We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workloadbased solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the bestcase scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem.

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ABC4MAS provides an environment to support the rapid assembly of agent-oriented business collaborations. ABC4MAS allows: (i) to set up a collaboration environment (CE) as a virtual organisation; (ii) to reach agreements within the CE to form short-term business collaborations; (iii) to enact business collaborations; and (iv) to track the performance of agents within business collaborations to build their trust and reputation within the CE.