Such agents may simply respond to their current environment without worrying about An important task in the analysis of multiagent systems the subsequent reaction of other agents; such behavior is said is to understand how groups of selfish players to be myopic. Now, coalition formation by computationally can form coalitions, i.e., work together in teams. In limited agents has been studied by a number of researchers in this paper, we study the dynamics of coalition formation multi-agent systems, starting with the work of [Shehory and under bounded rationality. We consider settings Kraus, 1999] and [Sandholm and Lesser, 1997]. However, where each team's profit is given by a concave myopic behavior in coalition formation received relatively little function, and propose three profit-sharing schemes, attention in the literature (for some exceptions, see [Dieckmann each of which is based on the concept of marginal and Schwalbe, 2002; Chalkiadakis and Boutilier, 2004; utility. The agents are assumed to be myopic, i.e., Airiau and Sen, 2009]). In contrast, myopic dynamics of they keep changing teams as long as they can increase non-cooperative games is the subject of a growing body of their payoff by doing so. We study the properties research (see, e.g.

We investigate the problem of influencing the preferences of players within a Boolean game so that, if all players act rationally, certain desirable outcomes will result. The way in which we influence preferences is by overlaying games with taxation schemes. In a Boolean game, each player has unique control of a set of Boolean variables, and the choices available to the player correspond to the possible assignments that may be made to these variables. Each player also has a goal, represented by a Boolean formula, that they desire to see satisfied. Whether or not a player’s goal is satisfied will depend both on their own choices and on the choices of others, which gives Boolean games their strategic charac- ter. We extend this basic framework by introducing an external principal who is able to levy a taxation scheme on the game, which imposes a cost on every possible action that a player can choose. By designing a taxation scheme appropriately, it is possible to perturb the preferences of the players, so that they are incentivised to choose some equilibrium that would not otherwise be chosen. After motivating and formally presenting our model, we explore some issues surrounding it, including the complexity of finding a taxation scheme that implements some socially desirable outcome, and then discuss desirable properties of taxation schemes.

In this paper we propose a mechanism that encourages agents, participating in an open MAS, to follow a desirable behaviour, by introducing modifications in the environment. This mechanism is deployed by using an infrastructure based on institutional agents called incentivators. Each external agent is assigned to an incentivator that is able to discover its preferences, and to learn the suitable modifications in the environment, in order to improve the global utility of a system in response to inadequate design or changes in the population of participating agents. The mechanism is evaluated in a p2p scenario.

We consider the following sequential allocation process. A benevolent central authority has to allocate a set of indivisible goods to a set of agents whose preferences it is totally ignorant of. We consider the process of allocating objects one after the other by designating an agent and asking her to pick one of the objects among those that remain. The problem consists in choosing the "best" sequence of agents, according to some optimality criterion. We assume that agents have additive preferences over objects. The choice of an optimality criterion depends on three parameters: how utilities of objects are related to their ranking in an agent's preference relation; how the preferences of different agents are correlated; and how social welfare is defined from the agents' utilities. We address the computation of a sequence maximizing expected social welfare under several assumptions. We also address strategical issues.

Note that this extra to make decisions about which plans are used to information is included as a preference rather than a goal, achieve their goals. Usually the choice of which as it is acceptable to satisfy the goal without satisfying the plan to use to achieve a particular goal is left up preference. For example, if the user prefers to fly on Dodgy to the system to determine. In this paper we show Airlines, but no such flights are available, then specifying this how preferences, which can be set by the user of the as a preference means that the user can still have a holiday; system, can be incorporated into the BDI execution specifying this as a goal would mean that the user refuses to process and used to guide the choices made.

Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information about their costs, constraints and preferences private, thus precluding conventional negotiation. We show how AI techniques, in particular Distributed Constraint Optimization (DCOP), can be integrated with cryptographic techniques to allow such coordination without revealing agents' private information. The problem of assigning customers to companies is formulated as a DCOP, for which we propose two novel, privacy-preserving algorithms. We compare their performances and privacy properties on a set of Vehicle Routing Problem benchmarks.

One approach for artificially intelligent agents wishing to maximise some performance metric in a given domain is to learn from a collection of training data that consists of actions or decisions made by some expert, in an attempt to imitate that expert's style. We refer to this type of agent as an expert imitator. In this paper we investigate whether performance can be improved by combining decisions from multiple expert imitators. In particular, we investigate two existing approaches for combining decisions. The first approach combines decisions by employing ensemble voting between multiple expert imitators. The second approach dynamically selects the best imitator to use at runtime given the performance of the imitators in the current environment. We investigate these approaches in the domain of computer poker. In particular, we create expert imitators for limit and no limit Texas Hold'em and determine whether their performance can be improved by combining their decisions using the two approaches listed above.

Programming agent behaviors is a tedious task. In HITAB, agents learn a hierarchical finite state automata The difficulty increases in a multiagent setting due to the increased (HFA) represented as a Moore machine where individual size of the design space. Density of interactions, the states correspond to agent behaviors or another HFA. An number of agents and the agent's heterogeneity (both capabilities HFA is built iteratively: staring with a behavior library consisting and behaviors) all contribute to the larger design space. The now expanded One training approach is Learning from Demonstration behavior library is then used to train an even more (LfD) in which agents learn behaviors in real-time based on complex behavior which is then saved to the library, and provided examples from a human demonstrator.

Distributed electricity producers, such as small wind farms and solar installations, pose several technical and economic challenges in Smart Grid design. One approach to addressing these challenges is through Broker Agents who buy electricity from distributed producers, and also sell electricity to consumers, via a Tariff Market--a new market mechanism where Broker Agents publish concurrent bid and ask prices. We investigate the learning of pricing strategies for an autonomous Broker Agent to profitably participate in a Tariff Market. We employ Markov Decision Processes (MDPs) and reinforcement learning. An important concern with this method is that even simple representations of the problem domain result in very large numbers of states in the MDP formulation because market prices can take nearly arbitrary real values. In this paper, we present the use of derived state space features, computed using statistics on Tariff Market prices and Broker Agent customer portfolios, to obtain a scalable state representation. We also contribute a set of pricing tactics that form building blocks in the learned Broker Agent strategy. We further present a Tariff Market simulation model based on real-world data and anticipated market dynamics. We use this model to obtain experimental results that show the learned strategy performing vastly better than a random strategy and significantly better than two other non-learning strategies.

We address the problem of optimal path finding for multiple agents where agents must not collide and their total travel cost should be minimized. Previous work used traditional single-agent search variants of the A* algorithm. We present a novel formalization for this problem which includes a search tree called the increasing cost tree (ICT) and a corresponding search algorithm that finds optimal solutions. We analyze this new formalization and compare it to the previous state-of-the-art A*-based approach. Experimental results on various domains show the benefits and drawbacks of this approach. A speedup of up to 3 orders of magnitude was obtained in a number of cases.