Valentin Robu

UAI Conference 2021 Conference Paper

BayLIME: Bayesian local interpretable model-agnostic explanations

• Xingyu Zhao 0001
• Wei Huang 0035
• Xiaowei Huang 0001
• Valentin Robu
• David Flynn

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI – which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e. g. , a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.

AAAI Conference 2019 Conference Paper

Probabilistic Model Checking of Robots Deployed in Extreme Environments

• Xingyu Zhao
• Valentin Robu
• David Flynn
• Fateme Dinmohammadi
• Michael Fisher
• Matt Webster

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot’s safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.

IJCAI Conference 2017 Conference Paper

Contract Design for Energy Demand Response

• Reshef Meir
• Hongyao Ma
• Valentin Robu

Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, and enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.

AAMAS Conference 2017 Conference Paper

Generalizing Demand Response Through Reward Bidding

• Hongyao Ma
• David C. Parkes
• Valentin Robu

Demand-side response (DR) is emerging as a crucial technology to assure stability of modern power grids. The uncertainty about the cost agents face for reducing consumption imposes challenges in achieving reliable, coordinated response. In recent work, Ma et al. [13] introduce DR as a mechanism design problem and solve it for a setting where an agent has a binary preparation decision and where, contingent on preparation, the probability an agent will be able to reduce demand and the cost to do so are fixed. We generalize this model to allow uncertainty in agents’ costs of responding, and also multiple levels of effort agents can exert in preparing. For both cases, the design of contingent payments now affects the probability of response. We design a new, truthful and reliable mechanism that uses a “rewardbidding”approach rather than the“penalty-bidding”approach. It has good performance when compared to natural benchmarks. The mechanism also extends to handle multiple units of demand response from each agent.

AAMAS Conference 2016 Conference Paper

Game-Theoretic Modeling of Transmission Line Reinforcements with Distributed Generation (Extended Abstract)

• Merlinda Andoni
• Valentin Robu

Favourable sites for renewable generation are often remote locations (such as islands) where installed capacity, e. g. from wind turbines, exceeds local aggregate demand. We study the effect that curtailment mechanisms - applied when there is excess generation - have on the incentives to build additional capacity and the profitability of the generators. Next, for a two-location setting, we study the combined effect that curtailment schemes and line access rules have on the decision to invest in transmission expansion. In particular, for “common access” rules, this leads to a Stackelberg game between transmission and local generation capacity investors, and we characterise the equilibrium of this game. Finally, we apply and exemplify our model to a concrete problem of a grid reinforcement project, between Hunterston and the Kintyre peninsula, in western Scotland, and we determine a mechanism for setting transmission charges that assures both the profitability of the line and local renewable investors.

IJCAI Conference 2016 Conference Paper

Incentivizing Reliability in Demand-Side Response

• Hongyao Ma
• Valentin Robu
• Na Li
• David C. Parkes

We study the problem of incentivizing reliable demand-response in modern electricity grids. Each agent is uncertain about her future ability to reduce demand and unreliable. Agents who choose to participate in a demand-response scheme may be paid when they respond and penalized otherwise. The goal is to reliably achieve a demand reduction target while selecting a minimal set of agents from those willing to participate. We design incentive-aligned, direct and indirect mechanisms. The direct mechanism elicits both response probabilities and costs, while the indirect mechanism elicits willingness to accept a penalty in the case of non-response. We benchmark against a spot auction, in which demand reduction is purchased from agents when needed. Both the direct and indirect mechanisms achieve the reliability target in a dominant-strategy equilibrium, select a small number of agents to prepare, and do so at low cost and with much lower variance in payments than the spot auction.

IJCAI Conference 2016 Conference Paper

Online Mechanism Design for Vehicle-to-Grid Car Parks

• Enrico H. Gerding
• Sebastian Stein
• Sofia Ceppi
• Valentin Robu

Vehicle-to-grid (V2G) is a promising approach whereby electric vehicles (EVs) are used to store excess electricity supply (e. g. , from renewable sources), which is sold back to the grid in times of scarcity. In this paper we consider the setting of a smart car park, where EVs come and go, and can be used for V2G while parked. We develop novel allocation and payment mechanisms which truthfully elicit the EV owners' preferences and constraints, including arrival, departure, required charge, as well as the costs of discharging due to loss of efficiency of the battery. The car park will schedule the charging and discharging of each EV, ensuring the constraints of the EVs are met, and taking into consideration predictions about future electricity prices. Optimally solving the global problem is intractable, and we present three novel heuristic online scheduling algorithms. We show that, under certain conditions, two of these satisfy monotonicity and are therefore truthful. We furthermore evaluate the algorithms using simulations, and we show that some of our algorithms benefit significantly from V2G, achieving positive benefit for the car park even when agents do not pay for using it.

AAAI Conference 2014 Conference Paper

Efficient Buyer Groups for Prediction-of-Use Electricity Tariffs

• Valentin Robu
• Meritxell Vinyals
• Alex Rogers
• Nicholas Jennings

Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customer’s consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work (Vinyals et al. 2014) studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i. e. costminimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-ofuse tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK.

UAI Conference 2014 Conference Paper

Efficient Regret Bounds for Online Bid Optimisation in Budget-Limited Sponsored Search Auctions

• Long Tran-Thanh
• Lampros C. Stavrogiannis
• Victor Naroditskiy
• Valentin Robu
• Nicholas R. Jennings
• Peter B. Key

We study the problem of an advertising agent who needs to intelligently distribute her budget across a sequence of online keyword bidding auctions. We assume the closing price of each auction is governed by the same unknown distribution, and study the problem of making provably optimal bidding decisions. Learning the distribution is done under censored observations, i. e. the closing price of an auction is revealed only if the bid we place is above it. We consider three algorithms, namely ε−First, Greedy Product- Limit (GPL) and LuekerLearn, respectively, and we show that these algorithms provably achieve Hannan-consistency. In particular, we show that the regret bound of ε−First is at most O(T 2 3 ) with high probability. For the other two algorithms, we first prove that, by using a censored data distribution estimator proposed by Zeng [19], the empirical distribution of the closing market price converges in probability to its true distribution with a O( 1 √ t ) rate, where t is the number of updates. Based on this result, we prove that both GPL and LuekerLearn achieve O( √ T) regret bound with high probability. This in fact provides an affirmative answer to the research question raised in [1]. We also evaluate the abovementioned algorithms using real bidding data, and show that although GPL achieves the best performance on average (up to 90% of the optimal solution), its long running time may limit its suitability in practice. By contrast, LuekerLearn and ε−First proposed in this paper achieve up to 85% of the optimal, but with an exponential reduction in computational complexity (a saving up to 95%, compared to GPL).

IJCAI Conference 2013 Conference Paper

Efficient Interdependent Value Combinatorial Auctions with Single Minded Bidders

• Valentin Robu
• David C. Parkes
• Takayuki Ito
• Nicholas R. Jennings

We study the problem of designing efficient auctions where bidders have interdependent values; i. e. , values that depend on the signals of other agents. We consider a contingent bid model in which agents can explicitly condition the value of their bids on the bids submitted by others. In particular, we adopt a linear contingent bidding model for single minded combinatorial auctions (CAs), in which submitted bids are linear combinations of bids received from others. We extend the existing state of the art, by identifying constraints on the interesting bundles and contingency weights reported by the agents which allow the efficient second priced, fixed point bids auction to be implemented in single minded CAs. Moreover, for domains in which the required single crossing condition fails (which characterizes when efficient, IC auctions are possible), we design a two-stage mechanism in which a subset of agents (“experts”) are allocated first, using their reports to allocate the remaining items to the other agents.

AIJ Journal 2013 Journal Article

Evaluating practical negotiating agents: Results and analysis of the 2011 international competition

• Tim Baarslag
• Katsuhide Fujita
• Enrico H. Gerding
• Koen Hindriks
• Takayuki Ito
• Nicholas R. Jennings
• Catholijn Jonker
• Sarit Kraus

This paper presents an in-depth analysis and the key insights gained from the Second International Automated Negotiating Agents Competition (ANAC 2011). ANAC is an international competition that challenges researchers to develop successful automated negotiation agents for scenarios where there is no information about the strategies and preferences of the opponents. The key objectives of this competition are to advance the state-of-the-art in the area of practical bilateral multi-issue negotiations, and to encourage the design of agents that are able to operate effectively across a variety of scenarios. Eighteen teams from seven different institutes competed. This paper describes these agents, the setup of the tournament, including the negotiation scenarios used, and the results of both the qualifying and final rounds of the tournament. We then go on to analyse the different strategies and techniques employed by the participants using two methods: (i) we classify the agents with respect to their concession behaviour against a set of standard benchmark strategies and (ii) we employ empirical game theory (EGT) to investigate the robustness of the strategies. Our analysis of the competition results allows us to highlight several interesting insights for the broader automated negotiation community. In particular, we show that the most adaptive negotiation strategies, while robust across different opponents, are not necessarily the ones that win the competition. Furthermore, our EGT analysis highlights the importance of considering metrics, in addition to utility maximisation (such as the size of the basin of attraction), in determining what makes a successful and robust negotiation agent for practical settings.

IJCAI Conference 2013 Conference Paper

Intention-Aware Routing to Minimise Delays at Electric Vehicle Charging Stations

• Mathijs M. de Weerdt
• Enrico H. Gerding
• Sebastian Stein
• Valentin Robu
• Nicholas R. Jennings

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 battery’s 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.

AAMAS Conference 2012 Conference Paper

A Model-Based Online Mechanism with Pre-Commitment and its Application to Electric Vehicle Charging

• Sebastian Stein
• Enrico Gerding
• Valentin Robu
• NICK JENNINGS

We introduce a novel online mechanism that schedules the allocation of an expiring and continuously-produced resource to self-interested agents with private preferences. A key application of our mechanism is the charging of pure electric vehicles, where owners arrive dynamically over time, and each owner requires a minimum amount of charge by its departure to complete its next trip. To truthfully elicit the agents' preferences in this setting, we introduce the new concept of pre-commitment: Whenever an agent is selected, our mechanism pre-commits to charging the vehicle by its reported departure time, but maintains flexibility about \emph{when} the charging takes place and at \emph{what rate}. Furthermore, to make effective allocation decisions we use a model-based approach by modifying Consensus, a well-known online optimisation algorithm. We show that our pre-commitment mechanism with modified Consensus incentivises truthful reporting. Furthermore, through simulations based on real-world data, we show empirically that the average utility achieved by our mechanism is 93% or more of the offline optimal.

AAAI Conference 2012 Conference Paper

Cooperative Virtual Power Plant Formation Using Scoring Rules

• Valentin Robu
• Ramachandra Kota
• Georgios Chalkiadakis
• Alex Rogers
• Nicholas Jennings

Virtual Power Plants (VPPs) are fast emerging as a suitable means of integrating small and distributed energy resources (DERs), like wind and solar, into the electricity supply network (Grid). VPPs are formed via the aggregation of a large number of such DERs, so that they exhibit the characteristics of a traditional generator in terms of predictability and robustness. In this work, we promote the formation of such “cooperative” VPPs (CVPPs) using multi-agent technology. In particular, we design a payment mechanism that encourages DERs to join CVPPs with large overall production. Our method is based on strictly proper scoring rules and incentivises the provision of accurate predictions from the CVPPs—and in turn, the member DERs—which aids in the planning of the supply schedule at the Grid. We empirically evaluate our approach using the real-world setting of 16 commercial wind farms in the UK. We show that our mechanism incentivises real DERs to form CVPPs, and outperforms the current state of the art payment mechanism developed for this problem.

AAMAS Conference 2012 Conference Paper

Cooperative Virtual Power Plant Formation Using Scoring Rules

• Valentin Robu
• Ramachandra Kota
• Georgios Chalkiadakis
• Alex Rogers
• NICK JENNINGS

The growing focus on sustainable and environmentally friendly energy production has resulted in the proliferation of distributed energy resources (DERs), mainly based on renewable sources like wind and sunlight. However, their small size and the intermittent nature of their supply means that such generators cannot easily be assimilated into the current electricity network (Grid) like conventional generators. Against this background, Virtual Power Plants are fast emerging as a solution to this problem whereby a large number of small energy generators may be aggregated together such that they exhibit the characteristics like a traditional generator in terms of predictability and robustness. In this work, we propose a method to promote the formation of such “cooperative” VPPs (CVPPs) using multi-agent technology. In particular, we design a payment mechanism that encourages DERs to join CVPPs with large overall production. Our method is based on strictly proper scoring rules and elicits accurate probabilistic estimates of energy production from the CVPPs—and in turn, the member DERs— which aids in the planning of the supply schedule at the Grid. We empirically evaluate our approach using the real-world setting of 16 commercial wind farms in the UK, and we show that our mechanism incentivises real DERs to form CVPPs and, moreover, it outperforms the current state of the art payment mechanism developed for this problem.

ECAI Conference 2012 Conference Paper

Cooperatives for Demand Side Management

• Ramachandra Kota
• Georgios Chalkiadakis
• Valentin Robu
• Alex Rogers
• Nicholas R. Jennings

We propose a new scheme for efficient demand side management for the Smart Grid. Specifically, we envisage and promote the formation of cooperatives of medium-large consumers and equip them (via our proposed mechanisms) with the capability of regularly participating in the existing electricity markets by providing electricity demand reduction services to the Grid. Based on mechanism design principles, we develop a model for such cooperatives by designing methods for estimating suitable reduction amounts, placing bids in the market and redistributing the obtained revenue amongst the member agents. Our mechanism is such that the member agents have no incentive to show artificial reductions with the aim of increasing their revenues.

ECAI Conference 2012 Conference Paper

Negotiating Concurrently with Unknown Opponents in omplex, Real-Time Domains

• Colin R. Williams
• Valentin Robu
• Enrico H. Gerding
• Nicholas R. Jennings

We propose a novel strategy to enable autonomous agents to negotiate concurrently with multiple, unknown opponents in real-ime, over complex multi-issue domains. We formalise our strategy as an optimisation problem, in which decisions are based on probabilistic information about the opponents' strategies acquired during negotiation. In doing so, we develop the first principled approach that enables the coordination of multiple, concurrent negotiation threads for practical negotiation settings. Furthermore, we validate our strategy using the agents and domains developed for the International Automated Negotiating Agents Competition (ANAC), and we benchmark our strategy against the state-of-the-art. We find that our approach significantly outperforms existing approaches, and this difference improves even further as the number of available negotiation opponents and the complexity of the negotiation domain increases.

AAMAS Conference 2011 Conference Paper

Cooperatives of Distributed Energy Resources for Efficient Virtual Power Plants

• Georgios Chalkiadakis
• Valentin Robu
• Ramachandra Kota
• Alex Rogers
• Nicholas R. Jennings

The creation of Virtual Power Plants (VPPs) has been suggested in recent years as the means for achieving the cost-efficient integration of the many distributed energy resources (DERs) that are starting to emerge in the electricity network. In this work, we contribute to the development of VPPs by offering a game-theoretic perspective to the problem. Specifically, we design cooperatives (or "cooperative VPPs"-CVPPs) of rational autonomous DER-agents representing small-to-medium size renewable electricity producers, which coalesce to profitably sell their energy to the electricity grid. By so doing, we help to counter the fact that individual DERs are often excluded from the wholesale energy market due to their perceived inefficiency and unreliability. We discuss the issues surrounding the emergence of such cooperatives, and propose a pricing mechanism with certain desirable properties. Specifically, our mechanism guarantees that CVPPs have the incentive to truthfully report to the grid accurate estimates of their electricity production, and that larger rather than smaller CVPPs form; this promotes CVPP efficiency and reliability. In addition, we propose a scheme to allocate payments within the cooperative, and show that, given this scheme and the pricing mechanism, the allocation is in the core and, as such, no subset of members has a financial incentive to break away from the CVPP. Moreover, we develop an analytical tool for quantifying the uncertainty about DER production estimates, and distinguishing among different types of errors regarding such estimates. We then utilize this tool to devise protocols to manage CVPP membership. Finally, we demonstrate these ideas through a simulation that uses real-world data.

AAMAS Conference 2011 Conference Paper

Online Mechanism Design for Electric Vehicle Charging

• Enrico H. Gerding
• Valentin Robu
• Sebastian Stein
• David C. Parkes
• Alex Rogers
• Nicholas R. Jennings

Plug-in hybrid electric vehicles are expected to place a considerable strain on local electricity distribution networks, requiring charging to be coordinated in order to accommodate capacity constraints. We design a novel online auction protocol for this problem, wherein vehicle owners use agents to bid for power and also state time windows in which a vehicle is available for charging. This is a multi-dimensional mechanism design domain, with owners having non-increasing marginal valuations for each subsequent unit of electricity. In our design, we couple a greedy allocation algorithm with the occasional "burning" of allocated power, leaving it unallocated, in order to adjust an allocation and achieve monotonicity and thus truthfulness. We consider two variations: burning at each time step or on-departure. Both mechanisms are evaluated in depth, using data from a real-world trial of electric vehicles in the UK to simulate system dynamics and valuations. The mechanisms provide higher allocative efficiency than a fixed price system, are almost competitive with a standard scheduling heuristic which assumes non-strategic agents, and can sustain a substantially larger number of vehicles at the same per-owner fuel cost saving than a simple random scheme.

IJCAI Conference 2011 Conference Paper

Using Gaussian Processes to Optimise Concession in Complex Negotiations against Unknown Opponents

• Colin R. Williams
• Valentin Robu
• Enrico H. Gerding
• Nicholas R. Jennings

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 opponent's future behaviour. We then use this to set the agent's 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.

ECAI Conference 2010 Conference Paper

Addressing the Exposure Problem of Bidding Agents Using Flexibly Priced Options

• Valentin Robu
• Ioannis A. Vetsikas
• Enrico H. Gerding
• Nicholas R. Jennings

In this paper we introduce a new option pricing mechanism for reducing the exposure problem encountered by bidding agents with complementary valuations when participating in sequential, second-price auction markets. Existing option pricing models have two main drawbacks: they either apply fixed exercise prices, which may deter bidders with low valuations, thereby decreasing allocative efficiency, or options are offered for free, in which case bidders are less likely to exercise them, thereby reducing seller revenues. The proposed mechanism involving flexibly priced options addresses these problems by calculating the exercise price as well as the option price based on the bids received during an auction. For this new model, which extends and encompasses all the previous models examined, we derive the optimal strategies for a bidding agent with complementary preferences. Finally, we use these strategies to evaluate the proposed option mechanism through Monte-Carlo simutions, and compare it to existing mechanisms, both in terms of the seller revenue and the social welfare. We show that our new mechanism achieves higher market efficiency compared to having no options and free options, while achieving higher revenues for the seller than any existing option mechanism.

AAMAS Conference 2010 Conference Paper

Flexibly Priced Options: A New Mechanism for Sequential Auction Markets with Complementary Goods

• Valentin Robu
• Ioannis Vetsikas
• Enrico Gerding
• Nicholas R. Jennings

In this work, we propose a novel option pricing mechanism for reducing the exposure problem encountered by bidders with complementary valuations when participating in sequential, second-priceauction markets. In our mechanism, both the option and the exercise price are determined dynamically, by the bids received in eachauction. We show that our flexible options model can achieve bettermarket allocation efficiency, at an only marginal cost to seller revenues compared to existing state of the art option pricing models.

AAMAS Conference 2008 Conference Paper

A Multi-Agent Platform for Auction-Based Allocation of Loads in Transportation Logistics

• Han Noot
• Valentin Robu
• Han La Poutr
• eacute;
• Willem-Jan van Schijndel

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. Therefore, 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.

AAMAS Conference 2008 Conference Paper

An Interactive Platform for Auction-Based Allocation of Loads in Transportation Logistics

• Valentin Robu
• Han Noot
• Han La Poutr
• eacute;
• Willem-Jan van Schijndel

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.

JAAMAS Journal 2007 Journal Article

An agent architecture for multi-attribute negotiation using incomplete preference information

• Catholijn M. Jonker
• Valentin Robu
• Jan Treur

Abstract A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a “guessing” heuristic, by which an agent uses the history of the opponent’s bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.

AAMAS Conference 2006 Conference Paper

Automating Supply Chain Negotiations using Autonomous Agents: a Case Study in Transportation Logistics

• Sander van der Putten
• Valentin Robu
• Han La Poutre
• Annemiek Jorritsma
• Margo Gal

AAMAS Conference 2005 Conference Paper

Modeling Complex Multi-Issue Negotiations Using Utility Graphs

• Valentin Robu
• Koye Somefun
• Han La Poutre

AAMAS Conference 2004 Conference Paper

Automated Multi-Attribute Negotiation with Efficient Use of Incomplete Preference Information

• Catholijn Jonker
• Valentin Robu