To solve the problem of group task allocation with time constraints in open and dynamic network environments, this paper proposes a decentralized combinatorial auction-based approach for group task allocation. In the proposed approach, both resource providers and consumers are modeled as intelligent agents. The proposed approach is decentralized, so all the agents are limited to communicating w...

For partitionable Mesh architectures, a modified QT (Quad Tree)-based processor allocation scheme (MQT) with two new task embedding heuristics is proposed. Existing processor allocation strategies assume any incoming request is also represented as a mesh. This paper introduces flexibility with which the requested task can be a linear array, a ring, or a mesh and still be allocated into a partit...

Mutual cooperation in Multi-Agent Systems is an important means to implement complex applications. The static task allocation is one of the key issues in agent cooperation mechanism. The latency of the network infrastructure, dynamic topology and node heterogeneity are important features of the agent system, and they bring new challenges to the static task allocation in multi-agent environment....

Communication constraints dictated by hardware often require a multi-robot system to make decisions and take actions locally. Unfortunately, local knowledge may impose limits that ultimately impede global optimality in a decentralized optimization problem. This paper enhances a recent anytime optimal assignment method based on a task-swap mechanism, redesigning the algorithm to address task all...

Systematic task allocation to different development sites in global software development projects can open business and engineering perspectives and help to reduce risks and problems inherent in distributed development. Relying only on a single evaluation criterion such as development cost when distributing tasks to development sites has shown to be very risky and often does not lead to success...

Multi-Agent system, which is relatively a recent term, can be viewed as a sub-field of Distributed AI. In order to construct and solve complex real world problems like task allocation in an organization, a number of agents can perform work cooperatively and collaboratively. To achieve the maximum system utility through the proper task allocation among agents, there is a notion of dynamic team f...

We explore a class of task allocation mechanisms that are incremental and can be tuned to the computational resource limitations of agents. Our focus is on distributed task and resource allocation problems involving coalitions of cooperative agents that must negotiate among themselves on the distribution of tasks. Our emphasis is on the design of mechanisms with desirable real-time and dynamic ...

Auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents can be trusted to effectively perform assigned tasks. However, reliable performance of team members may not always be a valid assumption. An approach to learning team member performance is presented, which enables more efficient task ass...