A Survey of Decision Support Mechanisms for Negotiation

This paper introduces a dependency analysis and a categorization of conceptualized and existing economic decision support mechanisms for negotiation. The focus of our survey is on economic decision support mechanisms, although some behavioural support mechanisms were included, to recognize the important work in that area. We categorize support mechanisms from four different aspects: (i) economic versus behavioral decision support, (ii) analytical versus strategical support, (iii) active versus passive support and (iv) implicit versus explicit support. Our survey suggests that active mechanisms would be more effective than passive ones, and that implicit mechanisms can shield the user from mathematical complexities. Furthermore, we provide a list of existing economic support mechanisms.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc

Actor-critic reinforcement learning for bidding in bilateral negotiation

Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of the coming offer based on previous offer exchanges and remaining time. Furthermore, an imitation learning approach called behavior cloning is adopted to speed up the learning process. Also, a novel reward function is introduced that does take not only the agent’s own utility but also the opponent’s utility at the end of the negotiation. The developed agent is empirically evaluated. Thus, a large number of negotiation sessions are run against a variety of opponents selected in different domains varying in size and opposition. The agent’s performance is compared with its opponents and the performance of the baseline agents negotiating with the same opponents. The empirical results show that our agent successfully negotiates against challenging opponents in different negotiation scenarios without requiring any former information about the opponent or domain in advance. Furthermore, it achieves better results than the baseline agents regarding the received utility at the end of the successful negotiations.Interactive Intelligenc

Bidding Support by the Pocket Negotiator Improves Negotiation Outcomes

This paper presents the negotiation support mechanisms provided by the Pocket Negotiator (PN) and an elaborate empirical evaluation of the economic decision support (EDS) mechanisms during the bidding phase of negotiations as provided by the PN. Some of these support mechanisms are offered actively, some passively. With passive support we mean that the user only gets that support by clicking a button, whereas active support is provided without prompting. Our results show, that PN improves negotiation outcomes, counters cognitive depletion, and encourages exploration of potential outcomes. We found that the active mechanisms were used more effectively than the passive ones and, overall, the various mechanisms were not used optimally, which opens up new avenues for research. As expected, the participants with higher negotiation skills outperformed the other groups, but still they benefited from PN support. Our experimental results show that people with enough technical skills and with some basic negotiation knowledge will benefit most from PN support. Our results also show that the cognitive depletion effect is reduced by Pocket Negotiator support. The questionnaire taken after the experiment shows that overall the participants found Pocket Negotiator easy to interact with, that it made them negotiate more quickly and that it improves their outcome. Based on our findings, we recommend to 1) provide active support mechanisms (push) to nudge users to be more effective, and 2) provide support mechanisms that shield the user from mathematical complexities.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work publicInteractive Intelligenc

Artificial Intelligence Techniques for Conflict Resolution

Conflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions.Interactive Intelligenc

Formal modelling and verification of a multi-agent negotiation approach for airline operations control

This paper proposes and evaluates a new airline disruption management strategy using multi-agent system modelling, simulation, and verification. This new strategy is based on a multi-agent negotiation protocol and is compared with three airline strategies based on established industry practices. The application concerns Airline Operations Control whose core functionality is disruption management. To evaluate the new strategy, a rule-based multi-agent system model of the AOC and crew processes has been developed. This model is used to assess the effects of multi-agent negotiation on airline performance in the context of a challenging disruption scenario. For the specific scenario considered, the multi-agent negotiation strategy outperforms the established strategies when the agents involved in the negotiation are experts. Another important contribution is that the paper presents a logic-based ontology used for formal modelling and analysis of AOC workflows.Interactive IntelligenceAerospace Transport & Operation

AhBuNe Agent: Winner of the Eleventh International Automated Negotiating Agent Competition (ANAC 2020)

The International Automated Negotiating Agent Competition introduces a new challenge each year to facilitate the research on agent-based negotiation and provide a test benchmark. ANAC 2020 addressed the problem of designing effective agents that do not know their users’ complete preferences in addition to their opponent’s negotiation strategy. Accordingly, this paper presents the negotiation strategy of the winner agent called “AhBuNe Agent”. The proposed heuristic-based bidding strategy checks whether it has sufficient orderings to reason about its complete preferences and accordingly decides whether to sacrifice some utility in return for preference elicitation. While making an offer, it uses the most-desired known outcome as a reference and modifies the content of the bid by adopting a concession-based strategy. By analyzing the content of the given ordered bids, the importance ranking of the issues is estimated. As our agent adopts a fixed time-based concession strategy and takes the estimated issue importance ranks into account, it determines to what extent the issues are to be modified. The evaluation results of the ANAC 2020 show that our agent beats the other participating agents in terms of the received individual score.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work publicInteractive Intelligenc

Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

In agent-based negotiations, it is crucial to understand the opponent’s behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent’s coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent’s coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent’s utility of the opponent’s coming offer, estimating the agent’s utility of that without using opponent-related variables, and estimating the opponent’s utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc

Symbolic knowledge extraction for explainable nutritional recommenders

Background and objective:This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. ConclusionsThanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.Interactive Intelligenc

Explanation-Based Negotiation Protocol for Nutrition Virtual Coaching

People’s awareness about the importance of healthy lifestyles is rising. This opens new possibilities for personalized intelligent health and coaching applications. In particular, there is a need for more than simple recommendations and mechanistic interactions. Recent studies have identified nutrition virtual coaching systems (NVC) as a technological solution, possibly bridging technologies such as recommender, informative, persuasive, and argumentation systems. Enabling NVC to explain recommendations and discuss (argument) dietary solutions and alternative items or behaviors is crucial to improve the transparency of these applications and enhance user acceptability and retain their engagement. This study primarily focuses on virtual agents personalizing the generation of food recipes recommendation according to users’ allergies, eating habits, lifestyles, nutritional values, etc. Although the agent would nudge the user to consume healthier food, users may tend to object in favor of tastier food. To resolve this divergence, we propose a user-agent negotiation interacting over the revision of the recommendation (via feedback and explanations) or convincing (via explainable arguments) the user of its benefits and importance. Finally, the paper presents our initial findings on the acceptability and usability of such a system obtained via tests with real users. Our preliminary experimental results show that the majority of the participants appreciate the ability to express their feedback as well as receive explanations of the recommendations, while there is still room for improvement in the persuasiveness of the explanations.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc

The 13th International Automated Negotiating Agent Competition Challenges and Results

An international competition for negotiating agents has been organized for years to facilitate research in agent-based negotiation and to encourage the design of negotiating agents that can operate in various scenarios. The 13th International Automated Negotiating Agents Competition (ANAC 2022) was held in conjunction with IJCAI2022. In ANAC2022, we had two leagues: Automated Negotiation League (ANL) and Supply Chain Management League (SCML). For the ANL, the participants designed a negotiation agent that can learn from the previous bilateral negotiation sessions it was involved in. In contrast, the research challenge was to make the right decisions to maximize the overall profit in a supply chain environment, such as determining with whom and when to negotiate. This chapter describes the overview of ANL and SCML in ANAC2022, and reports the results of each league, respectively.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Interactive Intelligenc