An Algorithm for Distributing Coalitional Value Calculations among Cooperating Agents

The process of forming coalitions of software agents generally requires calculating a value for every possible coalition which indicates how beneficial that coalition would be if it was formed. Now, instead of having a single agent calculate all these values (as is typically the case), it is more efficient to distribute this calculation among the agents, thus using all the computational resources available to the system and avoiding the existence of a single point of failure. Given this, we present a novel algorithm for distributing this calculation among agents in cooperative environments. Specifically, by using our algorithm, each agent is assigned some part of the calculation such that the agents’ shares are exhaustive and disjoint. Moreover, the algorithm is decentralized, requires no communication between the agents, has minimal memory requirements, and can reflect variations in the computational speeds of the agents. To evaluate the effectiveness of our algorithm, we compare it with the only other algorithm available in the literature for distributing the coalitional value calculations (due to Shehory and Kraus). This shows that for the case of 25 agents, the distribution process of our algorithm took less than 0.02% of the time, the values were calculated using 0.000006% of the memory, the calculation redundancy was reduced from 383229848 to 0, and the total number of bytes sent between the agents dropped from 1146989648 to 0 (note that for larger numbers of agents, these improvements become exponentially better)

The Computational Difficulty of Bribery in Qualitative Coalitional Games

Qualitative coalitional games (QCG) are representations of coalitional games in which self interested agents, each with their own individual goals, group together in order to achieve a set of goals which satisfy all the agents within that group. In such a representation, it is the strategy of the agents to find the best coalition to join. Previous work into QCGs has investigated the computational complexity of determining which is the best coalition to join. We plan to expand on this work by investigating the computational complexity of computing agent power in QCGs as well as by showing that insincere strategies, particularly bribery, are possible when the envy-freeness assumption is removed but that it is computationally difficult to identify the best agents to bribe.Bribery, Coalition Formation, Computational Complexity

Insinking: A Methodology to Exploit Synergy in Transportation

vehicle routing;cooperative games;retailing;insinking;Shapley Monotonic Path;Logistic Service Providers

Enhancing cooperation in wireless networks using different concepts of game theory

PhDOptimizing radio resource within a network and across cooperating heterogeneous networks is the focus of this thesis. Cooperation in a multi-network environment is tackled by investigating network selection mechanisms. These play an important role in ensuring quality of service for users in a multi-network environment. Churning of mobile users from one service provider to another is already common when people change contracts and in a heterogeneous communication environment, where mobile users have freedom to choose the best wireless service-real time selection is expected to become common feature. This real time selection impacts both the technical and the economic aspects of wireless network operations. Next generation wireless networks will enable a dynamic environment whereby the nodes of the same or even different network operator can interact and cooperate to improve their performance. Cooperation has emerged as a novel communication paradigm that can yield tremendous performance gains from the physical layer all the way up to the application layer. Game theory and in particular coalitional game theory is a highly suited mathematical tool for modelling cooperation between wireless networks and is investigated in this thesis. In this thesis, the churning behaviour of wireless service users is modelled by using evolutionary game theory in the context of WLAN access points and WiMAX networks. This approach illustrates how to improve the user perceived QoS in heterogeneous networks using a two-layered optimization. The top layer views the problem of prediction of the network that would be chosen by a user where the criteria are offered bit rate, price, mobility support and reputation. At the second level, conditional on the strategies chosen by the users, the network provider hypothetically, reconfigures the network, subject to the network constraints of bandwidth and acceptable SNR and optimizes the network coverage to support users who would otherwise not be serviced adequately. This forms an iterative cycle until a solution that optimizes the user satisfaction subject to the adjustments that the network provider can make to mitigate the binding constraints, is found and applied to the real network. The evolutionary equilibrium, which is used to 3 compute the average number of users choosing each wireless service, is taken as the solution. This thesis also proposes a fair and practical cooperation framework in which the base stations belonging to the same network provider cooperate, to serve each other‘s customers. How this cooperation can potentially increase their aggregate payoffs through efficient utilization of resources is shown for the case of dynamic frequency allocation. This cooperation framework needs to intelligently determine the cooperating partner and provide a rational basis for sharing aggregate payoff between the cooperative partners for the stability of the coalition. The optimum cooperation strategy, which involves the allocations of the channels to mobile customers, can be obtained as solutions of linear programming optimizations

Heuristic methods for coalition structure generation

The Coalition Structure Generation (CSG) problem requires finding an optimal partition of a set of n agents. An optimal partition means one that maximizes global welfare. Computing an optimal coalition structure is computationally hard especially when there are externalities, i.e., when the worth of a coalition is dependent on the organisation of agents outside the coalition. A number of algorithms were previously proposed to solve the CSG problem but most of these methods were designed for systems without externalities. Very little attention has been paid to finding optimal coalition structures in the presence of externalities, although externalities are a key feature of many real world multiagent systems. Moreover, the existing methods, being non-heuristic, have exponential time complexity which means that they are infeasible for any but systems comprised of a small number of agents. The aim of this research is to develop effective heuristic methods for finding optimal coalition structures in systems with externalities, where time taken to find a solution is more important than the quality of the solution. To this end, four different heuristics methods namely tabu search, simulated annealing, ant colony search and particle swarm optimisation are explored. In particular, neighbourhood operators were devised for the effective exploration of the search space and a compact representation method was formulated for storing details about the multiagent system. Using these, the heuristic methods were devised and their performance was evaluated extensively for a wide range of input data

Insinking:A Methodology to Exploit Synergy in Transportation

Over the last decades, companies ’ average profit margins have been decreasing and as a result efficiency of transportation processes has become critical. To cut down transportation costs, shippers often outsource their transportation activities to a Logistics Service Provider of their choice. This paper proposes a procedure that puts the initiative with the service provider instead. This procedure is based on both operations research and game theoretical insights. To stress the contrast between the traditional push approach of outsourcing, and the here proposed pull approach where the service provider is the initiator of the shift of logistics activities from the shipper to the Logistics Service Provider, we will refer to this phenomenon as insinking, the antonym of outsourcing. Insinking has the advantage that the logistics service provider can proactively select a group of shippers with a strong synergy potential. Moreover, these synergies can be allocated to the participating shippers in a fair and sustainable way by means of customized tariffs. Insinking is illustrated by means of a case study in the Dutch grocery transportation sector

Dynamic Formation and Strategic Management of Web Services Communities

In the last few years, communities of services have been studied in a certain numbers of proposals as virtual pockets of similar expertise. The motivation is to provide these services with high chance of discovery through better visibility, and to enhance their capabilities when it comes to provide requested functionalities. There are some proposed mechanisms and models on aggregating web services and making them cooperate within their communities. However, forming optimal and stable communities as coalitions to maximize individual and group efficiency and income for all the involved parties has not been addressed yet. Moreover, in the proposed frameworks of these communities, a common assumption is that residing services, which are supposed to be autonomous and intelligent, are competing over received requests. However, those services can also exhibit cooperative behaviors, for instance in terms of substituting each other. When competitive and cooperative behaviors and strategies are combined, autonomous services are said to be "coopetitive". Deciding to compete or cooperate inside communities is a problem yet to be investigated. In this thesis, we first identify the problem of defining efficient algorithms for coalition formation mechanisms. We study the community formation problem in two different settings: 1) communities with centralized manager having complete information using cooperative game-theoretic techniques; and 2) communities with distributed decision making mechanisms having incomplete information using training methods. We propose mechanisms for community membership requests and selections of web services in the scenarios where there is interaction between one community and many web services and scenarios where web services can join multiple established communities. Then in order to address the coopetitive relation within communities of web services, we propose a decision making mechanism for our web services to efficiently choose competition or cooperation strategies to maximize their payoffs. We prove that the proposed decision mechanism is efficient and can be implemented in time linear in the length of the time period considered for the analysis and the number of services in the community. Moreover, we conduct extensive simulations, analyze various scenarios, and confirm the obtained theoretical results using parameters from a real web services dataset

Decentralised Coalition Formation Methods for Multi-Agent Systems

Coalition formation is a process whereby agents recognise that cooperation with others can occur in a mutually beneficial manner and therefore the agents can choose appropriate temporary groups (named coalitions) to form. The benefit of each coalition can be measured by: the goals it achieves; the tasks it completes; or the utility it gains. Determining the set of coalitions that should form is difficult even in centralised cooperative circumstances due to: (a) the exponential number of different possible coalitions; (b) the ``super exponential'' number of possible sets of coalitions; and (c) the many ways in which the agents of a coalition can agree to distribute its gains between its members (if this gain can be transferred between the agents). The inherent distributed and potentially self-interested nature of multi-agent systems further complicates the coalition formation process. How to design decentralised coalition formation methods for multi-agent systems is a significant challenge and is the topic of this thesis. The desirable characteristics for these methods to have are (among others): (i) a balanced computational load between the agents; (ii) an optimal solution found with distributed knowledge; (iii) bounded communication costs; and (iv) to allow coalitions to form even when the agents disagree on their values. The coalition formation methods presented in this thesis implement one or more of these desirable characteristics. The contribution of this thesis begins with a decentralised dialogue game that utilise argumentation to allow agents to reason over and come to a conclusion on what are the best coalitions to form, when the coalitions are valued qualitatively. Next, the thesis details two decentralised algorithms that allow the agents to complete the coalition formation process in a specific coalition formation model, named characteristic function games. The first algorithm allows the coalition value calculations to be distributed between the agents of the system in an approximately equal manner using no communication, where each agent assigned to calculate the value of a coalition is included in that coalition as a member. The second algorithm allows the agents to find one of the most stable coalition formation solutions, even though each agent has only partial knowledge of the system. The final contribution of this thesis is a new coalition formation model, which allows the agents to find the expected payoff maximising coalitions to form, when each agent may disagree on the quantitative value of each coalition. This new model introduces more risk to agents valuing a coalition higher than the other agents, and so encourages pessimistic valuations