A multi-agent platform for auction-based allocation of loads in transportation logistics

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands. The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i.e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings. The paper first introduces the business problem setting and then describes the architecture and main characteristics of our auction platform. We conclude with a preliminary discussion of our experience from a human bidding experiment, involving Vos planners competing for orders both against each other and against some (simple) automated strategies

Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have_nonlinear_ preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.Comment: 10 pages, 5 eps figures, ACM Proceedings documentclass, Published in "Proc. 6th Int'l Conf. on Electronic Commerce ICEC04, Delft, The Netherlands," M. Janssen, H. Sol, R. Wagenaar (eds.). ACM Pres

Negotiating over Bundles and Prices Using Aggregate Knowledge

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers.Comment: 15 pages, 7 eps figures, Springer llncs documentclass. Extended version of the paper published in "E-Commerce and Web Technologies," Kurt Bauknecht, Martin Bichler and Birgit Pr\"{o}ll (eds.). Springer Lecture Notes in Computer Science, Volume 3182, Berlin: Springer, p. 218--22

Using priced options to solve the exposure problem in sequential auctions

We propose a priced options model for solving the exposure problem of bidders with valuation synergies participating in a sequence of online auctions. We consider a setting in which complementary-valued items are offered sequentially by different sellers, who have the choice of either selling their item directly or through a priced option. In our model, the seller fixes the exercise price for this option, and then sells it through a first-price auction. We analyze this model from a decision-theoretic perspective and we show, for a setting where the competition is formed by local bidders (which desire a single item), that using options can increase the expected profit for both sides. Furthermore, we derive the equations that provide minimum and maximum bounds between which the bids of the synergy buyer are expected to fall, in order for both sides of the market to have an incentive to use the options mechanism. Next, we perform an experimental analysis of a market in which multiple synergy buyers are active simultaneously. We show that, despite the extra competition, some synergy buyers may benefit, because sellers are forced to set their exercise prices for options at levels which encourage participation of all buyers.</jats:p

Bounded Distributed Flocking Control of Nonholonomic Mobile Robots

There have been numerous studies on the problem of flocking control for multiagent systems whose simplified models are presented in terms of point-mass elements. Meanwhile, full dynamic models pose some challenging problems in addressing the flocking control problem of mobile robots due to their nonholonomic dynamic properties. Taking practical constraints into consideration, we propose a novel approach to distributed flocking control of nonholonomic mobile robots by bounded feedback. The flocking control objectives consist of velocity consensus, collision avoidance, and cohesion maintenance among mobile robots. A flocking control protocol which is based on the information of neighbor mobile robots is constructed. The theoretical analysis is conducted with the help of a Lyapunov-like function and graph theory. Simulation results are shown to demonstrate the efficacy of the proposed distributed flocking control scheme

Constraining the regular Galactic Magnetic Field with the 5-year WMAP polarization measurements at 22 GHz

[ABRIDGED] The knowledge of the regular component of the Galactic magnetic field gives important information about the structure and dynamics of the Milky Way, as well as constitutes a basic tool to determine cosmic rays trajectories. It can also provide clear windows where primordial magnetic fields could be detected. We want to obtain the regular (large scale) pattern of the magnetic field distribution of the Milky Way that better fits the polarized synchrotron emission as seen by the 5-year WMAP data at 22 GHz. We have done a systematic study of a number of Galactic magnetic field models: axisymmetric, bisymmetric, logarithmic spiral arms, concentric circular rings with reversals and bi-toroidal. We have explored the parameter space defining each of these models using a grid-based approach. In total, more than one million models are computed. The model selection is done using a Bayesian approach. For each model, the posterior distributions are obtained and marginalised over the unwanted parameters to obtain the marginal 1-D probability distribution functions. In general, axisymmetric models provide a better description of the halo component, although attending to their goodness-of-fit, the rest of the models cannot be rejected. In the case of disk component, the analysis is not very sensitive for obtaining the disk large scale structure, because of the effective available area (less than 8% of the whole map and less than 40% of the disk). Nevertheless, within a given family of models, the best-fit parameters are compatible with those found in the literature. The family of models that better describes the polarized synchrotron halo emission is the axisymmetric one, with magnetic spiral arms with a pitch angle of ~24 degrees, and a strong vertical field of 1 microG at z ~ 1 kpc. When a radial variation is fitted, models require fast variations.Comment: 14 pages, 9 figures. Accepted for publication in A&

Integrating power and reserve trade in electricity networks

As power markets become liberalised and include more intermittent generation, the trade of reserve energy will become more important. We propose a novel bidding mechanism to integrate power and reserve markets. It facilitates planning for bidding in both markets and adds expressivity to reserve bids\footnote{This work is a part of the IDeaNeD project and sponsored by Agentschap NL, a research funding agency of the dutch ministry of economic affairs, in the IOP-EMVT program. It has also been presented at the AAMAS 2011 conference

Integrating power and reserve trade in electricity networks

In power markets, the trade of reserve energy will become more important as more intermittent generation is traded. In this work, we propose a novel bidding mechanism for the integration of power and reserve markets. It adds expressivity to reserve bids and facilitates planning