Belief revision games (BRGs) are concerned with the dynamics of the beliefs of a group of communicating agents. BRGs are "zero-player" games where at each step every agent revises her own beliefs by taking account for the beliefs of her acquaintances. Each agent is associated with a belief state defined on some finite propositional language. We provide a general definition for such games where each agent has her own revision policy, and show that the belief sequences of agents can always be finitely characterized. We then define a set of revision policies based on belief merging operators. We point out a set of appealing properties for BRGs and investigate the extent to which these properties are satisfied by the merging-based policies under consideration.

When two or more self-interested agents put their plans to execution in the same environment, conflicts may arise as a consequence, for instance, of a common utilization of resources. In this case, an agent can postpone the execution of a particular action, if this punctually solves the conflict, or it can resort to execute a different plan if the agent's payoff significantly diminishes due to the action deferral. In this paper, we present a game-theoretic approach to non-cooperative planning that helps predict before execution what plan schedules agents will adopt so that the set of strategies of all agents constitute a Nash equilibrium. We perform some experiments and discuss the solutions obtained with our game-theoretical approach, analyzing how the conflicts between the plans determine the strategic behavior of the agents.

Argumentative discussion is a highly demanding task. In order to help people in such situations, this paper provides an innovative methodology for developing an agent that can support people in argumentative discussions by proposing possible arguments to them. By analyzing more than 130 human discussions and 140 questionnaires, answered by people, we show that the well-established Argumentation Theory is not a good predictor of people's choice of arguments. Then, we present a model that has 76% accuracy when predicting people’s top three argument choices given a partial deliberation. We present the Predictive and Relevance based Heuristic agent (PRH), which uses this model with a heuristic that estimates the relevance of possible arguments to the last argument given in order to propose possible arguments. Through extensive human studies with over 200 human subjects, we show that people’s satisfaction from the PRH agent is significantly higher than from other agents that propose arguments based on Argumentation Theory, predict arguments without the heuristics or only the heuristics. People also use the PRH agent's proposed arguments significantly more often than those proposed by the other agents.

This paper represents a paradigm shift in what advice agents should provide people. Contrary to what was previously thought, we empirically show that agents that dispense optimal advice will not necessary facilitate the best improvement in people's strategies. Instead, we claim that agents should at times suboptimally advise. We provide results demonstrating the effectiveness of a suboptimal advising approach in extensive experiments in two canonical mixed agent-human advice-giving domains. Our proposed guideline for suboptimal advising is to rely on the level of intuitiveness of the optimal advice as a measure for how much the suboptimal advice presented to the user should drift from the optimal value.

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i.e., workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker's reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

Recent studies have suggested that current agent-based models are not sufficiently sophisticated to reproduce results achieved by human collaborative learning and reasoning. Such studies suggest that humans are diverse and dynamic when solving problems socially. However, despite their relevance to problem-solving, these two behavioral features have not yet been fully investigated. In this paper we analyse a recent social problem-solving model and attempt to address its shortcomings. Specifically, we investigate the effects of separating exploitation from exploration in agent behaviors and explore the concept of diversity in such models. We found out that diverse populations outperform homogeneous ones in both efficient and inefficient networks. Finally, we show that agent diversity is more relevant than the strategic behavioral dynamics. This work contributes towards understanding the role of diverse and dynamic behaviors in social problem-solving as well as the advancement of state-of-art social problem-solving models.

Motivated by current day crowdsourcing platforms and emergence of online labor markets, this work addresses the problem of task allocation and payment decisions when unreliable and strategic workers arrive over time to work on tasks which must be completed within a deadline. We consider the following scenario: a requester has a set of tasks that must be completed before a deadline; agents (aka crowd workers) arrive over time and it is required to make sequential decisions regarding task allocation and pricing. Agents may have different costs for providing service and these costs are private information of the agents. We assume that agents are not strategic about their arrival times but could be strategic about their costs of service. In addition, agents could be unreliable in the sense of not being able to complete the assigned tasks within the allocated time; these tasks must then be reallocated to other agents to ensure ontime completion of the set of tasks by the deadline. For this setting, we propose two mechanisms: a DPM (DynamicPrice Mechanism) and an ABM (Auction Based Mechanism). Both mechanisms are dominant strategy incentive compatible, budget feasible, and also satisfy ex-post individual rationality for agents who complete the allocated tasks. These mechanisms can be implemented in current day crowdsourcing platforms with minimal changes to the current interaction model.

This paper explores the use of answer set programming (ASP) in solving distributed constraint optimization problems (DCOPs). It makes the following contributions: (i)~It shows how one can formulate DCOPs as logic programs; (ii)~It introduces ASP-DPOP, the first DCOP algorithm that is based on logic programming; (iii)~It experimentally shows that ASP-DPOP can be up to two orders of magnitude faster than DPOP (its imperative-programming counterpart) as well as solve some problems that DPOP fails to solve due to memory limitations; and (iv)~It demonstrates the applicability of ASP in the wide array of multi-agent problems currently modeled as DCOPs.

We propose a novel representation for coalitional games with externalities, called Partition Decision Trees. This representation is based on rooted directed trees, where non-leaf nodes are labelled with agents' names, leaf nodes are labelled with payoff vectors, and edges indicate membership of agents in coalitions. We show that this representation is fully expressive, and for certain classes of games significantly more concise than an extensive representation. Most importantly, Partition Decision Trees are the first formalism in the literature under which most of the direct extensions of the Shapley value to games with externalities can be computed in polynomial time.

In this paper, we consider the facility location problem un- der a novel model recently proposed in the literature, which combines the no-money constraint (i.e. the impossibility to employ monetary transfers between the mechanism and the agents) with the presence of heterogeneous facilities, i.e. facilities serving different purposes. Agents thus have a significantly different cost model w.r.t. the classical model with homogeneous facilities studied in literature. We initiate the study of non-utilitarian optimization functions under this novel model. In particular, we consider the case where the optimization goal consists of minimizing the maximum connection cost of the agents. In this setting, we investigate both deterministic and randomized algorithms and derive both lower and upper bounds regarding the approximability of strate- gyproof mechanisms.