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

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

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

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%

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

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

What to bid and when to stop

Negotiation is an important activity in human society, and is studied by various disciplines, ranging from economics and game theory, to electronic commerce, social psychology, and artificial intelligence. Traditionally, negotiation is a necessary, but also time-consuming and expensive activity. Therefore, in the last decades there has been a large interest in the automation of negotiation, for example in the setting of e-commerce. This interest is fueled by the promise of automated agents eventually being able to negotiate on behalf of human negotiators.Every year, automated negotiation agents are improving in various ways, and there is now a large body of negotiation strategies available, all with their unique strengths and weaknesses. For example, some agents are able to predict the opponent's preferences very well, while others focus more on having a sophisticated bidding strategy. The problem however, is that there is little incremental improvement in agent design, as the agents are tested in varying negotiation settings, using a diverse set of performance measures. This makes it very difficult to meaningfully compare the agents, let alone their underlying techniques. As a result, we lack a reliable way to pinpoint the most effective components in a negotiating agent.There are two major advantages of distinguishing between the different components of a negotiating agent's strategy: first, it allows the study of the behavior and performance of the components in isolation. For example, it becomes possible to compare the preference learning component of all agents, and to identify the best among them. Second, we can proceed to mix and match different components to create new negotiation strategies., e.g.: replacing the preference learning technique of an agent and then examining whether this makes a difference. Such a procedure enables us to combine the individual components to systematically explore the space of possible negotiation strategies.To develop a compositional approach to evaluate and combine the components, we identify structure in most agent designs by introducing the BOA architecture, in which we can develop and integrate the different components of a negotiating agent. We identify three main components of a general negotiation strategy; namely a bidding strategy (B), possibly an opponent model (O), and an acceptance strategy (A). The bidding strategy considers what concessions it deems appropriate given its own preferences, and takes the opponent into account by using an opponent model. The acceptance strategy decides whether offers proposed by the opponent should be accepted.The BOA architecture is integrated into a generic negotiation environment called Genius, which is a software environment for designing and evaluating negotiation strategies. To explore the negotiation strategy space of the negotiation research community, we amend the Genius repository with various existing agents and scenarios from literature. Additionally, we organize a yearly international negotiation competition (ANAC) to harvest even more strategies and scenarios. ANAC also acts as an evaluation tool for negotiation strategies, and encourages the design of negotiation strategies and scenarios.We re-implement agents from literature and ANAC and decouple them to fit into the BOA architecture without introducing any changes in their behavior. For each of the three components, we manage to find and analyze the best ones for specific cases, as described below. We show that the BOA framework leads to significant improvements in agent design by wining ANAC 2013, which had 19 participating teams from 8 international institutions, with an agent that is designed using the BOA framework and is informed by a preliminary analysis of the different components.In every negotiation, one of the negotiating parties must accept an offer to reach an agreement. Therefore, it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When contemplating whether to accept an offer, the agent is faced with the acceptance dilemma: accepting the offer may be suboptimal, as better offers may still be presented before time runs out. On the other hand, accepting too late may prevent an agreement from being reached, resulting in a break off with no gain for either party. We classify and compare state-of-the-art generic acceptance conditions. We propose new acceptance strategies and we demonstrate that they outperform the other conditions. We also provide insight into why some conditions work better than others and investigate correlations between the properties of the negotiation scenario and the efficacy of acceptance conditions.Later, we adopt a more principled approach by applying optimal stopping theory to calculate the optimal decision on the acceptance of an offer. We approach the decision of whether to accept as a sequential decision problem, by modeling the bids received as a stochastic process. We determine the optimal acceptance policies for particular opponent classes and we present an approach to estimate the expected range of offers when the type of opponent is unknown. We show that the proposed approach is able to find the optimal time to accept, and improves upon all existing acceptance strategies.Another principal component of a negotiating agent's strategy is its ability to take the opponent's preferences into account. The quality of an opponent model can be measured in two different ways. One is to use the agent's performance as a benchmark for the model's quality. We evaluate and compare the performance of a selection of state-of-the-art opponent modeling techniques in negotiation. We provide an overview of the factors influencing the quality of a model and we analyze how the performance of opponent models depends on the negotiation setting. We identify a class of simple and surprisingly effective opponent modeling techniques that did not receive much previous attention in literature.The other way to measure the quality of an opponent model is to directly evaluate its accuracy by using similarity measures. We review all methods to measure the accuracy of an opponent model and we then analyze how changes in accuracy translate into performance differences. Moreover, we pinpoint the best predictors for good performance. This leads to new insights concerning how to construct an opponent model, and what we need to measure when optimizing performance.Finally, we take two different approaches to gain more insight into effective bidding strategies. We present a new classification method for negotiation strategies, based on their pattern of concession making against different kinds of opponents. We apply this technique to classify some well-known negotiating strategies, and we formulate guidelines on how agents should bid in order to be successful, which gives insight into the bidding strategy space of negotiating agents. Furthermore, we apply optimal stopping theory again, this time to find the concessions that maximize utility for the bidder against particular opponents. We show there is an interesting connection between optimal bidding and optimal acceptance strategies, in the sense that they are mirrored versions of each other.Lastly, after analyzing all components separately, we put the pieces back together again. We take all BOA components accumulated so far, including the best ones, and combine them all together to explore the space of negotiation strategies.We compute the contribution of each component to the overall negotiation result, and we study the interaction between components. We find that combining the best agent components indeed makes the strongest agents. This shows that the component-based view of the BOA architecture not only provides a useful basis for developing negotiating agents but also provides a useful analytical tool. By varying the BOA components we are able to demonstrate the contribution of each component to the negotiation result, and thus analyze the significance of each. The bidding strategy is by far the most important to consider, followed by the acceptance conditions and finally followed by the opponent model.Our results validate the analytical approach of the BOA framework to first optimize the individual components, and then to recombine them into a negotiating agent

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

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