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

Coalition structure generation over graphs

We give the analysis of the computational complexity of coalition structure generation over graphs. Given an undirected graph G = (N,E) and a valuation function v : P(N) → R over the subsets of nodes, the problem is to find a partition of N into connected subsets, that maximises the sum of the components values. This problem is generally NP-complete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected members — that is, two nodes have no effect on each others marginal contribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minor free graphs. Our proof is constructive and yields algorithms for solving corresponding instances of the problem. Furthermore, we derive linear time bounds for graphs of bounded treewidth. However, as we show, the problem remains NP-complete for planar graphs, and hence, for any Kk minor free graphs where k ≥ 5. Moreover, a 3-SAT problem with m clauses can be represented by a coalition structure generation problem over a planar graph with O(m2) nodes. Importantly, our hardness result holds for a particular subclass of valuation functions, termed edge sum, where the value of each subset of nodes is simply determined by the sum of given weights of the edges in the induced subgraph

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

Insinking: A Methodology to Exploit Synergy in Transportation

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

Tasks for Agent-Based Negotiation Teams:Analysis, Review, and Challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.Comment: Engineering Applications of Artificial Intelligence, 201

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

One for all, all for one---von Neumann, Wald, Rawls, and Pareto

Applications of the maximin criterion extend beyond economics to statistics, computer science, politics, and operations research. However, the maximin criterion---be it von Neumann's, Wald's, or Rawls'---draws fierce criticism due to its extremely pessimistic stance. I propose a novel concept, dubbed the optimin criterion, which is based on (Pareto) optimizing the worst-case payoffs of tacit agreements. The optimin criterion generalizes and unifies results in various fields: It not only coincides with (i) Wald's statistical decision-making criterion when Nature is antagonistic, (ii) the core in cooperative games when the core is nonempty, though it exists even if the core is empty, but it also generalizes (iii) Nash equilibrium in $n$-person constant-sum games, (iv) stable matchings in matching models, and (v) competitive equilibrium in the Arrow-Debreu economy. Moreover, every Nash equilibrium satisfies the optimin criterion in an auxiliary game