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...ould besperformed in a distributed fashion. They propose an onlinesdynamic task allocation system for reallocation to achieve asteam goal that can respond to and recover from real timescontingencies. =-=[13, 14]-=- present a reasonable system thatsenables a group of heterogeneous robots to form coalitions tosaccomplish a multi-robot task using tightly coupled sensorssharing. The advantages of this new approach ...

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...y be more appropriate for that task (e.g., [25, 18]). 2. Auction based methods, where each task is submitted for bids to a set of agents, and a final allocation is made from the bids submitted (e.g., =-=[30, 10, 12, 31, 34]-=-). A comparison of auction-based methods and token-based approaches [33] shows that auctions produce higher rewards but require more communications. However, Kalra et al. [13] compared both approaches...

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... applications specific to multi-robot coordination include search and rescue [26, 49, 8], robotic soccer [19], mobile sensor networks [16, 30], mine collection [11], and patrol with adversaries [2]. Approaches in development of teams or coalitions within multiagent systems include utilizing inductive logic programming among individuals for path planning [17], forming coalitions [31, 32], and ad-hoc team development [36, 27]. Negotiated learning architectures have been employed in the formation of coalitions as well [34, 14], while the dynamics of market-based coalitions has also been examined [39]. Many interesting applications of partnerships within multiagent systems have been utilized for the demonstration of research in the area, the majority of which being variations on the box pushing domain [45]. Through the use of heterogeneous teams however, role allocation has been applied through the concept of joint intentions [24] and mobile robots have successfully organized an environment of pucks [18]. Biological inspiration is widely researched in the field of multiagent systems due to the many parallels that can be drawn to swarms and collective intelligence in nature [9]. For example...

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...ered domains where tasks are interrelated by precedence and simultaneity constraints [9] [8]. Other work has explicitly addressed domains where tasks require the joint efforts of multiple agents [15] =-=[14]-=- [12]. While these approaches support reasoning about precedence constraints and interdependent agent schedules, none of these approaches extensively explore time-extended allocation or are well suite...

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...Jones et al., 2011). The MRTA problem has also been combined with techniques from multi-agent coordination and optimization techniques such as negotiation (Viguria et al., 2007), coalition formation (=-=Tang and Parker, 2007-=-), reinforcement learning (Schneider et al., 2005), vector regression learning (Jones et al., 2006), Hungarian algorithm (Liu and Shell, 2011), vacancy chains (Dahl et al., 2009), and dynamic vehicle ...

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...c team development [36, 27]. Negotiated learning architectures have been employed in the formation of coalitions as well [34, 14], while the dynamics of market-based coalitions has also been examined =-=[39]-=-. Many interesting applications of partnerships within multiagent systems have been utilized for the demonstration of research in the area, the majority of which being variations on the box pushing do...

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...aper. Multi-robot task allocation has been formally analyzed by Gerkey and Mataric in [7]. Market-based approach to multi-robot coordination problem has been applied (e.g., for site clearing) in [8], =-=[9]-=- and used in the work of Tang and Parker [9]. Robots first form coalitions at the low level to solve a single multi-robot task. Coalitions, and possibly individual robots, then compete for tasks at th...