-organisation, adaptiveness to scenario change and a lack of scenario specific parameter tunings. Emergence Medium Access Control(E-MAC) is a scheme inspired by biological social populations that individually react to environmental stimuli. Using a very simple protocol, it exhibits the desired emergent properties. When compared to a well established practical counterpart, the IEEE 802.11 CSMA/CA standard, it exhibits better throughput, end-to-end delay and fairness. This paper describes the motivation and design of E-MAC, and presents the above comparison.
This paper surveys the literature over the last decades in the field of self-organizing multiagent systems. Self-organization has been extensively studied and applied in multiagent systems and other fields, e.g., sens...
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This paper surveys the literature over the last decades in the field of self-organizing multiagent systems. Self-organization has been extensively studied and applied in multiagent systems and other fields, e.g., sensor networks and grid systems. Self-organization mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organization mechanisms developed for use in multiagent systems. In this paper, we provide a survey of existing literature on self-organization mechanisms in multiagent systems. We also highlight the future work on key research issues in multiagent systems. This paper can serve as a guide and a starting point for anyone who will conduct research on self-organization in multiagent systems. Also, this paper complements existing survey studies on self-organization in multiagent systems.
Robotic is a great substitute for human to explore the dangerous areas, and will also be a great help for disaster management. Although the rise of depth sensor technologies gives a huge boost to robotic vision resear...
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Robotic is a great substitute for human to explore the dangerous areas, and will also be a great help for disaster management. Although the rise of depth sensor technologies gives a huge boost to robotic vision research, traditional approaches cannot be applied to disaster-handling robots directly due to some limitations. In this paper, we focus on the 3D robotic perception, and propose a view-invariant Convolutional Neural Network (CNN) Model for scene understanding in disaster scenarios. The proposed system is highly distributed and parallel, which is of great help to improve the efficiency of network training. In our system, two individual CNNs are used to, respectively, propose objects from input data and classify their categories. We attempt to overcome the difficulties and restrictions caused by disasters using several specially-designed multi-task loss functions. The most significant advantage in our work is that the proposed method can learn a view-invariant feature with no requirement on RGB data, which is essential for harsh, disordered and changeable environments. Additionally, an effective optimization algorithm to accelerate the learning process is also included in our work. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.
The principle of service composition based on multi-agent is that multi-agent can coordinate to reach Pareto-optimal Nash equilibrium. Reinforcement learning algorithms can be used to deal with the coordination proble...
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The principle of service composition based on multi-agent is that multi-agent can coordinate to reach Pareto-optimal Nash equilibrium. Reinforcement learning algorithms can be used to deal with the coordination problem in cooperative games. In this paper, the multi-agent coordination problems in cooperative games for different user preference is investigated. In our case, each agent can represent a user's preference, and it finally learns a policy that is best fit for that user. Most previous works study the deterministic gain of a state. However, in practical service environments, the gain may be nondeterministic due to unstable Quality of Service (QoS). In addition, user preference should be considered. To avoid local optimal solution, we let each agent randomly change interacting partners in each iteration. Thus, an agent can learn its optimal strategy by interacting repeatedly with the rest of agents representing different user preference. The experimental results show that our reinforcement learning algorithm' outperforms other learning methods. (C) 2016 Elsevier B.V. All rights reserved.
Myerson"s graph-restricted games are a well-known formalism for modeling cooperation that"s subject to restrictions. In particular, Myerson considered a coalitional game in which cooperation is possible only...
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Myerson"s graph-restricted games are a well-known formalism for modeling cooperation that"s subject to restrictions. In particular, Myerson considered a coalitional game in which cooperation is possible only through an underlying network of links between agents. A unique fair solution concept for graph-restricted games is called the Myerson value. One study generalized these results by considering probabilistic graphs in which agents can cooperate via links only to some extent, that is, with some probability. The authors" algorithm is based on the enumeration of all connected subgraphs in the graph. As a sample application of the new algorithm, they consider a probabilistic graph that represents likelihood of pairwise collaboration between political parties before the 2015 general elections in the UK.
Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, each time an agent generates a new...
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Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent's locally perceived demand. In this paper, we extend this basic coordination protocol to additionally incorporate the complementary flow of information reflective of an intersection's current congestion level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and congestion level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic protocol, we are able to approach network-wide optimality and reduce inefficiency due to myopic intersection control decisions.
In recent years, there has been an increasing interest in the implementation of distributed systems for monitoring and controlling different parameters. However, a major problem with this kind of systems is the contro...
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ISBN:
(纸本)9781538606247
In recent years, there has been an increasing interest in the implementation of distributed systems for monitoring and controlling different parameters. However, a major problem with this kind of systems is the control. One approach that is being explored for the manipulation of large distributed systems is the use of bio-inspired techniques. Some of these techniques allow the emergence of the control of the system from the combination of the interactions of its components. This type of control has been called Emergent Control. This paper proposes a general architecture for the implementation of Emergent Control and presents examples of its utilization for applications in which Emergent Control can be applied.
Inland waterway management should undergo heavy changes due to a commitment to increase the waterway traffic in a context of climate change. These new constraints will impose an adaptive and resilient management of th...
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