Advances in practical optimal coalition structure algorithms

This thesis presents a number of algorithms for forming coalitions among cooperative agents in pragmatic domains where traditional cooperative game theory solution concepts do not apply due to bounded rationality of agents. While previous work in coalition formation in multi-agent systems research operated on relatively small number of agents, e.g. less than 30 agents, this work explores coalition formation among 100 agents, this is due to limited computational resources not the performance of the our algorithms. We explore a bestfirst search centralized algorithm for optimal coalition structures which is based on a novel idea of deciding what is the best coalition to put into coalition structure being generated. Empirical results show that the solution reaches optimality quickly and terminates quickly in pragmatic domains. We further explore on optimal coalition structures with distributed algorithms in linear and non-linear domains. For the linear domains, we explore linear production and integer programming. For the non-linear domains we explore logistic providers. Based on existing algorithms, we explore a novel environment of forming coalitions in supply networks involving buyers, sellers and logistics providers agents. In this setting, buyers form coalitions to increase their negotiation power while sellers and logistics providers form coalitions to aggregate their supply power and optimize their resources usage

Agent-based coalitions in dynamic supply chains

Coalition formation is an important issue in multi-agent systems. Recent work in the area has focused on reducing the complexity of forming coalitions, i.e., each agent deliberately searches for potential coalition members before negotiating with them. We propose a framework for coalition formation of agents in a dynamic supply chain environment. The framework is composed of a negotiation protocol and a decision mechanism. The negotiation protocol allows thorough communication among agents across sectors (buyers, sellers, logistic providers). With the decision mechanism, agents take two steps to form coalitions: i) agents in each sector form loosely-coupled coalitions in order to decrease the complexity of the negotiation, and ii) agents form coalitions across sectors in order to deliver goods to end customers. We provide an example of how they can help agents form coalitions successfully

A Framework to Support Coalition Formation in Supply Chain Collaboration

This paper proposes a framework for agents in globally collaborative supply chain by applying the concept of coalition formation, a cooperation game in game theory. This framework provides mutual benefits to every party involved buyers, sellers and logistics providers. It provides a common gateway that allows individual parties to locate the right partners, negotiate with them, and form coalition in the best possible ways. The framework is applicable to real world e-business models, including B2C, B2B, supply chain and logistics, SME, etc. We firstly discuss common needs existing in today e-business. We then discuss about our framework, i.e., negotiation protocol and decision mechanism

Towards optimal service composition upon QoS in agent cooperation

It is quite common in tourism industry that a tourist would love to gain the most wonderful experience from visiting multiple places in one trip. This is a service composition problem and is difficult to manage because of several reasons. We address this problem by proposing an agent-based service composition framework to allocate to the tourist an optimal composite service. We take into account a number of factors including: 1) all the places of interest must be visited; 2) the preference on visiting places must be obeyed; 3) the total price is within the budget; 4) the time constraint must be obeyed; 5) the payoffs for service providers are worthwhile and fair. We propose a bottom-up approach to allocate the optimal service composition where intelligent agents are deployed to provide flexibility and efficiency to the system. As a result, the system is more independent and every party is better off

A best-first anytime algorithm for computing optimal coalition structures

This work presents a best-first anytime algorithm for computing optimal coalition structures. The approach is novel in that it generates coalition structures based on coalition values, while existing algorithms base their generation on the structure (members and configurations) of coalitions. With our algorithm, coalition structures are generated by repeatedly choosing the best coalition, as determined using a novel metric called agent\u27s contribution to coalition structure that we define. We have compared the performance of our algorithm against that of Rahwan et al [5] using 20 data distributions. Our results show that our algorithm almost always converges on an optimal coalition structure faster (although it terminates later in some cases). Empirically, our algorithm almost always yields better than or as good as Rahwan et al\u27s results at any point in time. Copyright 2008, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

A pruning-based algorithm for computing optimal coalition structures in linear production domains

Computing optimal coalition structures is an important research problem in multi-agent systems. It has rich application in real world problems, including logistics and supply chains. We study computing optimal coalition structures in linear production domains. The common goal of the agents is to maximize the system’s profit. Agents perform two steps: i) deliberate profitable coalitions, and ii) exchange computed coalitions and generate coalition structures. In our previous studies, agents keep growing their coalitions from the singleton ones in the deliberation step. This work takes opposite approach that agents keep pruning unlikely profitable coalitions from the grand coalition. It also relaxes the strict condition of coalition center, which yields the minimal cost to the coalition. Here, agents merely keep generating profitable coalitions. Furthermore, we introduce new concepts, i.e., best coalitions and pattern, in our algorithm and provide an example of how it can work. Lastly, we show that our algorithm outperforms exhaustive search in generating optimal coalition structures in terms of elapsed time and number of coalition structures generated

A general family of preferential belief removal operators

Most belief change operators in the AGM tradition assume an underlying plausibility ordering over the possible worlds which is transitive and complete. A unifying structure for these operators, based on supplementing the plausibility ordering with a second, guiding, relation over the worlds was presented in Booth et al. (Artif Intell 174:1339–1368, 2010). However it is not always reasonable to assume completeness of the underlying ordering. In this paper we generalise the structure of Booth et al. (Artif Intell 174:1339–1368, 2010) to allow incomparabilities between worlds. We axiomatise the resulting class of belief removal functions, and show that it includes an important family of removal functions based on finite prioritised belief bases

A distributed branch-and-bound algorithm for computing optimal coalition structures

Coalition formation is an important area of research in multi-agent systems. Computing optimal coalition structures for a large number of agents is an important problem in coalition formation but has received little attention in the literature. Previous studies assume that each coalition value is known a priori. This assumption is impractical in real world settings. Furthermore, the problem of finding coalition values become intractable for even a relatively small number of agents. This work proposes a distributed branch-and-bound algorithm for computing optimal coalition structures in linear production domain, where each coalition value is not known a priori. The common goal of the agents is to maximize the system’s profit. In our algorithm, agents perform two tasks: i) deliberate profitable coalitions, and ii) cooperatively compute optimal coalition structures. We show that our algorithm outperforms exhaustive search in generating optimal coalition structure in terms of elapses time and number of coalition structures generated