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Distributed Task Assignment is a convenient abstraction for load-balancing applications, workflow systems or supply-chain management. The topological features of such task networks are far from random but instead resemble that of small-worlds and scale-free networks. The agent's interaction is accordingly prescribed by this network structure. Simulating decentralised optimisation algorithms using...
It is crucial for embedded systems to adapt to the dynamics of open environments. This adaptation process becomes especially challenging in the context of multiagent systems. In this paper, we argue that multiagent meta-level control is an effective way to determine when this adaptation process should be done and how much effort should be invested in adaptation as opposed to continuing with the current...
The paper proposes a biologically-inspired cognitive agent model, known as FALCON-X, based on an integration of the Adaptive Control of Thought (ACT-R) architecture and a class of self-organizing neural networks called fusion Adaptive Resonance Theory (fusion ART). By replacing the production system of ACT-R by a fusion ART model, FALCON-X integrates high-level deliberative cognitive behaviors and...
If we imagine a dynamic environment whose behavior may change in time we can figure out the difficulties that agents located there will have trying to solve problems related to this environment. Changes in an environment e.g. a market, can be quite drastic: from changing the dependencies of some products to add new actions to build new products. The agents should try to cooperate or compete against...
In this paper we propose a mechanism of prediction of domestic human activity in a smart home context. We use those predictions to adapt the behavior of home appliances whose impact on the environment is delayed (for example the heating). The behaviors of appliances are built by a reinforcement learning mechanism. We compare the behavior built by the learning approach with both a merely reactive behavior...
This paper has the objective of presenting inteligent-agent methodology to evaluate the Air Traffic Flow Management (ATFM) scenario which aggregates the Dynamic Density criterion. The new evaluation function is proposed to determine the rewards in a reinforcement learning ATFM agent. The paper includes a specific analysis focused on the air scenario observed in Brazilian airspace. The comparative...
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