We consider the problem of repeatedly matching a set of alternatives to a set of agents in the absence of monetary transfer. We propose a generic framework for evaluating sequential matching mechanisms with dynamic preferences, and show that unlike single-shot settings, the random serial dictatorship mechanism is manipulable.

Flocking is a emergent behavior exhibited by many different animal species, including birds and fish. In our work we consider adding a small set of influencing agents, that are under our control, into a flock. Following ad hoc teamwork methodology, we assume that we are given knowledge of, but no direct control over, the rest of the flock. In our ongoing work highlighted in this abstract, we are specifically considering the problem of where to initially place influencing agents that we add to such a flock. We use these influencing agents to influence the flock to behave in a particular way - for example, to fly in a particular orientation or fly in a particular pattern such as to avoid an obstacle.

The NO paradigm can model many operations. Examples When agents dwell inside an organization, they form patterns are systems of river dam control, factory cells, electrical of interactions that we call paradigms. There are many power grids, and traffic control on land, sea, and space. As existing paradigms to describe organizations, which affect a paradigm, it does not functionally alter the operations to its performance features. These paradigms include hierarchies, which it is applied. The paradigm can be understood in terms holarchies, coalitions, teams, congregations, societies, of the ways it permits command and control regimes. Invariably, federations, markets and matrix organizations (Horling and NO relies on a network on which it dwells.

Most real-world games and many recreational games are games of incomplete information. Over the last dozen years, abstraction has emerged as a key enabler for solving large incomplete-information games. First, the game is abstracted to generate a smaller, abstract game that is strategically similar to the original game. Second, an approximate equilibrium is computed in the abstract game. Third, the strategy from the abstract game is mapped back to the original game. In this paper, I will review key developments in the field. I present reasons for abstracting games, and point out the issue of abstraction pathology. I then review the practical algorithms for information abstraction and action abstraction. I then cover recent theoretical breakthroughs that beget bounds on the quality of the strategy from the abstract game, when measured in the original game. I then discuss how to reverse map the opponent's action into the abstraction if the opponent makes a move that is not in the abstraction. Finally, I discuss other topics of current and future research.

Many models and mechanisms in resource and cost allocation have been developed that are simple and abstract. By means of two case studies, I argue that it is now timely to consider richer models for the fair division of resources and for the allocation of costs. Such models should have features like asynchronicity which reflect more of the true complexity of many fair division and cost allocation problems met in the real world. I suggest that computation can be used in such models to increase both efficiency and fairness of the allocations. As a result, we may be able to do more with fewer resources and greater fairness.

Machine learning and game theory are two important directions of AI. The former usually assumes data is independent of the models to be learned; the latter usually assumes agents are fully rational. In many modern Internet applications, like sponsored search and crowdsourcing, the two basic assumptions are violated and new challenges are posed to both machine learning and game theory. To better model and study such applications, we need to go beyond conventional machine learning and game theory (mechanism design), and adopt a new approach called mechanism learning with mechanism induced data. Specifically, we propose to learn a behavior model from data to describe how real agents play the complicated game, instead of making the full-rationality assumption. Then we propose to optimize the mechanism by using the learned behavior models to predict the future behaviors of agents in response to the new mechanism. Because the above process couples mechanism learning and behavior learning in a loop, new algorithms and theories are needed to perform the task and guarantee the asymptotical performance. As shown in this paper, there are many interesting research topics along this direction, many of which are still open problems, waiting for researchers in our community to deeply investigate.

We address the problem of planning collision-free paths for multiple agents using optimization methods known as proximal algorithms. Recently this approach was explored in Bento et al. (2013), which demonstrated its ease of parallelization and decentralization, the speed with which the algorithms generate good quality solutions, and its ability to incorporate different proximal operators, each ensuring that paths satisfy a desired property. Unfortunately, the operators derived only apply to paths in 2D and require that any intermediate waypoints we might want agents to follow be preassigned to specific agents, limiting their range of applicability. In this paper we resolve these limitations. We introduce new operators to deal with agents moving in arbitrary dimensions that are faster to compute than their 2D predecessors and we introduce landmarks, space-time positions that are automatically assigned to the set of agents under different optimality criteria. Finally, we report the performance of the new operators in several numerical experiments.

The utilitarian solution criterion, which has been extensively studied in multi-agent decision making under uncertainty, aims to maximize the sum of individual utilities. However, as the utilitarian solution often discriminates against some agents, it is not desirable for many practical applications where agents have their own interests and fairness is expected. To address this issue, this paper introduces egalitarian solution criteria for sequential decision-making under uncertainty, which are based on the maximin principle. Motivated by different application domains, we propose four maximin fairness criteria and develop corresponding algorithms for computing their optimal policies. Furthermore, we analyze the connections between these criteria and discuss and compare their characteristics.

A scoring rule is a device for eliciting and assessing probabilistic forecasts from an agent. When dealing with continuous outcome spaces, and absent any prior insights into the structure of the agent's beliefs, the rule should allow for a flexible reporting interface that can accurately represent complicated, multi-modal distributions. In this paper, we provide such a scoring rule based on a nonparametric approach of eliciting a set of samples from the agent and efficiently evaluating the score using kernel methods. We prove that sampled reports of increasing size converge rapidly to the true score, and that sampled reports are approximately optimal. We also demonstrate a connection between the scoring rule and the maximum mean discrepancy divergence. Experimental results are provided that confirm rapid convergence and that the expected score correlates well with standard notions of divergence, both important considerations for ensuring that agents are incentivized to report accurate information.

Following Fleming (1952), Harsanyi (1955) showed that if the collective preference satisfies von Neumann and Morgenstern's Prade's axioms (1995), and particularly risk aversion, The present paper raises the question of collective resorts on (i) the identification of a theory of decision decision making under possibilistic uncertainty. The making under uncertainty (DMU) that captures the decision next Section recalls the basic notions on which our work relies makers' behaviour with respect to uncertainty and (ii) the (decision under possibilistic uncertainty, collective utility specification of a collective utility function (CUF) as it may functions, etc.).