An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

Machine Learning Approach for Optimizing Negotiation Agents

The increasing popularity of Internet and World Wide Web (WWW) fuels the rise of electronic commerce (E-Commerce). Negotiation plays an important role in ecommerce as business deals are often made through some kind of negotiations. Negotiation is the process of resolving conflicts among parties having different criteria so that they can reach an agreement in which all their constraints are satisfied. Automating negotiation can save human’s time and effort to solve these combinatorial problems. Intelligent Trading Agency (ITA) is an automated agentbased one-to-many negotiation framework which is incorporated by several one-toone negotiations. ITA uses constraint satisfaction approach to evaluate and generate offers during the negotiation. This one-to-many negotiation model in e-commerce retail has advantages in terms of customizability, scalability, reusability and robustness. Since negotiation agents practice predefined negotiation strategies, decisions of the agents to select the best course of action do not take the dynamics of negotiation into consideration. The lack of knowledge capturing between agents during the negotiation causes the inefficiency of negotiation while the final outcomes obtained are probably sub-optimal. The objective of this research is to implement machine learning approach that allows agents to reuse their negotiation experience to improve the final outcomes of one-to-many negotiation. The preliminary research on automated negotiation agents utilizes case-based reasoning, Bayesian learning and evolutionary approach to learn the negotiation. The geneticbased and Bayesian learning model of multi-attribute one-to-many negotiation, namely GA Improved-ITA and Bayes Improved-ITA are proposed. In these models, agents learn the negotiation by capturing their opponent’s preferences and constraints. The two models are tested in randomly generated negotiation problems to observe their performance in negotiation learning. The learnability of GA Improved-ITA enables the agents to identify their opponent’s preferable negotiation issues. Bayes Improved-ITA agents model their opponent’s utility structure by employing Bayesian belief updating process. Results from the experimental work indicate that it is promising to employ machine learning approach in negotiation problems. GA Improved-ITA and Bayes Improved-ITA have achieved better performance in terms of negotiation payoff, negotiation cost and justification of negotiation decision in comparison with ITA. The joint utility of GA Improved-ITA and Bayes Improved-ITA is 137.5% and 125% higher than the joint utility of ITA while the negotiation cost of GA Improved-ITA is 28.6% lower than ITA. The negotiation successful rate of GA Improved-ITA and Bayes Improved-ITA is 10.2% and 37.12% higher than ITA. By having knowledge of opponent’s preferences and constraints, negotiation agents can obtain more optimal outcomes. As a conclusion, the adaptive nature of agents will increase the fitness of autonomous agents in the dynamic electronic market rather than practicing the sophisticated negotiation strategies. As future work, the GA and Bayes Improved-ITA can be integrated with grid concept to allocate and acquire resource among cross-platform agents during negotiation

Learning in Multi-Agent Information Systems - A Survey from IS Perspective

Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area

Unanimously acceptable agreements for negotiation teams in unpredictable domains

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences. 2014 Elsevier B.V. All rights reserved.This research is partially supported by TIN2012-36586-C03-01 of the Spanish government and PROMETEOII/2013/019 of Generalitat Valenciana. Other part of this research is supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic Affairs; the Pocket Negotiator Project with Grant No. VICI-Project 08075.Sánchez Anguix, V.; Aydogan, R.; Julian Inglada, VJ.; Jonker, C. (2014). Unanimously acceptable agreements for negotiation teams in unpredictable domains. Electronic Commerce Research and Applications. 13(4):243-265. https://doi.org/10.1016/j.elerap.2014.05.002S24326513

A recommendation framework based on automated ranking for selecting negotiation agents. Application to a water market

This thesis presents an approach which relies on automatic learning and data mining techniques in order to search the best group of items from a set, according to the behaviour observed in previous groups. The approach is applied to a framework of a water market system, which aims to develop negotiation processes, where trading tables are built in order to trade water rights from users. Our task will focus on predicting which agents will show the most appropriate behaviour when are invited to participate in a trading table, with the purpose of achieving the most bene cial agreement. This way, a model is developed and learns from past transactions occurred in the market. Then, when a new trading table is opened in order to trade a water right, the model predicts, taking into account the individual features of the trading table, the behaviour of all the agents that can be invited to join the negotiation, and thus, becoming potential buyers of the water right. Once the model has made the predictions for a trading table, the agents are ranked according to their probability (which has been assigned by the model) of becoming a buyer in that negotiation. Two di erent methods are proposed in the thesis for dealing with the ranked participants. Depending on the method used, from this ranking we can select the desired number of participants for making the group, or choose only the top user of the list and rebuild the model adding some aggregate information in order to throw a more detailed prediction.Dura Garcia, EM. (2011). A recommendation framework based on automated ranking for selecting negotiation agents. Application to a water market. http://hdl.handle.net/10251/15875Archivo delegad

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

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future