By Michal Pechoucek, Simon G. Thompson, Holger Voos
This number of lately released and refereed papers comes from workshops and colloquia held during the last years. The papers describe the advance of command and regulate platforms, army communications structures, info platforms, surveillance platforms, self reliant cars, simulators, and HCI. the gathering presents for the 1st time an summary of the main major advances within the expertise of clever brokers.
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Additional resources for Defense Industry Applications of Autonomous Agents and Multi-Agent Systems (Whitestein Series in Software Agent Technologies and Autonomic Computing)
Note that D 3 is able to handle more speciﬁc cases than D 1 and D2 . α would like to assign either a UGV or a UAV to his mission (preferring a UAV), and thus has the goals assigned(mission(m), uav(predator)), assigned(mission(m), ugv(sentry)) With a higher utility being given to the former goal. Both agents are aware of the following facts: hasT ask(mission(m), task(t)) higherP riority(mission(m), mission(n)) capable(t, uav(predator)) implies(recentRain(l), sand(l), mud(l)) ugv(sentry) taskLocated(t, l) atLocation(h, l) Agent α also believes that available(uav(predator)), hasRoad(l) and, believes there is a good chance that, if necessary reassign(mission(m), uav(predator)) would work.
2. Matchmaking abstract architecture A matchmaking application is not entirely characterised by the semantic relationships that might be established among concepts. An important issue of a matchmaking application is the distinction between the attribute-level and the component-level: a component may be described by different attributes, and so different matching schemas could be applied to each attribute depending on the particular meaning or role it plays within the component. In our application, we have identiﬁed two main kinds of components to be matched against the ISR requirements of a mission, each one characterised by different attributes that deserve a separate treatment.
Both are underpinned by Bayesian Decision Theory but diﬀer in their implementation details depending on whether the sensor nodes have a common objective or unequal 26 D. D. Ramchurn and A. Rogers Figure 5. Surveillance scenario involving multiple sensing agents and multiple users. Of interest is how the agents and users should interact to gather relevant data in a rapid and eﬀective manner. objectives. Each variant was evaluated in simulation against a target identiﬁcation problem (represented by a Bayesian Network).
Defense Industry Applications of Autonomous Agents and Multi-Agent Systems (Whitestein Series in Software Agent Technologies and Autonomic Computing) by Michal Pechoucek, Simon G. Thompson, Holger Voos