Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents

Abstract

Previous approaches to select agents to form a team rely on single-agent capabilities, and team performance is treated as a sum of such known capabilities. Motivated by complex team formation situations, we address the problem where both single-agent capabilities may not be known upfront, e.g., as in ad hoc teams, and where team performance goes beyond single-agent capabilities and depends on the specific synergy among agents. We formally introduce a novel weighted synergy graph model to capture new interactions among agents. Agents are represented as vertices in the graph, and their capabilities are represented as Normally-distributed variables. The edges of the weighted graph represent how well the agents work together, i.e., their synergy in a team. We contribute a learning algorithm that learns the weighted synergy graph using observations of performance of teams of only two and three agents. Further, we contribute two team formation algorithms, one that finds the optimal team in exponential time, and one that approximates the optimal team in polynomial time. We extensively evaluate our learning algorithm, and demonstrate the expressiveness of the weighted synergy graph in a variety of problems. We show our approach in a rich ad hoc team formation problem capturing a rescue domain, namely the RoboCup Rescue domain, where simulated robots rescue civilians and put out fires in a simulated urban disaster. We show that the weighted synergy graph outperforms a competing algorithm, thus illustrating the efficacy of our model and algorithms.

Hybrid mission planning with coalition formation

“…Greedy algorithms can derive solutions quickly, but make no guarantees on the solution quality (Shehory and Kraus, 1998;Vig and Adams, 2006b;Ramchurn et al, 2010;Service and Adams, 2011;Sujit et al, 2014). Approximation algorithms provide solution quality guarantees, but suffer from poor worst-case run-time complexity, which can render them inappropriate for real-time applications (Dang and Jennings, 2004;Rahwan et al, 2009;Liemhetcharat and Veloso, 2014). Market-based techniques offer fault-tolerance for a distributed system, but have high communication processing requirements (Dias, 2004;Dias et al, 2005;Vig and Adams, 2006a;Shiroma and Campos, 2009;Service et al, 2014).…”

Allocating Multiple Types of Tasks to Heterogeneous Agents Based on the Theory of Comparative Advantage

“…How to optimally assign a set of robots to a set of tasks is well known as multirobot task allocation (MRTA) problem [13]. Liemhetcharat and Veloso [14] suggested that "the MRTA problem is categorized along three axes: single-task robots (ST) versus multitask robots (MT), single-robot tasks (SR) versus multirobot tasks (MR), and instantaneous assignment (IA) versus time-extended assignment (TA)." In the example shown in Figure 1, each robot accomplishes its advantageous task.…”

Computing Coalitions in Multiagent Systems: A Contextual Reasoning Approach

“…[18,30]; and solutions from the field of robotics based on schema theory, e.g. [35,36] or synergy [22]. The distinct characteristics of our approach are: (a) it allows agents to use different knowledge representation models; (b) based on a non-monotonic reasoning model, it enables representing and reasoning with agents with conflicting goals; and (c) it provides both centralized and distributed algorithms for computing coalitions, and can hence be applied in settings with different requirements for information hiding and sharing.…”