Anytime Coalition Structure Generation with Worst Case Guarantees

Coalition formation is a key topic in multiagent systems. One would prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show necessary for establishing a bound. We present an algorithm that establishes a tight bound within this minimal amount of search, and show that any other algorithm would have to search strictly more. The fraction of nodes needed to be searched approaches zero as the number of agents grows. If additional time remains, our anytime algorithm searches further, and establishes a progressively lower tight bound. Surprisingly, just searching one more node drops the bound in half. As desired, our algorithm lowers the bound rapidly early on, and exhibits diminishing returns to computation. It also drastically outperforms its obvious contenders. Finally, we show how to distribute the desired search across self-interested manipulative agents

Multi-agent Learning For Game-theoretical Problems

Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. We use game-theoretic evaluation criteria such as optimality, stability, and resulting equilibria

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

Trust-based Incentive Mechanisms for Community-based Multiagent Systems

In this thesis we study peer-based communities which are online communities whose services are provided by their participant agents. In order to improve the services an agent enjoys in these communities, we need to improve the services other agents offer. Towards this goal, we propose a novel solution which allows communities to share the experience of their members with other communities. The experience of a community with an agent is captured in the evaluation rating of the agent within the community, which can either represent the trustworthiness or the reputation of the agent. We argue that exchanging this information is the right way to improve the services the agent offers since it: i) exploits the information that each community accumulates to allow other communities to decide whether to accept the agent while it also puts pressure on the agent to behave well, since it is aware that any misbehaviour will be spread to the communities it might wish to join in the future, ii) can prevent the agent from overstretching itself among many communities, since this may lead the agent to provide very limited services to each of these communities due to its limited resources, and thus its trustworthiness and reputation might be compromised. We study mechanisms that can be used to facilitate the exchange of trust or reputation information between communities. We make two key contributions. First, we propose a graph-based model which allows a particular community to determine which other communities to ask information from. We leverage consistency of past information and provide an equilibrium analysis showing that communities are best-off when they truthfully report the requested information, and describe how payments should be made to support the equilibrium. Our second contribution is a promise-based trust model where agents are judged based on the contributions they promise and deliver to the community. We outline a set of desirable properties such a model must exhibit, provide an instantiation, and an empirical evaluation

Planung kooperativer Fahrmanöver für kognitive Automobile

Fahrerassistenzsysteme eröffnen die Möglichkeit für automatische Eingriffe in Gefahrensituationen und bieten dadurch ein Potenzial zur Unfallvermeidung und zur Minimierung der Unfallschwere im Straßenverkehr. Die Handlungen mehrerer kognitiver Fahrzeuge können über Funkkommunikation miteinander koordiniert werden. Diese Dissertation untersucht potenziell echtzeitfähige Bewegungsplanungsalgorithmen zur Planung von Fahrmanövern, die von mehreren Fahrzeugen kooperativ ausgeführt werden können

Agents in electronic commerce: component technologies for automated negotiation and coalition formation

Abstract. Automated negotiation and coalition formation among self-interested agents are playing an increasingly important role in electronic commerce. Such agents cannot be coordinated by externally imposing their strategies. Instead the interaction protocols have to be designed so that each agent is motivated to follow the strategy that the protocol designer wants it to follow. This paper reviews six component technologies that we have developed for making such interactions less manipulable and more efficient in terms of the computational processes and the outcomes: 1. OCSM-contracts in marginal cost based contracting, 2. leveled commitment contracts, 3. anytime coalition structure generation with worst case guarantees, 4. trading off computation cost against optimization quality within each coalition, 5. distributing search among insincere agents, and 6. unenforced contract execution. Each of these technologies represents a different way of battling self-interest and combinatorial complexity simultaneously. This is a key battle when multi-agent systems move into large-scale open settings