Correlation Clustering Based Coalition Formation For Multi-Robot Task Allocation

In this paper, we study the multi-robot task allocation problem where a group of robots needs to be allocated to a set of tasks so that the tasks can be finished optimally. One task may need more than one robot to finish it. Therefore the robots need to form coalitions to complete these tasks. Multi-robot coalition formation for task allocation is a well-known NP-hard problem. To solve this problem, we use a linear-programming based graph partitioning approach along with a region growing strategy which allocates (near) optimal robot coalitions to tasks in a negligible amount of time. Our proposed algorithm is fast (only taking 230 secs. for 100 robots and 10 tasks) and it also finds a near-optimal solution (up to 97.66% of the optimal). We have empirically demonstrated that the proposed approach in this paper always finds a solution which is closer (up to 9.1 times) to the optimal solution than a theoretical worst-case bound proved in an earlier work

MULTIAGENT SYSTEMS FOR SHOP FLOOR ARHITECTURE MANAGEMENT

The paper presents the problem of shop floor agility. In order to cope with the disturbances and uncertainties that characterise the current business scenarios faced by manufacturing companies, the capability of their shop floors needs to be improved quickly, such that these shop floors may be adapted, changed or become easily modifiable (shop floor reengineering). One of the critical elements in any shop floor reengineering process is the way the control/supervision architecture is changed or modified to accommodate for the new process and equipment. This paper, therefore, proposes an multi-agent architecture to support the fast adaptation or changes in the control/supervision architecture.multi-agent system, shop floor agility, control/supervision architecture, virtual organisation.

Simple Coalitional Games with Beliefs

We introduce coalitional games with beliefs (CGBs), a natural generalization of coalitional games to environments where agents possess private beliefs regarding the capabilities (or types) of others. We put forward a model to capture such agent-type uncertainty, and study coalitional stability in this setting. Specifically, we introduce a notion of the core for CGBs, both with and without coalition structures. For simple games without coalition structures, we then provide a characterization of the core that matches the one for the full information case, and use it to derive a polynomial-time algorithm to check core nonemptiness. In contrast, we demonstrate that in games with coalition structures allowing beliefs increases the computational complexity of stability-related problems. In doing so, we introduce and analyze weighted voting games with beliefs, which may be of independent interest. Finally, we discuss connections between our model and other classes of coalitional games

Coalition Formation and Execution in Multi-robot Tasks

In this research, I explore several related problems in distributed robot systems that must be addressed in order to achieve multi-robot tasks, in which individual robots may not possess all the required capabilities. While most previous research work on multi-robot cooperation mainly concentrates on loosely-coupled multi-robot tasks, a more challenging problem is to also address tightly-coupled multi- robot tasks involving close robot interactions, which often require capability sharing. Three related topics towards addressing these tasks are discussed, as follows: Forming coalitions, which determines how robots should form into subgroups (i.e., coalitions) to address individual tasks. To achieve system autonomy, the ability to identify the feasibility of potential solutions is critical for forming coalitions. A general IQ-ASyMTRe architecture, which is formally proven to be sound and complete in this research, is introduced to incorporate this capability based on the ASyMTRe architecture. Executing coalitions, which coordinates different robots within the same coalition during physical execution to accomplish individual tasks. For executing coalitions, the IQ-ASyMTRe+ approach is presented. An information quality measure is introduced to control the robots to maintain the required constraints for task execution in dynamic environment. Redundancies at sensory and computational levels are utilized to enable execution that is robust to internal and external influences. Task allocation, which optimizes the overall performance of the system when multiple tasks need to be addressed. In this research, this problem is analyzed and the formulation is extended. A new greedy heuristic is introduced, which considers inter-task resource constraints to approximate the influence between different assignments in task allocation. Through combining the above approaches, a framework that achieves system autonomy can be created for addressing multi-robot tasks