We introduce EHS*, a novel temporal-epistemic logic defined on temporal intervals characterised by regular expressions. We investigate the complexity of verifying multi-agent systems against EHS* specifications for a number of fragments of EHS* with results ranging from PSPACE-completeness to non-elementary time. The findings show that, at least for the fragments under analysis, the increase in expressiveness obtained by using regular expressions rather than end-points as standard, can be achieved without increasing the complexity of the problem. We show that the expressiveness of regular expressions can also be adopted at the level of specifications without severe computational cost. To do so we introduce a further temporal-epistemic logic, called EHSre, in which regular expressions are used within propositions, and give a polynomial time reduction of the model checking problem from EHSre to EHS*.

There has been a great deal of interest in AI recently in applying Nevertheless, the intuition that underlies sequential and ideas of game theory to model interacting agents who perfect equilibrium, namely, players should play optimally have possibly different preferences as to the outcome of the even off the equilibrium path, seems to make sense even interaction.

We study an epistemic logic where knowledge is built from what the agents observe (including higher-order visibility) and what the agents learn from public announcements. This fixes two main drawbacks of previous observability-based approaches where who sees what is common knowledge and where the epistemic operators distribute over disjunction. The latter forbids the modeling of most of the classical epistemic problems, starting with the muddy children puzzle. We integrate a dynamic dimension where both facts of the world and the agents’ observability can be modified by assignment programs. We establish that the model checking problem is PSPACE-complete.

In temporal logics, the operator F expresses that at some time in the future something happens, e.g., a request is eventually granted. Unfortunately, there is no bound on the time un- til the eventuality is satisfied which in many cases does not correspond to the intuitive meaning system designers have, namely, that F abstracts the idea that there is a bound on this time although its magnitude is not known. An elegant way to capture this meaning is through Prompt-LTL, which extends LTL with the operator F P ("prompt eventually"). We extend this work by studying alternating-time epistemic temporal logics extended with F P . We study the model-checking problem of the logic Prompt- KATL∗, which is ATL∗ extended with epistemic operators and prompt eventually. We also obtain results for the model-checking problem of some of its fragments. Namely, of Prompt-KATL (ATL with epistemic operators and prompt eventually), Prompt-KCTL∗ (CTL∗ with epistemic operators and prompt eventually), and finally the existential fragments of Prompt-KATL∗ and Prompt-KATL.

We review several different languages for collective decision making problems, in which agents express their judgments, opinions, or beliefs over elements of a logically structured domain. Several such languages have been proposed in the literature to compactly represent the questions on which the agents are asked to give their views. In particular, the framework of judgment aggregation allows agents to vote directly on complex, logically related formulas, whereas the setting of binary aggregation asks agents to vote on propositional variables, over which dependencies are expressed by means of an integrity constraint. We compare these two languages and some of their variants according to their relative succinctness and according to the computational complexity of aggregating several individual views expressed in such languages into a collective judgment. Our main finding is that the formula-based language of judgment aggregation is more succinct than the constraint-based language of binary aggregation. In many (but not all) practically relevant situations, this increase in succinctness does not entail an increase in complexity of the corresponding problem of computing the outcome of an aggregation rule.

We study hedonic games with dichotomous preferences. Hedonic games are cooperative games in which players desire to form coalitions, but only care about the makeup of the coalitions of which they are members; they are indifferent about the makeup of other coalitions. The assumption of dichotomous preferences means that, additionally, each player's preference relation partitions the set of coalitions of which that player is a member into just two equivalence classes: satisfactory and unsatisfactory. A player is indifferent between satisfactory coalitions, and is indifferent between unsatisfactory coalitions, but strictly prefers any satisfactory coalition over any unsatisfactory coalition. We develop a succinct representation for such games, in which each player's preference relation is represented by a propositional formula. We show how solution concepts for hedonic games with dichotomous preferences are characterised by propositional formulas.

Most research into Swarm Intelligence explores swarms of autonomous robots or simulated agents. Little work, however, has been done on swarms of networked humans. This paper introduces UNU, an online platform that enables networked users to assemble in real-time swarms and tackle problems as an Artificial Swarm Intelligence (ASI). Modeled after biological swarms, UNU enables large groups of networked users to work together in real-time synchrony, forging a unified dynamic system that can quickly answer questions and make decisions. Early testing suggests that human swarming has significant potential for harnessing the Collective Intelligence (CI) of online groups, often exceeding the natural abilities of individual participants.

Games that model real world interactions are often complex, with huge numbers of possible strategies and information states. We are interested in better understanding the effect of abstraction in game-theoretic analysis. In particular, we focus on the strategy selection problem: how should an agent choose a strategy to play in a game, based on an abstracted game model? This problem has three interacting Figure 1: 2-players asymmetric abstractions components: (1) the method for abstracting the game, (2) the method for selecting a strategy based on the abstraction, and An example of an abstraction meta-game is shown in Figure (3) the method for mapping this strategy back to the original 1. In this example, we have two players who are playing game. This approach has been studied extensively for the one-shot normal form game shown at the top of the poker, which is a 2-player, zero-sum game. However, much figure; this is the base game. They each perform their own less is known about how abstraction interacts with strategy (unspecified) abstraction to reduce the game.

People collaborate in carrying out such complex activities as treating patients, co-authoring documents and developing software. While technologies such as Dropbox and Github enable groups to work in a distributed manner, coordinating team members' individual activities poses significant challenges. In this paper, we formalize the problem of "information sharing in loosely-coupled extended-duration teamwork." We develop a new representation, Mutual Influence Potential Networks (MIP-Nets), to model collaboration patterns and dependencies among activities, and an algorithm, MIP-DOI, that uses this representation to reason about information sharing.

We survey some recent research regarding strategic behaviour in resource allocation problems, focusing on the fair division of indivisible goods. We consider a number of computational questions like how a single strategic agent misreports their preferences to ensure a particular outcome, and how agents compute a Nash equilibrium when they all act strategically. We also identify a number of future directions like dealing with non-additive utilities, and partial or probabilistic information about the preferences of other agents.