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 NPcomplete; in particular, it is hard for a defined class of valuation functions which are independent of disconnected membersthat is, two nodes have no effect on each others marginal con- tribution to their vertex separator. Nonetheless, for all such functions we provide bounds on the complexity of coalition structure generation over general and minorfree 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 NPcomplete for planar graphs, and hence, for any K_k minorfree 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(m^2) 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.

Multiagent systems (MAS) are widely accepted as an important method for solving problems of a distributed nature. A key to the success of MAS is efficient and effective multiagent learning (MAL). The past twenty-five years have seen a great interest and tremendous progress in the field of MAL. This article introduces and overviews this field by presenting its fundamentals, sketching its historical development and describing some key algorithms for MAL. Moreover, main challenges that the field is facing today are indentified.

This article gives an introduction to agent-based modeling and simulation (ABMS). After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given.

In this paper, we study the problem of controlling a group of indistinguishable agents with non-negligible sizes to take arbitrary desired formations. The agents, confined to an arbitrary connected graph, are capable of moving from one vertex to an adjacent vertex in one time step. The control policy must ensure that no collisions occur, which may happen when two agents attempt to move to the same vertex or move along the same edge. Counting each edge as having unit distance, we show that a (centralized) policy/schedule exists that moves the agents to the desired formation along paths having shortest total distance. The control policy also guarantees that a convergence time (the time when the formation is complete) of no more than n l 1, in which n is the number of agents,lis the maximum (shortest) distance between any two initial and goal vertices.

Barring swarm robotics, a substantial share of current machine-human and machine-machine learning and interaction mechanisms are being developed and fed by results of agent-based computer simulations, game-theoretic models, or robotic experiments based on a dyadic communication pattern. Yet, in real life, humans no less frequently communicate in groups, and gain knowledge and take decisions basing on information cumulatively gleaned from more than one single source. These properties should be taken into consideration in the design of autonomous artificial cognitive systems construed to interact with learn from more than one contact or 'neighbour'. To this end, significant practical import can be gleaned from research applying strict science methodology to human and social phenomena, e.g. to discovery of realistic creativity potential spans, or the 'exposure thresholds' after which new information could be accepted by a cognitive agent. The results will be presented of a project analysing the social propagation of neologisms in a microblogging service. From local, low-level interactions and information flows between agents inventing and imitating discrete lexemes we aim to describe the processes of the emergence of more global systemic order and dynamics, using the latest methods of complexity science. Whether in order to mimic them, or to 'enhance' them, parameters gleaned from complexity science approaches to humans' social and humanistic behaviour should subsequently be incorporated as points of reference in the field of robotics and human-machine interaction.

For the problem of moving a set of agents on a connected graphto agent-specific goal locations, free of collisions, we propose a multiflow based integer linear programming (ILP) model that finds a time optimal solution. The resulting algorithm from our ILP model is complete and guarantees to yield true optimal solutions. Focusing on the time optimal formulation, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. The computational results confirm the effectiveness of our method.

The current state-of-the-art algorithm for optimal coalition structure generation is IDP-IP — an algorithm that combines IDP (a dynamic programming algorithm due to Rahwan and Jennings, AAAI'08) with IP (a tree-search algorithm due to Rahwan et al., JAIR'09). In this paper we analyse IDP-IP, highlight its limitations, and then develop a new approach for combining IDP with IP that overcomes these limitations.

We consider the problem of selecting fair divisions of a heterogeneous divisible good among a set of agents. Recent work (Cohler et al., AAAI 2011) focused on designing algorithms for computing maxsum—social welfare maximizing—allocations under the fairness notion of envy-freeness. Maxsum allocations can also be found under alternative notions such as equitability. In this paper, we examine the properties of these allocations. In particular, We provide conditions for when maxsum envy-free or equitable allocations are Pareto optimal and give examples where fairness with Pareto optimality is not possible. We also prove that maxsum envy-free allocations have weakly greater welfare than maxsum equitable allocations when agents have structured valuations, and we derive an approximate version of this inequality for general valuations.

Market or auction based algorithms offer effective methods for de-centralized task assignment in multi-agent teams. Typically there is an implicit assumption that agents are willing to cooperate and can be trusted to perform assigned tasks. Reciprocal collaboration may not always be a valid assumption. In cases where auctions are used for task allocation, without explicit revenue exchange, incentives are needed to enforce cooperation. An approach to incentive based trust is presented, which enables detection of team members that are not contributing and for dynamic formation of teams.

The importance of diversity in reasoning and learning to successfully address complex problems is examined. We discuss an approach by which a multiagent framework with decentralized control mechanisms provides diverse perspectives and hypotheses addressing a class of complex problems. We introduce the SNARE multiagent system. SNARE performs tasks to gain situational awareness of situations of interest in a Social Media Space. It applies a decentralized control mechanism for each agent; this mechanism enables an agent to interact with other agents to reason and learn. This approach facilitates dynamic agent organizations that adapt the topologies of interactions between agents based on the problem context.