Enrico H. Gerding

A revenue sharing mechanism for federated search and advertising

Federated search engines combine search results from two or more (general--purpose or domain--specific) content providers. They enable complex searches (e.g., complete vacation planning) or more reliable results by allowing users to receive high quality results from a variety of sources. We propose a new revenue sharing mechanism for federated search engines, considering different actors involved in the search results generation (i.e., content providers, advertising providers, hybrid content+advertising providers, and content integrators). We extend the existing sponsored search auctions by supporting heterogeneous participants and redistribution of monetary values to the different actors, while maintaining flexibility in the payment scheme.

Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions

We describe a system for bilateral negotiations in which artificial agents aregenerated by an evolutionary algorithm (EA). The negotiations are governed bya finite-horizon version of the alternating-offers protocol. Several issuesare negotiated simulataneously. We first analyse and validate the outcomes ofthe evolutionary system, using the game-theoretic subgame-perfect equilibriumas a benchmark. We then present two extensions of the negotiation model. Inthe first extension agents take into account the fairness of the obtainedpayoff. We find that when the fairness norm is consistently applied during thenegotiation, agents reach symmetric outcomes which are robust and ratherinsensitive to the actual fairness settings. In the second extension we modela competitive market situation where agents have multiple bargainingopportunities before reaching the final agreement. Symmetric outcomes are nowalso obtained, even when the number of bargaining opportunities is small. Wefurthermore study the influence of search or negotiation costs in this game.

Using Gaussian processes to optimise concession in complex negotiations against unknown opponents

In multi-issue automated negotiation against unknown opponents, a key part of effective negotiation is the choice of concession strategy. In this paper, we develop a principled concession strategy, based on Gaussian processes predicting the opponents future behaviour. We then use this to set the agents concession rate dynamically during a single negotiation session. We analyse the performance of our strategy and show that it outperforms the state-of-the-art negotiating agents from the 2010 Automated Negotiating Agents Competition, in both a tournament setting and in self-play, across a variety of negotiation domains.

Automated trading with performance weighted random forests and seasonality

Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.

Automated Negotiation and Bundling of Information Goods

In this paper, we present a novel system for selling bundles of news items. Through the system, customers bargain with the seller over the price and quality of the delivered goods. The advantage of the developed system is that it allows for a high degree of flexibility in the price, quality, and content of the offered bundles. The price, quality, and content of the delivered goods may, for example, differ based on daily dynamics and personal interests of customers. Autonomous “software agents” execute the negotiation on behalf of the users of the system. To perform the actual negotiation these agents make use of bargaining strategies. We decompose bargaining strategies into concession strategies and Pareto efficient search strategies. Additionally, we introduce the orthogonal and orthogonal-DF strategy: two Pareto search strategies. We show through computer experiments that the use of these Pareto search strategies will result in very efficient bargaining outcomes. Moreover, the system is set up such that it is actually in the best interest of the customer to have their agent adhere to this approach of disentangling the bargaining strategy.

IAMhaggler: A Negotiation Agent for Complex Environments

We describe the strategy used by our agent, IAMhaggler, which finished in third place in the 2010 Automated Negotiating Agent Competition. It uses a concession strategy to determine the utility level at which to make offers. This concession strategy uses a principled approach which considers the offers made by the opponent. It then uses a Pareto-search algorithm combined with Bayesian learning in order to generate a multi-issue offer with a specific utility as given by its concession strategy.

What the 2007 TAC Market Design Game tells us about effective auction mechanisms

This paper analyzes the entrants to the 2007 tac Market Design Game. We present a classification of the entries to the competition, and use this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of post-tournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case.

An online mechanism for multi-unit demand and its application to plug-in hybrid electric vehicle charging

We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.

An Overview of the Results and Insights from the Third Automated Negotiating Agents Competition (ANAC2012)

The third Automated Negotiating Agents Competition (ANAC 2012) was held at the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2012, Valencia, Spain). ANAC is an international competition that aims to encourage research into bilateral, multi-issue negotiation, by providing a platform in which strategies developed independently by different research teams can be tried and compared against each other, in a real-time competition. In the 2012 edition, we received 17 entries from 9 different universities worldwide, out of which 8 were selected for the final round. This chapter aims to provide a broad description of the competition set-up (especially highlighting the changes from previous editions), the preference domains and the strategies submitted, as well as the results from both the qualifying and final rounds.

Intention-aware routing to minimise delays at electric vehicle charging stations: the research related to this demonstration has been published at IJCAI 2013 [1]

En-route charging stations allow electric vehicles to greatly extend their range. However, as a full charge takes a considerable amount of time, there may be significant waiting times at peak hours. To address this problem, we propose a novel navigation system, which communicates its intentions (i.e., routing policies) to other drivers. Using these intentions, our system accurately predicts congestion at charging stations and suggests the most efficient route to its user. We achieve this by extending existing time-dependent stochastic routing algorithms to include the batterys state of charge and charging stations. Furthermore, we describe a novel technique for combining historical information with agent intentions to predict the queues at charging stations. Through simulations we show that our system leads to a significant increase in utility compared to existing approaches that do not explicitly model waiting times or use intentions, in some cases reducing waiting times by over 80% and achieving near-optimal overall journey times. This work was published at IJCAI 2013 [1].