The VCG mechanism is the gold standard for combinatorial auctions (CAs), and it maximizes social welfare. In contrast, the revenue-maximizing (aka optimal) CA is unknown, and designing one is NP-hard. Therefore, research on optimal CAs has progressed into special settings. Notably, Levin [1997] derived the optimal CA for complements when each agent's private type is one-dimensional. We introduce a new research avenue for increasing revenue where we poke holes in the allocation space — based on the bids — and then use a welfare-maximizing allocation rule within the remaining allocation set. In this paper, the first step down this avenue, we introduce a new form of "reserve pricing" into CAs. We show that Levin's optimal revenue can be 2-approximated by using "monopoly reserve prices" to curtail the allocation set, followed by welfare-maximizing allocation and Levin's payment rule. A key lemma of potential independent interest is that the expected revenue from any truthful allocation-monotonic mechanism equals the expected virtual valuation; this generalizes Myerson's lemma [1981] from the single-parameter environment. Our mechanism is close to the gold standard and thus easier to adopt than Levin's. It also requires less information about the prior over the bidders' types, and is always more efficient. Finally, we show that the optimal revenue can be 6-approximated even if the "reserve pricing" is required to be symmetric across bidders.

In this context, while it methods (see, e.g., [Shehory and Kraus, 1998; Sandholm et is desirable to generate a coalition structure that al., 1999; Sen and Dutta, 2000; Dang and Jennings, 2004; maximizes the sum of the values of the coalitions, Rahwan et al., 2009b]). In this context, an important line of the space of possible solutions is often too large research is the development of anytime CSG algorithms. In to allow exhaustive search. Thus, a fundamental particular, an algorithm is "anytime" if it can return a solution open question in this area is the following: Can we at any point of time during its execution, and the quality of its search through only a subset of coalition structures, solution improves monotonically until termination. This is and be guaranteed to find a solution that is within particularly desirable in the multi-agent system context since a desirable bound β from optimum? If so, what is the agents might not always have sufficient time to run the the minimum such subset?

The design of multiagent simulations devoted to complex systems, addresses the issue of modeling behaviors that are involved at different space, time, behavior scales, each one being relevant so as to represent a feature of the phenomenon. We propose here a generic formalism intended to represent multiple environments, endowed with their own spatiotemporal scales and with behavioral rules for the agents they contain. An environment can be nested inside any agent, which itself is situated in one or more environments. This leads to a lattice decomposition of the global system, which appears to be necessary for an accurate design of multi-scale systems. This uniform representation of entities and behaviors at each abstraction level relies upon an interaction-oriented approach for the design of agent simulations, which clearly separates agents from interactions, from the modeling to the code. We also explain the implementation of our formalism within an existing interaction-based platform.

Decentralized partially observable Markov decision processes (DEC-POMDPs) are used to plan policies for multiple agents that must maximize a joint reward function but do not communicate with each other. The agents act under uncertainty about each other and the environment. This planning task arises in optimization of wireless networks, and other scenarios where communication between agents is restricted by costs or physical limits. DEC-POMDPs are a promising solution, but optimizing policies quickly becomes computationally intractable when problem size grows. Factored DEC-POMDPs allow large problems to be described in compact form, but have the same worst case complexity as non-factored DEC-POMDPs. We propose an efficient optimization algorithm for large factored infinite-horizon DEC-POMDPs. We formulate expectation-maximization based optimization into a new form, where complexity can be kept tractable by factored approximations. Our method performs well, and it can solve problems with more agents and larger state spaces than state of the art DEC-POMDP methods. We give results for factored infinite-horizon DEC-POMDP problems with up to 10 agents.

In agent-oriented programming and planning, agents' actions are typically specified in terms of postconditions, and the model of execution assumes that the environment carries the actions out exactly as specified. That is, it is assumed that the state of the environment after an action has been executed will satisfy its postcondition. In reality, however, such environments are rare: the actual execution of an action may fail, and the envisaged outcome is not met. We provide a conceptual framework for reasoning about success and failure of agents' behaviours. In particular, we propose a measure that reflects how "good" an environment is with respect to agent's capabilities and a given goal it might pursue. We also discuss which types of goals are worth pursuing, depending on the type of environment the agent is acting in.

Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based on Case-Based Reasoning) that solves new problems using previous experiences. This approach requires: to be able to gather and evaluate experiences; and to be described in such a way that similar social situations require similar regulations. As a scenario to evaluate our proposal, we use a simplified version of a traffic scenario, where agents are traveling cars. Our goals are to avoid collisions between cars and to avoid heavy traffic. These situations, when happen, lead to the synthesis of new regulations. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity. Overtime the system generates a set of regulations that, if followed, improve system performance (i.e. goal achievement).

Cooperation among automated agents is becoming increasingly important in various artificial intelligence applications. Coalitional (i.e., cooperative) game theory supplies conceptual and mathematical tools useful in the analysis of such interactions, and in particular in the achievement of stable outcomes among self-interested agents. Here, we study the minimal external subsidy required to stabilize the core of a coalitional game. Following the Cost of Stability (CoS) model introduced by Bachrach et al. [2009a], we give tight bounds on the required subsidy under various restrictions on the social structure of the game. We then compare the extended core induced by subsidies with the least core of the game, proving tight bounds on the ratio between the minimal subsidy and the minimal demand relaxation that each lead to stability.

Algorithms for finding game-theoretic solutions are now used in several real-world security applications. This work has generally assumed a Stackelberg model where the defender commits to a mixed strategy first. In general two-player normal-form games, Stackelberg strategies are easier to compute than Nash equilibria, though it has recently been shown that in many security games, Stackelberg strategies are also Nash strategies for the defender. However, the work on security games so far assumes that the attacker attacks only a single target. In this paper, we generalize to the case where the attacker attacks multiple targets simultaneously. Here, Stackelberg and Nash strategies for the defender can be truly different. We provide a polynomial-time algorithm for finding a Nash equilibrium. The algorithm gradually increases the number of defender resources and maintains an equilibrium throughout this process. Moreover, we prove that Nash equilibria in security games with multiple attackers satisfy the interchange property, which resolves the problem of equilibrium selection in such games. On the other hand, we show that Stackelberg strategies are actually NP-hard to compute in this context. Finally, we provide experimental results.

Car pollution is one of the major causes of green-house emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Existing efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that min- imize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important fea- ture when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.

A systematic study on the abstract level is the first step towards algorithms that solve the problem. We show that different semantics of ability in ATL Ultimately, we show that what agents can achieve is more give rise to different validity sets. As a consequence, sensitive to the strategic model of an agent (and a precise notion different notions of ability induce different of achievement) than it was generally realized. No less strategic logics and different general properties importantly, our study reveals that some natural properties - of games. Moreover, the study can be seen as the usually taken for granted when reasoning about action - may first systematic step towards satisfiability-checking cease to be universally true if we change the strategic setting.