Generating Pareto-Optimal Offers in Bilateral Automated Negotiation with One-Side Uncertain Importance Weights

Pareto efficiency is a seminal condition in the bargaining problem which leads autonomous agents to a Nash-equilibrium. This paper investigates the problem of the generating Pareto-optimal offers in bilateral multi-issues negotiation where an agent has incomplete information and the other one has perfect information. To this end, at first, the bilateral negotiation is modeled by split the pie game and alternating-offer protocol. Then, the properties of the Pareto-optimal offers are investigated. Finally, based on properties of the Pareto-optimal offers, an algorithmic solution for generating near-optimal offers with incomplete information is presented. The agent with incomplete information generates near-optimal offers in O(n łog n). The results indicate that, in the early rounds of the negotiation, the agent with incomplete information can generate near-optimal offers, but as time passes the agent can learn its opponents preferences and generate Pareto-optimal offers. The empirical analysis also indicates that the proposed algorithm outperform the smart random trade-offs (SRT) algorithm

Overcoming the Fixed-Pie Bias in Multi-Issue Negotiation

Multi-issue negotiation may produce mutual beneficial results to both negotiators while single-issue negotiation can not. However, there are difficulties in automating a multi-issue negotiation, since the search space grows dramatically as the number of issues increases. Although many concession strategy learning mechanisms have been proposed to deal with the problem, recent research uncovered that the fixed strategy of concession and the fixed-pie bias are the two major interferences in the automation of multi-issue negotiation. It is suggested that the lack of communication between agents may have impeded information sharing and joint-problem solving possibilities. In this paper, we show that the fixed-pie bias can interfere with the negotiation outcome if there are non-conflicting issues. We propose a new negotiation model and an innovative algorithm that not only allows information to be shared in a controlled way, but also allows the information shared to be effectively used for conducting a systematic search over the negotiation problem space. The combined mechanism is capable of using strategies learned from counter-offers and is immune to the fixed-strategy limitation and the fixed-pie bias. It contributes to the automation of multi-issue negotiation in the context of open and dynamic environments

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

We propose a novel strategy to enable autonomous agents to negotiate concurrently with multiple, unknown opponents in real-time, 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

An Agent Architecture for Concurrent Bilateral Negotiations

Abstract. We present an architecture that makes use of symbolic decision-making to support agents participating in concurrent bilateral negotiations. The architecture is a revised version of previous work with the KGP model [23, 12], which we specialise with knowledge about the agent’s self, the negotiation opponents and the environment. Our work combines the specification of domain-independent decision-making with a new protocol for concurrent negotiation that revisits the well-known alternating offers protocol [22]. We show how the decision-making can be specialised to represent the agent’s strategies, utilities and prefer-ences using a Prolog-like meta-program. The work prepares the ground for supporting decision-making in concurrent bilateral negotiations that is more lightweight than previous work and contributes towards a fully developed model of the architecture

The 2007 Summer Workshop on Money, Banking and Payments: an overview

The 2007 Summer Workshop on Money, Banking, Payments and Finance met at the Federal Reserve Bank of Cleveland this summer, as we have over the past several years. The following document summarizes and ties together the contributions presented at the workshop this year.Monetary policy ; Monetary theory ; Money ; Banks and banking

Modified bargaining protocols for automated negotiation in open multi-agent systems

Current research in multi-agent systems (MAS) has advanced to the development of open MAS, which are characterized by the heterogeneity of agents, free exit/entry and decentralized control. Conflicts of interest among agents are inevitable, and hence automated negotiation to resolve them is one of the promising solutions. This thesis studies three modifications on alternating-offer bargaining protocols for automated negotiation in open MAS. The long-term goal of this research is to design negotiation protocols which can be easily used by intelligent agents in accommodating their need in resolving their conflicts. In particular, we propose three modifications: allowing non-monotonic offers during the bargaining (non-monotonic-offers bargaining protocol), allowing strategic delay (delay-based bargaining protocol), and allowing strategic ignorance to augment argumentation when the bargaining comprises argumentation (ignorance-based argumentation-based negotiation protocol). Utility theory and decision-theoretic approaches are used in the theoretical analysis part, with an aim to prove the benefit of these three modifications in negotiation among myopic agents under uncertainty. Empirical studies by means of computer simulation are conducted in analyzing the cost and benefit of these modifications. Social agents, who use common human bargaining strategies, are the subjects of the simulation. In general, we assume that agents are bounded rational with various degrees of belief and trust toward their opponents. In particular in the study of the non-monotonic-offers bargaining protocol, we assume that our agents have diminishing surplus. We further assume that our agents have increasing surplus in the study of delay-based bargaining protocol. And in the study of ignorance-based argumentation-based negotiation protocol, we assume that agents may have different knowledge and use different ontologies and reasoning engines. Through theoretical analysis under various settings, we show the benefit of allowing these modifications in terms of agents’ expected surplus. And through simulation, we show the benefit of allowing these modifications in terms of social welfare (total surplus). Several implementation issues are then discussed, and their potential solutions in terms of some additional policies are proposed. Finally, we also suggest some future work which can potentially improve the reliability of these modifications

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

The Impact of Rent Controls in Non-Walrasian Markets: An Agent-Based Modeling Approach

We use agent-based models to consider rent ceilings in non-Walrasian housing markets, where bargaining between landlord and tenant leads to exchange at a range of prices. In the non-Walrasian setting agents who would be extramarginal in the Walrasian setting frequently are successful in renting, and actually account for a significant share of the units rented. This has several implications. First, rent ceilings above the Walrasian equilibrium price (WEP) can affect the market outcome. Second, rent ceilings that reduce the number of units rented do not necessarily reduce total market surplus. Finally, the distributional impact of rent controls differs from the Walrasian setting.