We study the existence and computational complexity of coalitional stability concepts based on social networks. Our concepts represent a natural and rich combinatorial generalization of a recent notion termed partition equilibrium. We assume that players in a strategic game are embedded in a social (or, communication) network, and there are coordination constraints defining the set of coalitions that can jointly deviate in the game. A main feature of our approach is that players act in a "considerate" fashion to ignore potentially profitable (group) deviations if the change in their strategy may cause a decrease of utility to their neighbors in the network. We explore the properties of such considerate equilibria in application to the celebrated class of resource selection games (RSGs). Our main result proves existence of a super-strong considerate equilibrium in all symmetric RSGs with strictly increasing delays, for any social network among the players and feasible coalitions represented by the set of cliques. The existence proof is constructive and yields an efficient algorithm. In fact, the computed considerate equilibrium is a Nash equilibrium for a standard RSG, thus showing that there exists a state that is stable against selfish and considerate behavior simultaneously. Furthermore, we provide results on convergence of considerate dynamics.

We propose a logical framework to represent and reason about agent interactions in normative systems. Our starting point is a dynamic logic of propositional assignments whose satisfiability problem is PSPACE-complete. We show that it embeds Coalition Logic of Propositional Control CL-PC and that various notions of ability and capability can be captured in it. We illustrate it on a water resource management case study. Finally, we show how the logic can be easily extended in order to represent constitutive rules which are also an essential component of the modelling of social reality.

The computational complexity of relevant corerelatedquestions for coalitional games is addressed from the coalition structure viewpoint, i.e., withoutassuming that the grand-coalition necessarily forms. In the analysis, games are assumed to be in "compact" form, i.e., their worth functions are implicitly given as polynomial-time computable functions over succinct game encodings provided as input. Within this setting, a complete picture of the complexity issues arising with the core, as well as with the related stability concepts of least core and cost of stability, is depicted. In particular, the special cases of superadditive games and of games whose sets of feasible coalitions are restricted over tree-like interaction graphs are also studied.

We address the issue of manipulating games through communication. In the specific setting we consider (a variation of Boolean games), we assume there is some set of environment variables, the value of which is not directly accessible to players; each player has their own beliefs about these variables, and makes decisions about what actions to perform based on these beliefs. The communication we consider takes the form of (truthful) announcements about the value of some environment variables; the effect of an announcement about some variable is to modify the beliefs of the players who hear the announcement so that they accurately reflect the value of the announced variables. By choosing announcements appropriately, it is possible to perturb the game away from certain rational outcomes and towards others. We specifically focus on the issue of stabilisation: making announcements that transform a game from having no stable states to one that has stable configurations.

Binary aggregation studies problems in which individuals express yes/no choices over a number of possibly correlated issues, and these individual choices need to be aggregated into a collective choice. We show how several classical frameworks of Social Choice Theory, particularly preference and judgment aggregation, can be viewed as binary aggregation problems by designing an appropriate set of integrity constraints for each specific setting. We explore the generality of this framework, showing that it makes available useful techniques both to prove theoretical results, such as a new impossibility theorem in preference aggregation, and to analyse practical problems, such as the characterisation of safe agendas in judgment aggregation in a syntactic way. The framework also allows us to formulate a general definition of paradox that is independent of the domain under consideration, which gives rise to the study of the class of aggregation procedures of generalised dictatorships.

We propose a formal model for argumentationbased dialogues between agents, using assumptionbased argumentation (ABA). The model is given in terms of ABA-specific utterances, trees drawn from dialogues and legal-move and outcome functions. We prove a formal connection between these dialogues and argumentation semantics. We illustrate persuasion as an application of the dialogue model.

In elections, an alternative is said to be a Condorcet winner if it is preferred to any other alternative by a majority of voters. While this is a very attractive solution concept, many elections do not have a Condorcet winner. In this paper, we propose a setvalued relaxation of this concept, which we call a Condorcet winning set: such sets consist of alternatives that collectively dominate any other alternative. We also consider a more general version of this concept, where instead of domination by a majority of voters we require domination by a given fraction theta of voters; we refer to this concept as theta-winning set. We explore social choice-theoretic and algorithmic aspects of these solution concepts, both theoretically and empirically.

We report on results from experiments where human traders interact with software-agent traders in a real-time asynchronous continuous double auction (CDA) experimental economics system. Our experiments are inspired by the seminal work reported by IBM at IJCAI 2001, where it was demonstrated that software-agent traders could consistently outperform human traders in real-time CDA markets. IBM tested two trading-agent strategies, ZIP and a modified version of GD, and in a subsequent paper they reported on a new strategy called GDX that was demonstrated to outperform GD and ZIP in agent vs. agent CDA competitions, on which basis it was claimed that GDX "...may offer the best performance of any published CDA bidding strategy.". In this paper, we employ experiment methods similar to those pioneered by IBM to test the performance of "Adaptive Aggressive" (AA) algorithmic traders. The results presented here confirm Vytelingum's claim that AA outperforms ZIP, GD, and GDX in agent vs. agent experiments. We then present the first results from testing AA against human traders in human vs. agent CDA experiments, and demonstrate that AA's performance against human traders is superior to that of ZIP, GD, and GDX. We therefore claim that, on the basis of the available evidence, AA may offer the best performance of any published bidding strategy.

We consider scenarios where several agents must aggregate their preferences over a large set of candidates with a combinatorial structure. That is, each candidate is an element of the Cartesian product of the domains of some variables. We assume agents compactly express their preferences over the candidates via soft constraints. We consider a sequential procedure that chooses one candidate by asking the agents to vote on one variable at a time. While some properties of this procedure have been already studied, here we focus on independence of irrelevant alternatives, non-dictatorship, and strategy-proofness. Also, we perform an experimental study that shows that the proposed sequential procedure yields a considerable saving in time with respect to a non-sequential approach, while the winners satisfy the agents just as well, independently of the variable ordering and of the presence of coalitions of agents.

We address the issue, in cognitive agents, of possible loss of previous information, which later might turn out to be correct when new information becomes available. To this aim, we propose a framework for changing the agent's mind without erasing forever previous information, thus allowing its recovery in case the change turns out to be wrong. In this new framework, a piece of information is represented as an argument which can be more or less accepted depending on the trustworthiness of the agent who proposes it. We adopt possibility theory to represent uncertainty about the information, and to model the fact that information sources can be only partially trusted. The originality of the proposed framework lies in the following two points: (i) argument reinstatement is mirrored in belief reinstatement in order to avoid the loss of previous information; (ii) new incoming information is represented under the form of arguments and it is associated with a plausibility degree depending on the trustworthiness of the information source.