A Multi-Tier Negotiation Protocol for Logistics Service Chains

Logistics service chains are characterized by multiple service providers contributing to the provision of a composite logistics service to a customer. In particular, various contractual dependencies exist across service chain levels. The object of our research is resource allocation which has to consider these dependencies to avoid overcommitment and overpurchasing. We propose a multi-tier negotiation protocol for solving this problem. The proposed artifact is developed from an interaction protocol engineering perspective in accordance with the design science paradigm. First evaluation experiments show that the protocol prevents overcommitments and overpurchasing, leading to higher expected profits for logistics service providers

Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent’s confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach—conducting multiple multi-issue negotiations—with its peers. Otherwise, it uses a pipelined approach—conducting multiple single-issue negotiations—with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent’s confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness

Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments

We propose an adaptive 1-to-many negotiation strategy for multiagent coalition formation in complex environments that are dynamic, uncertain, and real-time. Our strategy deals with how to assign multiple issues to a set of concurrent negotiations based on an initiating agent’s confidence in its profiling of its peer agents. When an agent is confident, it uses a packaged approach—conducting multiple multi-issue negotiations—with its peers. Otherwise, it uses a pipelined approach—conducting multiple single-issue negotiations—with its peers. The initiating agent is also capable of using both approaches in a hybrid, dealing with a mixed group of responding peers. An agent’s confidence in its profile or view of another agent is crucial, and that depends on the environment in which the agents operate. To evaluate the proposed strategy, we use a coalition formation framework in a complex environment. Results show that the proposed strategy outperforms the purely pipelined strategy and the purely packaged strategy in both efficiency and effectiveness

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

Concurrent bilateral negotiation for open e-markets: The Conan strategy

We develop a novel strategy that supports software agents to make decisions on how to negotiate for a resource in open and dynamic e-markets. Although existing negotiation strategies offer a number of sophisticated features, including modelling an opponent and negotiating with many opponents simultaneously, they abstract away from the dynamicity of the market and the model that the agent holds for itself in terms of ongoing negotiations, thus ignoring information that increases an agent’s utility. Our proposed strategy COncurrent Negotiating AgeNts (Conan) considers a weighted combination of modelling the market environment and the progress of concurrent negotiations in which the agent partakes. We conduct extensive experiments to evaluate the strategy’s performance in various settings where different opponents from the literature provide a competitive market. Our experiments provide statistically significant results showing how Conan outperforms the state-of-the-art in terms of the utility gained during negotiations

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

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 robu

Practical strategies for agent-based negotiation in complex environments

Agent-based negotiation, whereby the negotiation is automated by software programs, can be applied to many different negotiation situations, including negotiations between friends, businesses or countries. A key benefit of agent-based negotiation over human negotiation is that it can be used to negotiate effectively in complex negotiation environments, which consist of multiple negotiation issues, time constraints, and multiple unknown opponents. While automated negotiation has been an active area of research in the past twenty years, existing work has a number of limitations. Specifically, most of the existing literature has considered time constraints in terms of the number of rounds of negotiation that take place. In contrast, in this work we consider time constraints which are based on the amount of time that has elapsed. This requires a different approach, since the time spent computing the next action has an effect on the utility of the outcome, whereas the actual number of offers exchanged does not. In addition to these time constraints, in the complex negotiation environments which we consider, there are multiple negotiation issues, and we assume that the opponents’ preferences over these issues and the behaviour of those opponents are unknown. Finally, in our environment there can be concurrent negotiations between many participants.Against this background, in this thesis we present the design of a range of practical negotiation strategies, the most advanced of which uses Gaussian process regression to coordinate its concession against its various opponents, whilst considering the behaviour of those opponents and the time constraints. In more detail, the strategy uses observations of the offers made by each opponent to predict the future concession of that opponent. By considering the discounting factor, it predicts the future time which maximises the utility of the offers, and we then use this in setting our rate of concession.Furthermore, we evaluate the negotiation agents that we have developed, which use our strategies, and show that, particularly in the more challenging scenarios, our most advanced strategy outperforms other state-of-the-art agents from the Automated Negotiating Agent Competition, which provides an international benchmark for this work. In more detail, our results show that, in one-to-one negotiation, in the highly discounted scenarios, our agent reaches outcomes which, on average, are 2.3% higher than those of the next best agent. Furthermore, using empirical game theoretic analysis we show the robustness of our strategy in a variety of tournament settings. This analysis shows that, in the highly discounted scenarios, no agent can benefit by choosing a different strategy (taken from the top four strategies in that setting) than ours. Finally, in the many-to-many negotiations, we show how our strategy is particularly effective in highly competitive scenarios, where it outperforms the state-of-the-art many-to-many negotiation strategy by up to 45%

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

Practical strategies for agent-based negotiation in complex environments

Agent-based negotiation, whereby the negotiation is automated by software programs, can be applied to many different negotiation situations, including negotiations between friends, businesses or countries. A key benefit of agent-based negotiation over human negotiation is that it can be used to negotiate effectively in complex negotiation environments, which consist of multiple negotiation issues, time constraints, and multiple unknown opponents. While automated negotiation has been an active area of research in the past twenty years, existing work has a number of limitations. Specifically, most of the existing literature has considered time constraints in terms of the number of rounds of negotiation that take place. In contrast, in this work we consider time constraints which are based on the amount of time that has elapsed. This requires a different approach, since the time spent computing the next action has an effect on the utility of the outcome, whereas the actual number of offers exchanged does not. In addition to these time constraints, in the complex negotiation environments which we consider, there are multiple negotiation issues, and we assume that the opponents’ preferences over these issues and the behaviour of those opponents are unknown. Finally, in our environment there can be concurrent negotiations between many participants. Against this background, in this thesis we present the design of a range of practical negotiation strategies, the most advanced of which uses Gaussian process regression to coordinate its concession against its various opponents, whilst considering the behaviour of those opponents and the time constraints. In more detail, the strategy uses observations of the offers made by each opponent to predict the future concession of that opponent. By considering the discounting factor, it predicts the future time which maximises the utility of the offers, and we then use this in setting our rate of concession. Furthermore, we evaluate the negotiation agents that we have developed, which use our strategies, and show that, particularly in the more challenging scenarios, our most advanced strategy outperforms other state-of-the-art agents from the Automated Negotiating Agent Competition, which provides an international benchmark for this work. In more detail, our results show that, in one-to-one negotiation, in the highly discounted scenarios, our agent reaches outcomes which, on average, are 2.3% higher than those of the next best agent. Furthermore, using empirical game theoretic analysis we show the robustness of our strategy in a variety of tournament settings. This analysis shows that, in the highly discounted scenarios, no agent can benefit by choosing a different strategy (taken from the top four strategies in that setting) than ours. Finally, in the many-to-many negotiations, we show how our strategy is particularly effective in highly competitive scenarios, where it outperforms the state-of-the-art many-to-many negotiation strategy by up to 45%.EThOS - Electronic Theses Online ServiceGBUnited Kingdo