Building and using agent-based models is often impractical, in part due to the cost of including expensive subject matter experts (SMEs) in the development process. In this paper, we describe a method for "bootstrapping" model building to lower the cost of overall model development. The models we are interested in here capture dynamic phenomena related to international and subnational conflict. The method of acquiring these models begins with event data drawn from news reports about a conflict region, and infers model characteristics particular to a conflict modeling framework called the Power Structure Toolkit (PSTK). We describe the toolkit and how it has been used prior to this work. We then describe the current problem of modeling conflict and the empirical data available to learn models, and extensions to the PSTK for model generation from this data. We also describe a formative evaluation of the system that compares the performance and costs of models built entirely by an SME against models built with an SME aided by the automated model generation process. Early results indicate at least equivalent prediction rates with significant savings in model generation costs.

In this paper, we introduce an on-line, decentralised coordination algorithm for monitoring and predicting the state of spatial phenomena by a team of mobile sensors. These sensors have their application domain in disaster response, where strict time constraints prohibit path planning in advance. The algorithm enables sensors to coordinate their movements with their direct neighbours to maximise the collective information gain, while predicting measurements at unobserved locations using a Gaussian process. It builds upon the max-sum message passing algorithm for decentralised coordination, for which we present two new generic pruning techniques that result in speed-up of up to 92% for 5 sensors. We empirically evaluate our algorithm against several on-line adaptive coordination mechanisms, and report a reduction in root mean squared error up to 50% compared to a greedy strategy.

Emerging service-oriented technologies allow software agents to automatically procure distributed services to complete complex tasks. However, in many application scenarios, service providers demand financial remuneration, execution times are uncertain and consumers have  deadlines for their tasks. In this paper, we address these issues by developing a novel approach that dynamically procures multiple, redundant services over time, in order to ensure success by the deadline. Specifically, we first present an algorithm for finding optimal procurement solutions, as well as a heuristic algorithm that achieves over 99% of the optimal and is capable of handling thousands of providers. Using experiments, we show that these algorithms achieve an improvement of up to 130% over current strategies that procure only single services. Finally, we consider settings where service costs are not known to the consumer, and introduce several mechanisms that incentivise providers to reveal their costs truthfully and that still achieve up to 95% efficiency.

Artificial societies — distributed systems of autonomous agents — are becoming increasingly important in e-commerce. Agents base their decisions on trust and reputation in ways analogous to human societies. Many different definitions for trust and reputation have been proposed that incorporate many sources of information; however, system designs have tended to focus much of their attention on direct interactions. Furthermore, trust updating schemes for direct interactions have tended to uncouple updates for positive and negative feedback. Consequently, behaviour in which cycles of positive feedback followed by a single negative feedback results in untrustworthy agents remaining undetected. This con-man style of behaviour is formally described and desirable characteristics of con-resistant trust schemes proposed. A con-resistant scheme is proposed and compared with FIRE, Regret and Yu and Singh's model. Simulation experiments demonstrate the utility of the con-resistant scheme.

Coalition structure generation has received considerable attention in recent research. Several algorithms have been proposed to solve this problem in Characteristic Function Games (CFGs), where every coalition is assumed to perform equally well in any coalition structure containing it. In contrast, very little attention has been given to the more general Partition Function Games (PFGs), where a coalition's effectiveness may change from one coalition structure to another. In this paper, we deal with PFGs with positive and negative externalities. In this context, we identify the minimum search that is required in order to establish a bound on the quality of the best coalition structure found. We then develop an anytime algorithm that improves this bound with further search, and show that it outperforms the existing state-of-the-art algorithms by orders of magnitude.

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.

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.

The problem of market clearing in an economy is that of finding prices such that supply meets demand. In this work, we propose a kernel method to compute nonlinear clearing prices for instances where linear prices do not suffice. We first present a procedure that, given a sample of values and costs for a set of bundles, implicitly computes nonlinear clearing prices by solving an appropriately formulated quadratic program. We then use this as a subroutine in an elicitation procedure that queries demand and supply incrementally over rounds, only as much as needed to reach clearing prices. An empirical evaluation demonstrates that, with a proper choice of kernel function, the method is able to find sparse nonlinear clearing prices with much less than full revelation of values and costs. When the kernel function is not suitable to clear the market, the method can be tuned to achieve approximate clearing.

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.