Multi-agent team cooperation: A game theory approach

Semsar-Kazerooni, Elham; Khorasani, K.

doi:10.1016/j.automatica.2009.06.006

Cited by 212 publications

(98 citation statements)

References 26 publications

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“…In collaborative multi-agent planning, a team of agents try to accomplish a task leading to a common goal by combining their capabilities and knowledge [15]. Two main approaches for collaborative multi-agent planning can be distinguished: (i) centralised, which involves a planner agent with full knowledge of the environment and the joint task to undertake, and (ii) distributed or decentralised [4], in which agents plan individually and coordinate with each other to find a common solution for the planning problem [10,13]. Centralised multi-agent planning is typically the most efficient, but is only feasible if agents do not have private or sensitive information [1].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

Palomares

Bauters

Liu

et al. 2016

Lecture Notes in Computer Science

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Abstract. In a team of multiple agents, the pursuance of a common goal is a defining characteristic. Since agents may have different capabilities, and effects of actions may be uncertain, a common goal can generally only be achieved through a careful cooperation between the different agents. In this work, we propose a novel two-stage planner that combines online planning at both team level and individual level through a subgoal delegation scheme. The proposal brings the advantages of online planning approaches to the multi-agent setting. A number of modifications are made to a classical UCT approximate algorithm to (i) adapt it to the application domains considered, (ii) reduce the branching factor in the underlying search process, and (iii) effectively manage uncertain information of action effects by using information fusion mechanisms. The proposed online multi-agent planner reduces the cost of planning and decreases the temporal cost of reaching a goal, while significantly increasing the chance of success of achieving the common goal.

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Section: Introductionmentioning

confidence: 99%

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

Palomares

Bauters

Liu

et al. 2016

Lecture Notes in Computer Science

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“…When the decision is made in several fields, the game is called multidimensional game theory [64]. The gaming model including two players is G = (S 1 , S 2 , u 1 , u 2 ) where S i , i = 1, 2 is the strategy space of each player and u i , i = 1, 2 is the payoff function of each player.…”

Section: Games Theory Reasoningmentioning

confidence: 99%

Multi Aerial Robot Planning

Sebbane

2014

Intelligent Systems, Control and Automation: Science and Engineering

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Multi-robot systems are a major research topic in robotics. Designing, testing and deploying in the real world a large number of aerial robots is a concrete possibility due to the recent technological advances. The first section of this chapter treats the different aspects of cooperation in a multi-agent systems. A cooperative control should be designed in terms of the available feedback information. A cascade-type guidance law is proposed, followed by consensus approach and flocking behavior. Since information flow over the network changes over time, cooperative control must react accordingly but ensure group cooperative behavior which is the major issue in analysis and synthesis. Connectivity and convergence of formations are also studied. Team approach is followed by deterministic decision making. Plans may be required for a team of aerial robots to plan for sensing, plan for action or plan for communication. Distributed receding horizon control as well as conflict resolution, artificial potentials and symbolic planning are thus analyzed. Then, association with limited communications is studied, followed by genetic algorithms and game theory reasoning. Next, multi-agent decision making under uncertainty is considered, formulating the Bayesian decentralized team decision problem, with and without explicit communication. Algorithms for optimal planning are then introduced as well as for task allocation and distributed chance constrained task allocation. Finally, some case studies are presented such as reconnaissance mission that can be defined as the road search problem or the general vehicle routing problem. Then, an approach is considered to coordinate a group of aerial robots without a central supervision, by using only local interactions between the robots. The third case is the optimization of perimeter patrol operation. If an aerial robot must be close from a location to monitor it correctly and the number of aerial robots does not allow covering each site simultaneously, a path planning problem arises. Finally stochastic strategies for surveillance are presented.Y.

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“…Information structure and neighboring sets [13] . To ensure cooperation among agents, each agent has to know the status of the other agents, and therefore, agents have to communicate with each other.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Target tracking and obstacle avoidance for multi-agent networks with input constraints

Yan

Guan

Luo

et al. 2011

Int. J. Autom. Comput.

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In this paper, the problems of target tracking and obstacle avoidance for multi-agent networks with input constraints are investigated. When there is a moving obstacle, the control objectives are to make the agents track a moving target and to avoid collisions among agents. First, without considering the input constraints, a novel distributed controller can be obtained based on the potential function. Second, at each sampling time, the control algorithm is optimized. Furthermore, to solve the problem that agents cannot effectively avoid the obstacles in dynamic environment where the obstacles are moving, a new velocity repulsive potential is designed. One advantage of the designed control algorithm is that each agent only requires local knowledge of its neighboring agents. Finally, simulation results are provided to verify the effectiveness of the proposed approach.

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Multi-agent team cooperation: A game theory approach

Cited by 212 publications

References 26 publications

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

A Two-Stage Online Approach for Collaborative Multi-agent Planning Under Uncertainty

Multi Aerial Robot Planning

Target tracking and obstacle avoidance for multi-agent networks with input constraints

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