We consider all possible games that have unique PNE payoffs. Our starting point is the classes of games that can be expressed by a conjunction class of two-person strictly competitive games. We first formulate of two binary clauses, and our program rediscovered the notions of games, strictly competitive games and Kats and Thisse's class of weakly unilaterally PNEs in first-order logic. Under our formulation, a class of competitive two-person games, and came games corresponds to a first-order sentence. In particular, the up with several other classes of games that have sentence that corresponds to the class of strictly competitive unique pure Nash equilibrium payoffs. It also came games is a conjunction of two binary clauses with all variables up with new classes of strict games that have unique universally quantified. So we implemented a program pure Nash equilibria, where a game is strict if for that examines all these universally quantified conjunctions of both player different profiles have different payoffs.

Effectively modeling an agent's cognitive model is an important problem in many domains. In this paper, we explore the agents people wrote to operate within optimization problems. We claim that the overwhelming majority of these agents used strategies based on bounded rationality, even when optimal solutions could have been implemented. Particularly, we believe that many elements from Aspiration Adaptation Theory (AAT) are useful in quantifying these strategies. To support these claims, we present extensive empirical results from over a hundred agents programmed to perform in optimization problems involving solving for one and two variables.

Recently, efficient approximation algorithms for finding Nash equilibria have been developed for the interesting class of anonymous games , where a player's utility does not depend on the identity of its opponents. In this paper, we tackle the problem of computing equilibria in such games with continuous player types , extending the framework to encompass settings with imperfect information. In particular, given the existence result for pure Bayes-Nash equilibiria in these games, we generalise the fictitious play algorithm by developing a novel procedure for finding a best response strategy, which is specifically designed to deal with continuous and, therefore, infinite type spaces. We then combine the best response computation with the general fictitious play structure to obtain an equilibrium. To illustrate the power of this approach, we apply our algorithm to the domain of simultaneous auctions with continuous private values and discrete bids, in which the algorithm shows quick convergence.

The problem of fairly dividing a cake (as a metaphor for a heterogeneous divisible good) has been the subject of much interest since the 1940's, and is of importance in multiagent resource allocation. Two fairness criteria are usually considered: proportionality, in the sense that each of the n agents receives at least 1/n of the cake; and the stronger property of envy-freeness, namely that each agent prefers its own piece of cake to the others' pieces. For proportional division, there are algorithms that require O(nlogn) steps, and recent lower bounds imply that one cannot do better. In stark contrast, known (discrete) algorithms for envy-free division require an unbounded number of steps, even when there are only four agents. In this paper, we give an Omega(n 2 ) lower bound for the number of steps required by envy-free cake-cutting algorithms. This result provides, for the first time, a true separation between envy-free and proportional division, thus giving a partial explanation for the notorious difficulty of the former problem.

Strategyproof classification deals with a setting where a decision-maker must classify a set of input points with binary labels, while minimizing the expected error. The labels of the input points are reported by self-interested agents, who might lie in order to obtain a classifier that more closely matches their own labels, thus creating a bias in the data; this motivates the design of truthful mechanisms that discourage false reports. Previous work [Meir et al., 2008] investigated both decision-theoretic and learning-theoretic variations of the setting, but only considered classifiers that belong to a degenerate class. In this paper we assume that the agents are interested in a shared set of input points. We show that this plausible assumption leads to powerful results. In particular, we demonstrate that variations of a truthful random dictator mechanism can guarantee approximately optimal outcomes with respect to any class of classifiers.

Experiments show that these approaches achieve high effectiveness Negotiation scenarios involving nonlinear utility (measured as high optimality rates and low failure rates functions are specially challenging, because traditional for the negotiations) in the evaluation scenario they describe negotiation mechanisms cannot be applied. (Section 2). However, as we will show empirically in Section Even mechanisms designed and proven useful for 5.2, these approaches perform worse as the circumstances of nonlinear utility spaces may fail if the utility space the scenario turn harder (that is, when the utility functions is highly nonlinear. For example, although both are highly nonlinear, like in B2B interactions or distributed contract sampling and constraint sampling have automated control systems). Under these circumstances, the been successfully used in auction based negotiations failure rate increases drastically, raising the need for an alternative with constraint-based utility spaces, they tend approach.

Buoyed by recent successes in the area of distributed constraint optimization problems (DCOPs), this paper addresses challenges faced when applying DCOPs to real-world domains. Three fundamental challenges must be addressed for a class of real-world domains, requiring novel DCOP algorithms. First, agents may not know the payoff matrix and must explore the environment to determine rewards associated with variable settings. Second, agents may need to maximize total accumulated reward rather than instantaneous final reward. Third, limited time horizons disallow exhaustive exploration of the environment. We propose and implement a set of novel algorithms that combine decision-theoretic exploration approaches with DCOP-mandated coordination. In addition to simulation results, we implement these algorithms on robots, deploying DCOPs on a distributed mobile sensor network.

This paper considers the setting wherein a group of agents (e.g., robots) is seeking to obtain a given tangible good, potentially available at different locations in a physical environment. Traveling between locations, as well as acquiring the good at any given location consumes from the resources available to the agents (e.g., battery charge). The availability of the good at any given location, as well as the exact cost of acquiring the good at the location is not fully known in advance, and observed only upon physically arriving at the location. However, a-priori probabilities on the availability and potential cost are provided. Given such as setting, the problem is to find a strategy/plan that maximizes the probability of acquiring the good while minimizing resource consumption. Sample applications include agents in exploration and patrol missions, e.g., rovers on Mars seeking to mine a specific mineral. Although this model captures many real world scenarios, it has not been investigated so far. We focus on the case where locations are aligned along a path, and study several variants of the problem, analyzing the effects of communication and coordination. For the case that agents can communicate, we present a polynomial algorithm that works for any fixed number of agents. For non-communicating agents, we present a polynomial algorithm that is suitable for any number of agents. Finally, we analyze the difference between homogeneous and heterogeneous agents, both with respect to their allotted resources and with respect to their capabilities.

In social choice, a preference function (PF) takes a set of votes (linear orders over a set of alternatives) as input, and produces one or more rankings (also linear orders over the alternatives) as output. Such functions have many applications, for example, aggregating the preferences of multiple agents, or merging rankings (of, say, webpages) into a single ranking. The key issue is choosing a PF to use. One natural and previously studied approach is to assume that there is an unobserved "correct" ranking, and the votes are noisy estimates of this. Then, we can use the PF that always chooses the maximum likelihood estimate (MLE) of the correct ranking. In this paper, we define simple ranking scoring functions (SRSFs) and show that the class of neutral SRSFs is exactly the class of neutral PFs that are MLEs for some noise model. We also define composite ranking scoring functions (CRSFs) and show a condition under which these coincide with SRSFs. We study key properties such as consistency and continuity, and consider some example PFs. In particular, we study Single Transferable Vote (STV), a commonly used PF, showing that it is a CRSF but not an SRSF, thereby clarifying the extent to which it is an MLE function. This also gives a new perspective on how ties should be broken under STV. We leave some open questions.

We introduce coalitional games with beliefs (CGBs), a natural generalization of coalitional games to environments where agents possess private beliefs regarding the capabilities (or types) of others. We put forward a model to capture such agent-type uncertainty, and study coalitional stability in this setting. Specifically, we introduce a notion of the core for CGBs, both with and without coalition structures. For simple games without coalition structures, we then provide a characterization of the core that matches the one for the full information case, and use it to derive a polynomial-time algorithm to check core nonemptiness. In contrast, we demonstrate that in games with coalition structures allowing beliefs increases the computational complexity of stability-related problems. In doing so, we introduce and analyze weighted voting games with beliefs, which may be of independent interest. Finally, we discuss connections between our model and other classes of coalitional games.