Multi-Agent Coordination: DCOPs and Beyond

Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages: it can represent a wide range of problem domains, and it supports the development of generic algorithms to solve them. Firstly, this paper presents some advances in both complete and approximate DCOP algorithms. Secondly, it explains that the DCOP model makes a number of unrealistic assumptions that severely limit its range of application. Finally, it points out hints on how to tackle such limitations. 1 Distributed constraint optimization Distributed constraint optimization problems (DCOPs) are a model for representing multi-agent systems in which agents cooperate to optimize a global objective. The DCOP model has two main advantages. Firstly, it can represent a wide range of problem domains such as wireless sensor networks [Zhang et al., 2005], peer-to-peer networks [Faltings et al., 2006], meeting scheduling [Maheswaran et al., 2004], and traffic control [Junges and Bazzan, 2008]. Secondly, it supports the development of generic solving algorithms. Therefore, researchers have developed several complete algorithms such as ADOPT [Modi et al., 2005], DPOP [Petcu and Faltings, 2005], and its generalization GDL [Aji and McEliece, 2000; Vinyals et al., 2010b]. Nevertheless, DCOPs are shown to be NP-Hard [Modi et al., 2005]. Being complete, the main advantage of these algorithms is that they guarantee the maximum possible quality: optimality. However, they scale poorly when the number of agents increases, regarding both computational and communication requirements. Function filtering is a promising technique [Brito and Meseguer, 2010] to achieve better scalability. Basically, given ∗This work has been funded by projects EVE (TIN200914702-C02-01 and 02), Agreement Technologies (CONSOLIDER CSD2007-0022), RECEDIT (TIN2009-13591-C02-02) and Generalitat de Catalunya (2009-SGR-1434 and 2009-SGR-362). Marc Pujol-Gonzalez is supported by the Ministry of Science and Innovation (BES-2010-030466) a method to compute approximations of cost functions and a candidate solution, function filtering allows pruning regions of the solution space that only contain non-optimal solutions. Notice that some application domains are specially communication constrained, whereas others are mainly computationally constrained. For instance, data transmission is severely limited in wireless sensor networks, and bandwidth is a scarce resource in peer-to-peer networks. Conversely, meeting scheduling and traffic control are usually computationally constrained because they mainly operate over high-speed networks. Such distinctions between resourceconstrained settings motivated us to study different function approximation methods to be employed along with function filtering, to reduce either communication or computation requirements as much as possible. First, in [Pujol-Gonzalez et al., 2011] we presented a novel class of approximation techniques, the so-called top-down approximations. Combining these new techniques with function filtering, we managed to reduce communication requirements by as much as two orders of magnitude, while keeping computational requirements at bay. As a consequence, the resulting algorithm appears as a very good candidate to solve DCOPs optimally in communication-constrained scenarios. Currently, we are working on improving the effectiveness of function filtering in computationally-constrained settings. Since function filtering’s pruning is based on lower and upper bounds on the optimal solution cost, tightening such bounds increases the amount of pruning. Because more pruning means a reduction on the solution space to explore, agents require less computational resources (both CPU and memory) to solve the same problem. Likewise, these savings in computational resources also increase the range of problems that can be solved optimally by algorithms employing function filtering. In fact, preliminary results indicate that our improvements allow agents to solve up to 75% more problem instances given the same resource constraints. Another approach to improve the scalability of DCOP algorithms is to drop optimality in favor of lower complexity, approximate algorithms. Traditionally, these algorithms have not offered any quality guarantees at all [Zhang et al., 2005], but recent works have been able to provide offline bounds for some of them [Farinelli et al., 2009; Kiekintveld et al., 2010]. The disadvantage of such offline bounds is that they are generally very weak. Thus, we provided a new class of al2838 Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence