By Joaquim Filipe, Ana Fred
This ebook constitutes the completely refereed post-conference complaints of the fifth foreign convention on brokers and synthetic Intelligence, ICAART 2013, held in Barcelona, Spain, in February 2013. The 20 revised complete papers offered including one invited paper have been conscientiously reviewed and chosen from 269 submissions. The papers are equipped in topical sections on synthetic intelligence and on agents.
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Additional resources for Agents and Artificial Intelligence: 5th International Conference, ICAART 2013, Barcelona, Spain, February 15-18, 2013. Revised Selected Papers
Intervals. The upper chart shows the target proﬁle (dashed line) and the resulting aggregated power output (solid line). The individual power output proﬁles of the devices are depicted in the lower chart. The latter is quite chaotic, which is due to the limited sets of available power output proﬁles per device. 5 kW per time step in the planning horizon. In Fig. 2, the process of the heuristic for this simulation run is shown in detail. This data is visible to the simulation observer only, the individual agents still act upon local knowledge.
Intervals. 32 C. Hinrichs et al. Fig. 2. Detailed illustration of the COHDA heuristic during a simulation. 3 Results As a ﬁrst step, we examined the general behavior of the heuristic without penalties. In Fig. 1, the results of a single simulation run (m = 30 devices with n = 2000 possible power proﬁles each) are visualized. The planning horizon was set to four hours in 15-min. intervals. The upper chart shows the target proﬁle (dashed line) and the resulting aggregated power output (solid line).
Of its elements wij with respect to the common global target c. More formally, an agent ai has to ﬁnd an assignment of its own selection variables xij , such that the objective function in (1) is minimized globally. Definition 1. A selection of an agent ai is a tuple γi = i, j where i is the identifier of ai , and j identifies the selected element wij such that xij = 1 and ni j=1 xij = 1. Agents are autonomous, so they may change their selection at any time. Therefore, we need to deﬁne the state of an agent.
Agents and Artificial Intelligence: 5th International Conference, ICAART 2013, Barcelona, Spain, February 15-18, 2013. Revised Selected Papers by Joaquim Filipe, Ana Fred