KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

Determining Successful Negotiation Strategies: The Evolution of Intelligent Agents

Due to the desire of almost all departments of business organizations to be interconnected and to make data accessible at any time and any place, more and more multi-agent systems are applied to business management. As numerous agents are roaming through the Internet, they compete for the limited resource to achieve their goal. In the end, some of them will succeed, while the others will fail. However, when agents are initially created, they have little knowledge and experience with relatively lower capability. They should also strive to adapt themselves to the changing environment. It is advantageous if they have the ability to learn and evolve. This paper addresses evolution of intelligent agents in virtual enterprises. Agent fitness and fuzzy multi-criteria decision-making approach are proposed as evolution mechanisms, and fuzzy soft goal is introduced to facilitate the evolution process. Genetic programming operators are employed to restructure agents in the proposed multi-agent evolution cycle. We conduct a series of experiments to determine the most successful strategies and to see how and when these strategies evolve depending on the context and negotiation stance of the agent’s opponent

Tasks for Agent-Based Negotiation Teams:Analysis, Review, and Challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.Comment: Engineering Applications of Artificial Intelligence, 201

On the Use of Optimization Techniques for Strategy Definition in Multi Issue Negotiations

Στην παρούσα διπλωματική εργασία αναλύεται το πρόβλημα της λήψης απόφασης σε συστήματα αυτόματων διαπραγματεύσεων. Σκοπός είναι να σχεδιαστεί ένας αποδοτικός αλγόριθμος βάσει του οποίου οι πράκτορες λογισμικού θα δρουν σε ένα σενάριο ταυτόχρονων διαπραγματεύσεων.Οι πράκτορες δεν έχουν καμία πληροφόρηση για τα χαρακτηριστικά των αντιπάλων.Οι διαπραγματεύσεις πραγματοποιούνται με απώτερο στόχο την ανταλλαγή προϊόντων μεταξύ αγοραστών και πωλητών με συγκεκριμένα ανταλλάγματα. Κάθε προϊόν χαρακτηρίζεται από μια ομάδα χαρακτηριστικών. Για παράδειγμα, ένα προϊόν μπορεί να χαρακτηριζεται από την τιμή, από το χρόνο παράδοσης, κλπ. Κάθε αγοραστής αντιστοιχίζεται στις αυτόματες διαπραγματεύσεις με έναν αριθμό πωλητών. Προτείνουμε αλγόριθμους που προσπαθούν να επιλύσουν το πρόβλημα προσέγγισης αβεβαιότητας με τελικό σκοπό τη μεγιστοποίηση της ανταμοιβής των χρηστών. Η ανταμοιβή υπολογίζεται ως το άθροισμα με τα αντίστοιχα βάρη των χαρακτηριστικών. Εστιάζουμε στην πλευρά του αγοραστή και ορίζουμε μεθοδους για τον υπολογισμό των βαρών που επηρεάζουν τη χρησιμότητα του χρήστη. Πιο συγκεκριμένα, προτείνουμε μεθόδους για την αλλαγή της στρατηγικής του αγοραστή με στόχο να προσεγγίσουμε την καλύτερη συμφωνία. Ακόμα, χρησιμοποείται ο αλγόριθμος της θεωρία του σμήνους (Particle Swarm Optimization Algorithm) ώστε μέσω της κίνησης στο Ν-διαστατο χώρο να συγκλίνουν οι πράκτορες λογισμικού στη βέλτιστη συμφωνία. Παρουσιάζεται, τέλος, ένας αριθμός από πειράματα για τις προτεινόμενες μεθόδους για να αξιολογηθεί η απόδοσή τους και να συγκριθούν τα αποτελέσματα με τη σχετική βιβλιογραφία. In this thesis, we deal with the problem of decision making in automated negotiations. We consider the case where software agents undertake the responsibility of representing their owners in such negotiations. The final aim is to provide an efficient algorithm in which software agents will act in a scenario of concurrent negotiations. Agents have no knowledge on the opponents’ characteristics. Negotiations are held for the exchange of products between buyers and sellers with specific returns. Each product is characterized by a set of issues. For example, a product could be characterized by its price, delivery time, and so on. The buyer is involved in concurrent negotiations with a number of sellers. We propose algorithms that try to solve the problem of handling the uncertainty with the final aim of maximizing the entities rewards. The reward is calculated as a weighted sum of the discussed issue values. We focus on the buyer side and define specific methodologies for defining the weights that affect the utility of the buyer. Moreover, we propose a methodology for changing the strategy of the buyer in order to reach the optimal agreement. We are based on the widely known Particle Swarm Optimization (PSO) algorithm that is implemented by software agents’ movements in N-dimensional space to reach the optimal solution. We present a number of experiments for the proposed methodologies that show their performance and we compare our results with results found in the literature

From Business Understanding to Deployment: An application of Machine Learning Algorithms to Forecast Customer Visits per Hour to a Fast-Casual Restaurant in Dublin

This research project identifies the significant factors that affects the number of customer visits to a fast-casual restaurant every hour and proceeds to develop several machine learning models to forecast customer visits. The core value proposition of fast-casual restaurants is quality food delivered at speed which means they have to prepare meals in advance of customers visit but the problem with this approach is in forecasting future demand, under estimating demand could lead to inadequate meal preparation which would leave customers unsatisfied while over estimation of demand could lead to wastage especially with restaurants having to comply with food safety regulations whereby heated food not consumed within 90 minutes has to be discarded. Hourly forecasting of demand as opposed to monthly or even daily forecasting is important to help the manager of the fast-casual restaurant optimize resources and reduce wastage. Approaches to forecasting demand can be broadly categorized into qualitative and quantitative methods. Quantitative methods can be further divided into time series and regression-based methods. The regression-based approach which is used for this study enabled the researcher to gather data on several factors hypothesized to have an impact on the number of customer visits to the fast-casual restaurant every hour, carry out an experiment to test for the significance of these factors and to develop several predictive machine learning models capable of predicting the number of customer visits every hour. The results of the experiments carried out shows that hour, day, public holidays, temperature, humidity, rain and windspeed are significant factors in predicting the number of hourly customer visits. Multiple linear regression, regression tree, random forest and gradient boosting machine learning algorithms were also trained to predict the number of customer visits with the Gradient boosting algorithm achieving the lowest Mean Absolute Percentage Error(MAPE) of 18.82%

An Automated Negotiation System for eCommerce Store Owners to Enable Flexible Product Pricing

If a store owner wishes to sell a product online, they traditionally have two options for deciding on a price. They can sell the product at a fixesd price like the products sold on sites like Amazon, or they can put the product in an auction and let demand from customers drive the final sales price like the products sold on sites like eBay. Both options have their pros and cons. An alternative option for deciding on a final sales price for the product is to enable negotiation on the product. With this, there is a dynamic nature to the price; each customer can negotiate with the store owner on the price which allows the final sales price to both change over time and on a customer by customer basis. The issue with enabling negotiation in the context of eCommerce is the time investment needed from the store owner. A store owner cannot negotiate every time an offer comes in from a potential customer, the potential time investment would not be acceptable. Using software agents to automate the process of negotiation for the seller is a potential solution to enabling negotiation in eCommerce for store owners. In this research, a system such as the one just described is developed in a way that mirrors real life negotiations more closely and after evaluation, is found to be a potential solution for the enabling of negotiation in eCommerce

Automatic Service Agreement Negotiators in Open Commerce Environments

There is a steady shift in e‑commerce from goods to services that must be provisioned according to service agreements. This study focuses on software frameworks to develop automated negotiators in open commerce environments. Analysis of the litera‑ ture on automated negotiation and typical case studies led to a catalog of 16 objective requirements and a conceptual model that was used to compare 11 state-of-the-art software frameworks. None of them was well suited for negotiating service agreements in open commerce environments. This motivated work on a reference architecture that provides the foundations to develop negotiation systems that address the previous requirements. A software framework was devised to validate the proposal by means of case studies. The study contributes to the fields of requirements engineering and software design, and is expected to support future efforts of practitioners and researchers because its findings bridge the gap among the existing automated negotiation techniques and lay the founda‑ tions for developing new software frameworksMinisterio de Educación y Ciencia TIN2006–00472Ministerio de Ciencia e Innovación TIN2009–07366Junta de Andalucía P07-TIC-2533 (Isabel)Ministerio de Educación y Ciencia TIN2007–64119Junta de Andalucía P07-TIC-02602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008–04718-

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