By Béatrice Duval, Jaap van den Herik, Stephane Loiseau, Joaquim Filipe
This publication constitutes the completely refereed post-conference lawsuits of the sixth overseas convention on brokers and synthetic Intelligence, ICAART 2014, held in Angers, France, in March 2014.
The 21 revised complete papers awarded including one invited paper have been rigorously reviewed and chosen from 225 submissions. The papers are prepared in topical sections on brokers and on man made intelligence.
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Additional info for Agents and Artificial Intelligence: 6th International Conference, ICAART 2014, Angers, France, March 6–8, 2014, Revised Selected Papers
A robot is greedy and will start collecting a known block that is the closest one and has the needed color). In the baseline, we have identiﬁed four dimensions of variation: which room a robot initially will visit, how a robot uses knowledge obtained about another robot through interference, how it selects a (next) room to visit, and what a robot will do when holding a block that is not needed now but needed later. Initial Room Selection. At the beginning of the task, a robot has to choose a room to explore since it does not have any information about the dispersed blocks in the environment.
Most of the current research on foraging, however, only has implicit communication for robot teams . The work in [3,5] studies communication in the simple foraging task without ordering constraints on the team goal and its impact on the completion time of the task. The work in  compares diﬀerent communication conditions where robots do not communicate, communicate the main behavior that they are executing, and communicate their target locations. Roughly these conditions map with our no communication, communicating only beliefs, and communicating only goals, whereas we also study the case where both beliefs and goals are exchanged.
Each agent-based model features a certain agent deﬁnition and architecture which can include several dimensions. In the ﬁrst place, it is essential to deﬁne Agent-Based Simulation of Interconnected Wholesale Electricity Markets 35 conceptually what the agent represents in the model. In the ﬁeld of Agent-based Computational Economics (ACE) agents generally are deﬁned as having a set of data and pre-deﬁned behavioural rules within a computationally constructed world . Agent architecture includes the design of speciﬁc agent decision models including adaptive learning algorithms.