Multi-robot coalition formation

As the community strives towards autonomous multi-robot systems, there is a need for these systems to autonomously form coalitions to complete assigned missions. Numerous coalition formation algorithms have been proposed in the software agent literature. Algorithms exist that form agent coalitions in both super additive and non-super additive environments. The algorithmic techniques vary from negotiation-based protocols in multi-agent system (MAS) environments to those based on computation in distributed problem solving (DPS) environments. Coalition formation behaviors have also been discussed in relation to game theory. Despite the plethora of MAS coalition formation literature, to the best of our knowledge none of the proposed algorithms have been demonstrated with an actual multi-robot system. There exists a discrepancy between the multi-agent algorithms and their applicability to the multi-robot domain. This paper aims to bridge that discrepancy by unearthing the issues that arise while attempting to tailor these algorithms to the multi-robot domain. A well-known multi-agent coalition formation algorithm has been studied in order to identify the necessary modifications to facilitate its application to the multi-robot domain. This paper reports multi-robot coalition formation results based upon simulation and actual robot experiments. A multi-agent coalition formation algorithm has been demonstrated on an actual robot system.     

Non-additive multi-objective robot coalition formation

Manifold increase in the complexity of robotic tasks has mandated the use of robotic teams called coalitions that collaborate to perform complex tasks. In this scenario, the problem of allocating tasks to teams of robots (also known as the coalition formation problem) assumes significance. So far, solutions to this NP-hard problem have focused on optimizing a single utility function such as resource utilization or the number of tasks completed. We have modeled the multi-robot coalition formation problem as a multi-objective optimization problem with conflicting objectives. This paper extends our recent work in multi-objective approaches to robot coalition formation, and proposes the application of the Pareto Archived Evolution Strategy (PAES) algorithm to the coalition formation problem, resulting in more efficient solutions. Simulations were carried out to demonstrate the relative diversity in the solution sets generated by PAES as compared to previously studied methods. Experiments also demonstrate the relative scalability of PAES. Finally, three different selection strategies were implemented to choose solutions from the Pareto optimal set. Impact of the selection strategies on the final coalitions formed has been shown using Player/Stage.     

Dynamic coalition formation among rational agents

Dynamic coalition formation (DCF) promises to be well suited for applications of ubiquitous and mobile computing. This article proposes a simulation-based DCF scheme designed to let rational agents form coalitions in dynamic environments.     

A knowledge-based approach to coalition formation

Establishing coalitions among humanitarian aid organizations is a challenge. The CPlanT (Coalition Planning Tool) system uses a multi-agent knowledge-based approach to reduce complexity and communication traffic, while also protecting stakeholder privacy.     

Hedonic coalition formation for optimal deployment

This paper presents a distributed algorithmic solution, termed Coalition formation and deployment algorithm , to achieve network configurations where agents cluster into coincident groups that are distributed optimally over the environment. The motivation for this problem comes from spatial estimation tasks executed with unreliable sensors. We propose a probabilistic strategy that combines a repeated game governing the formation of coalitions with a spatial motion component governing their location. For a class of probabilistic coalition switching laws, we establish the convergence of the agents to coincident groups of a desired size in finite time and the asymptotic convergence of the overall network to the optimal deployment, both with probability 1. We also investigate the algorithm’s time and communication complexity. Specifically, we upper bound the expected completion time of executions that use the proportional-to-number-of-unmatched-agents coalition switching law under arbitrary and complete communication topologies. We also upper bound the number of messages required per timestep to execute our strategy. The proposed algorithm is robust to agent addition and subtraction. From a coalitional game perspective, the algorithm is novel in that the players’ information is limited to the neighboring clusters. From a motion coordination perspective, the algorithm is novel because it brings together the basic tasks of rendezvous (individual agents into clusters) and deployment (clusters in the environment). Simulations illustrate the correctness, robustness, and complexity results.     

Scalable hedonic coalition formation for task allocation with heterogeneous robots

Intelligent Service Robotics - Tasks in the real world are complex and often require multiple robots to collaborate to be serviced. In many cases, a task might require different sensory inputs and...     

Multi-robot mission planning using evolutionary computation with incremental task addition

Intelligent Service Robotics - Mission planning asks the robot to solve complex missions, requiring the robot to execute several actions such that a complex goal condition (called mission) is...     

Sequentially optimal repeated coalition formation under uncertainty

Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed (and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming “new” coalitions to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known), using value of information to define optimal exploration policies. Our framework effectively integrates decision making during repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability of our model to facilitate knowledge transfer across different dynamic tasks.     

基于Agent的电子市场结伴购买算法

以电子商务市场中买方Agent的结伴购买为背景,研究了自利主体的动态结盟问题。基于博弈论,考虑协商花费为常数情况,若所有Agent都采用“底线”策略,存在唯一的纳什平衡解。最后给出了计算底线值的方法及算法。     

A dynamic game of coalition formation under ambiguity

In a previous paper, we generalized to the mixed strategy case the γ model of coalition formation (introduced by Hart and Kurz in Econometrica 51(4):1047–1064, 1983) for situations in which players have ambiguous expectations about the formation of the coalitions in which they are not involved; then we analyzed the corresponding evolutionary games. In this paper, we embody into the model rationality of the players; it follows that allowing for mixed strategies makes it impossible to construct unequivocally a von Neumann–Morgestein expected utility function coherent (in the sense of de Finetti B in Sul Significato Soggettivo della Probabilità, Fundamenta Mathematicae, T, vol XVIII, pp 298–329, 1931) to every strategy profile. We find out that if the multiplicity of coherent beliefs problem is approached by considering “ambiguity loving” players then existence results for classical static equilibria can be obtained in this model. Moreover, we provide conditions for the game to be dynamically playable and we find how the coalition structure beliefs might evolve coherently (according) to the evolution of the strategies.     

Power and welfare in bargaining for coalition structure formation

We investigate a noncooperative bargaining game for partitioning n agents into non-overlapping coalitions. The game has n time periods during which the players are called according to an exogenous agenda to propose offers. With probability \(\delta \), the game ends during any time period \(t<n\). If it does, the first t players on the agenda get a chance to propose but the others do not. Thus, \(\delta \) is a measure of the degree of democracy within the game (ranging from democracy for \(\delta =0\), through increasing levels of authoritarianism as \(\delta \) approaches 1, to dictatorship for \(\delta =1\)). We determine the subgame perfect equilibrium (SPE) and study how a player’s position on the agenda affects his bargaining power. We analyze the relation between the distribution of power of individual players, the level of democracy, and the welfare efficiency of the game. We find that purely democratic games are welfare inefficient and that introducing a degree of authoritarianism into the game makes the distribution of power more equitable and also maximizes welfare. These results remain invariant under two types of player preferences: one where each player’s preference is a total order on the space of possible coalition structures and the other where each player either likes or dislikes a coalition structure. Finally, we show that the SPE partition may or may not be core stable.     

基于人工免疫系统的异构多机器人联盟形成

对动态环境下多机器人联盟形成问题进行了研究,提出了一种基于人工免疫系统的异构多机器人联盟形成方法.该方法在对比人工免疫系统与多机器人系统相似关系的基础上,利用人工免疫系统的隐喻机制为面向动态感知任务的异构多机器人联盟形成问题提供了一种新的思路和解决方法.进行了未知非结构化环境下的多机器人协作搜集仿真实验,仿真结果表明所提方法可以使多机器人系统自主地形成机器人联盟以完成动态感知任务,提高了多机器人系统执行任务的效率.     

基于神经网络的坦克机动任务规划

在未来高技术局部战争务件下,坦克不得不面临一个动态复杂的战场环境,这就要求坦克能根据战场环境实时地进行任务规划。以单辆坦克在陆地上的自主机动为背景,提出了一种基于神经网络的坦克机动任务规划方法。在机动任务规划的实施方面,重点描述了两个基本方面：坦克机动环境的评估及坦克机动任务规划的自主实现。以神经网络方法为基础,提出了实现坦克自主机动的基本思路,并编制应用程序,进行实例检验。结果表明,这种方法科学合理,为坦克机动任务的规划提供了新思路。神经网络方法具有普遍意义,能推广应用于坦克其它方面的任务规划。     

Optimizing coalition formation for tasks with dynamically evolving rewards and nondeterministic action effects

We consider a problem domain where coalitions of agents are formed in order to execute tasks. Each task is assigned at most one coalition of agents, and the coalition can be reorganized during execution. Executing a task means bringing it to one of the desired terminal states, which might take several time steps. The state of the task evolves even if no coalition is assigned to its execution and depends nondeterministically on the cumulative actions of the agents in the coalition. Furthermore, we assume that the reward obtained for executing a task evolves in time: the more the execution of the task is delayed, the lesser the reward. A representative example of this class of problems is the allocation of firefighters to fires in a disaster rescue environment. We describe a practical methodology through which a problem of this class can be encoded as a Markov Decision Process. Due to the three levels of factoring in the resulting MDP (the states, actions and rewards are composites of the original features of the problem) the resulting MDP can be directly solved only for small problem instances. We describe two methods for parallel decomposition of the MDP: the MDP RSUA approach for random sampling and uniform allocation and the MDP REUSE method which reuses the lower level MDP to allocate resources to the parallel subproblems. Through an experimental study which models the problem domain using the fire simulation components of the Robocup Rescue simulator, we show that both methods significantly outperform heuristic approaches and MDP REUSE provides an overall higher performance than MDP RSUA.     

Exogenous coalition formation in the e-marketplace based on geographical proximity

This paper considers a model for exogenous coalition formation in e-marketplaces. Using its informational advantage, an e-retailer creates coalitions of customers based on geographical proximity. Most of the literature regards this process as endogenous: a coalition leader among the buyers bundles eventual purchases together in order to obtain a better bargaining position. In contrast – and in response to what is typically observed in business practice – we analyse a situation in which an existing e-retailer exogenously forms customers’ coalitions. Results of this study are highly encouraging. Namely, we demonstrate that even under highly imperfect warehouse management schemes leading to contagion effects, the proposed combined delivery service may offer significant efficiency gains as well as opportunities for Pareto improvement.     

Autonomous underwater vehicles: Hybrid control of mission and motion

This paper provides an experimental implementation and verification of a hybrid (mixed discrete state/ continuous state) controller for semi-autonomous and autonomous underwater vehicles in which the missions imply multiple task robot behavior. An overview of some of the missions being considered for this rapidly developing technology is mentioned including environmental monitoring, underwater inspection, geological survey as well as military missions in mine countermeasures.The functionalities required of such vehicles and their relation to intelligent control technology is discussed. In particular, the use of Prolog as a computer language for the specification of the discrete event system (DES) aspects of the mission control is proposed. The connections between a Prolog specification and the more common Petri Net graphical representation of a DES are made. Links are made between activation commands, transitioning signals, and the continuous state dynamic control system (DCS) responsible for vehicle stabilization.Details are given of the NPS Phoenix vehicle implementation at the present time, together with experimental validation of the concepts outlined using a simplified example mission. The paper ends with a listing of questions and concerns for the evaluation of software controllers. A list of references is given for readers interested in this subject.     

Negotiation support systems and team negotiations: The coalition formation perspective

The use of software to support negotiations has captured the attention of academics and practitioners for some three decades and the research stream of negotiation support systems (NSS) has emerged. Over the years, many NSS have been developed and used in training and research but they have been rarely deployed in organizations. Our speculation is that much existing research is confined to dyadic (i.e., one-to-one) settings which may not adequately reflect the real-world situations in which teams, rather than individuals, often engage in negotiations. To address the gap, our current research aspires to conceptualize the NSS in supporting team negotiations and to theoretically examine their impact. Coalition formation has been a prevalent organizational phenomenon that constitutes important dynamics in any negotiating team; it will be conceptualized as the mechanism through which NSS impacts upon team negotiation outcomes in our paper. Globalization has rendered cross-cultural negotiations a commonplace; at the same time, culture serves as a most salient attribute in activating coalitions. In light of the above, cultural diversity is studied as the antecedent to coalition formation and moderates the influence of NSS on the extent of coalition formation. A set of research propositions, derived from a theoretical framework, are raised and their implications discussed.     

基于图约束联盟形成的社会共享乘车问题

对社会共享乘车问题进行研究.在对图约束联盟形成及其求解分析的基础上,将社会共享乘车问题转化为一个受社交网络约束的图约束联盟形成问题;建立起一个社会共享乘车问题模型,得到该问题模型的最佳联盟结构以及最优路径,采用一种改进的分支定界方法来求解这个社会共享乘车问题,使该系统的社会福利最大化.实验结果表明,提出算法模型能够改善...     

Mechanism design for coalition formation and cost sharing in group-buying markets

We study the mechanism design problem of coalition formation and cost sharing in a group-buying electronic marketplace, where buyers can form coalitions to take advantage of volume based discounts. The desirable mechanism properties include stability (in the core), and incentive compatibility with good efficiency. We show the impossibility to simultaneously satisfy efficiency, budget balance and individual rationality at a Bayesian–Nash equilibrium, and propose a mechanism in the core of the game. We then present and evaluate a group of reasonable mechanisms. Empirical results show positive correlation between stability and incentive compatibility (which is in turn related to efficiency).     

A group AHP-TOPSIS framework for human spaceflight mission planning at NASA

Human spaceflight mission planning is a complex task with many interacting systems and mission phases. Analog missions are Earth-based science missions whose purpose is to help understand the complexities inherent in future human spaceflight missions. The goal of performing an analog mission is to prepare crewmembers and support teams for future space missions in a low risk-low cost environment by repeatedly testing vehicles, habitats, and surface terrain simulators. This study presents a group multi-attribute decision making (MADM) framework developed at the Johnson Space Center (JSC) for the Integrated human exploration mission simulation facility (INTEGRITY) project to assess the priority of human spaceflight mission simulators. The proposed framework integrates subjective judgments derived from the analytic hierarchy process (AHP) with the entropy information and the technique for order preference by similarity to the ideal solution (TOPSIS) into a series of preference models for the human exploration of Mars. Three different variations of TOPSIS including conventional, adjusted and modified TOPSIS methods are considered in the proposed framework.