作者：
Jensen, Alexander BirchVilladsen, JørgenAlgorithms
Logic and Graphs Section Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads Building 324 Kongens Lyngby2800 Denmark
We provide a brief description of the GOAL-DTU system for the multi-agentprogramming Contest, including the overall strategy and how the system is designed to apply this strategy. Our agents are implemented using the...
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multi-agents are being increasingly used in many areas, especially in Health systems. In a game the agents’ paradigm provides more autonomy, intelligence and pro-activity. In this context, multi-agents systems can be...
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Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same s...
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ISBN:
(纸本)9781538652213
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. multi-agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to the multiagent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.
This paper presents a hybrid control framework for the motion planning of a multi-agent system including N robotic agents and M objects, under high level goals. In particular, we design control protocols that allow th...
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Using multi-agent Based Simulation (MABS), computing resources requirements often limit the extent to which a model could be experimented. As the number of agents and the size of the environment are constantly growing...
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ISBN:
(纸本)9781450342391
Using multi-agent Based Simulation (MABS), computing resources requirements often limit the extent to which a model could be experimented. As the number of agents and the size of the environment are constantly growing in these simulations, using General-Purpose Computing on Graphics Units (GPGPU) appears to be very promising as it allows to use the massively parallel architecture of the GPU (Graphics Processing Unit) to do High Performance Computing (HPC). Considering the use of GPGPU for developing MABS, the conclusions of Perumalla and Aaby's work [25] in 2008 was twofold: (1) data parallel execution capabilities of GPU can be used effectively in MABS and afford excellent speedup on models and (2) effective use of data parallel execution requires resolution of modeling and execution challenges at the cost of a decrease in modularity, ease of programmability and reusability. In this paper, we propose to study through experiments if the conclusions and issues outlined by Perumalla and Aaby are still true despite the evolution of GPGPU and MABS. To this end, we use the GPU environmental delegation principle on four models in order to compare CPU and GPU implementations. Then, we discuss and analyze the results from both a conceptual and a performance point of view.
Using multi-agent Based Simulation (MABS), computing resources requirements often limit the extent to which a model could be experimented. As the number of agents and the size of the environment are constantly growing...
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ISBN:
(纸本)9781510855083
Using multi-agent Based Simulation (MABS), computing resources requirements often limit the extent to which a model could be experimented. As the number of agents and the size of the environment are constantly growing in these simulations, using General-Purpose Computing on Graphics Units (GPGPU) appears to be very promising as it allows to use the massively parallel architecture of the GPU (Graphics Processing Unit) to do High Performance Computing (HPC). Considering the use of GPGPU for developing MABS, the conclusions of Perumalla and Aaby's work [25] in 2008 was twofold: (1) data parallel execution capabilities of GPU can be used effectively in MABS and afford excellent speedup on models and (2) effective use of data parallel execution requires resolution of modeling and execution challenges at the cost of a decrease in modularity, ease of programmability and reusability. In this paper, we propose to study through experiments if the conclusions and issues outlined by Perumalla and Aaby are still true despite the evolution of GPGPU and MABS. To this end, we use the GPU environmental delegation principle on four models in order to compare CPU and GPU implementations. Then, we discuss and analyze the results from both a conceptual and a performance point of view.
This paper describes a CASE tool for developing complex systems in which heterogeneous and autonomous agents may need to coexist in a complex social and legal framework. Model-Driven Technologies are used to integrate...
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This paper describes a CASE tool for developing complex systems in which heterogeneous and autonomous agents may need to coexist in a complex social and legal framework. Model-Driven Technologies are used to integrate the design of systems of this kind with the verification of the models and with the generation of executable code from these models. The verification module is based on model-checking techniques to check the coherence of a modeled legal context at design time is presented and it is exemplified with a case study. (C) 2011 Elsevier B.V. All rights reserved.
This paper describes a CASE tool for developing complex systems in which heterogeneous and autonomous agents may need to coexist in a complex social and legal framework. Model-Driven Technologies are used to integrate...
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The Mobile Traffic Simulator (MTS) is a simulation tool for wireless communications, specifically for the cellular communication. This tool was developed with the aim of analyzing the capacity of base stations in cell...
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ISBN:
(纸本)9781467329323;9781467329293
The Mobile Traffic Simulator (MTS) is a simulation tool for wireless communications, specifically for the cellular communication. This tool was developed with the aim of analyzing the capacity of base stations in cellular networks for a proper capacity and coverage planning, using the Google Maps. The MTS was developed in a modern programming language for rendering in browsers, JavaScript. It used a multi-agent model considering actual data, and it is very important because allows to simulate real conditions.
The proceedings contain 10 papers. The topics discussed include: a formal model of emotions: integrating qualitative and quantitative aspects;adding organizations and roles as primitives to the JADE framework;belief u...
The proceedings contain 10 papers. The topics discussed include: a formal model of emotions: integrating qualitative and quantitative aspects;adding organizations and roles as primitives to the JADE framework;belief update in agentSpeak-DL;from agents to artifacts back and forth: purposive and doxastic use of artifacts in MAS;GOAL agents instantiate intention logic;non-Marconian agent evolution with EVOLP;normative multi-agent programs and their logics;task suspension in agentsystems;and ten challenges for normative multiagentsystems.
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