The development of autonomous multi-unmanned aerial vehicles, or multi-UAVs, represents significant progress in various fields, including the military, business, and civilian sectors. As this innovative field develops, the necessity of creating models that guarantee the fundamental components of security, privacy, dependability, effectiveness, and safety is growing. Based on this observation, this research suggests a reputation-based team formation (RTF) approach that rates each UAV according to the level of services it provides. Such a system is essential for critical multi-UAV Beyond Visual Line of Sight (BVLOS) operations requiring high dependability, effectiveness, safety, and privacy protection. To validate RTF’s effectiveness, we focus on a simulation scenario where multi-UAVs are deployed to localize a target in a forest where a fire is spreading. The criticality of this operation is underscored by the need to locate the target before the fire reaches it. Our results demonstrate that RTF successfully enables cooperative UAVs to block malicious ones and significantly reduce their influence, even in the presence of multiple compromised UAVs.

This paper introduces a novel Dynamic and Partially Observable Multiagent Path-Finding (DPO-MAPF) problem and presents a multitier solution approach accordingly. Unlike traditional MAPF problems with static obstacles, DPO-MAPF involves dynamically moving obstacles that are partially observable and exhibit unpredictable behavior. Our multitier solution approach combines centralized planning with decentralized execution. In the first tier, we apply state-of-the-art centralized and offline path planning techniques to navigate around static, known obstacles (e.g., walls, buildings, mountains). In the second tier, we propose a decentralized and online conflict resolution mechanism to handle the uncertainties introduced by partially observable and dynamically moving obstacles (e.g., humans, vehicles, animals, and so on). This resolution employs a metaheuristic-based revision process guided by a consensus protocol to ensure fair and efficient path allocation among agents. Extensive simulations validate the proposed framework, demonstrating its effectiveness in finding valid solutions while ensuring fairness and adaptability in dynamic and uncertain environments.

By examining the patterns of solutions obtained for various instances, one can gain insights into the structure and behavior of combinatorial optimization (CO) problems and develop efficient algorithms for solving them. Machine learning techniques, especially Graph Neural Networks (GNNs), have shown promise in parametrizing and automating this laborious design process. The inductive bias of GNNs allows for learning solutions to mixed-integer programming (MIP) formulations of constrained CO problems with a relational representation of decision variables and constraints. The trained GNNs can be leveraged with primal heuristics to construct high-quality feasible solutions to CO problems quickly. However, current GNN-based end-to-end learning approaches have limitations for scalable training and generalization on larger-scale instances; therefore, they have been mostly evaluated over small-scale instances. Addressing this issue, our study builds on supervised learning of optimal solutions to the downscaled instances of given large-scale CO problems. We introduce several improvements on a recent GNN model for CO to generalize on instances of a larger scale than those used in training. We also propose a two-stage primal heuristic strategy based on uncertainty-quantification to automatically configure how solution search relies on the predicted decision values. Our models can generalize on 16x upscaled instances of commonly benchmarked five CO problems. Unlike the regressive performance of existing GNN-based CO approaches as the scale of problems increases, the CO pipelines using our models offer an incremental performance improvement relative to CPLEX. The proposed uncertainty-based primal heuristics provide 6-75% better optimality gap values and 45-99% better primal gap values for the 16x upscaled instances and brings immense speedup to obtain high-quality solutions. All these gains are achieved through a computationally efficient modeling approach without sacrificing solution quality.

Negotiation is pivotal for conflict resolution in human-agent interactions, where emotional and behavioral dynamics can significantly shape the outcomes.However, many existing strategies prioritize time-or behavior-based tactics and overlook the dynamic role of emotional awareness.This paper presents the Solver Agent, which integrates real-time facial expression recognition into a hybrid strategy incorporating time-and behavior-based approaches.It is deployed on a humanoid robot with multimodal interaction capabilities (speech, gestures, facial expression analysis) to dynamically refine its bidding and concession strategies based on an opponent's emotional cues and negotiation patterns.In user studies with 28 participants, the Solver Agent achieved higher agent scores, improved social welfare, and faster agreements than a baseline hybrid strategy without compromising participant satisfaction.Participants also viewed the Solver Agent as more attuned to their preferences and goals.These findings highlight that embodied emotion-aware negotiation can foster equitable and efficient collaboration, pointing to new opportunities in human-agent interaction research.

Theory-of-Mind (ToM), the ability to infer the mental states, goals, and preferences of others -is a core component of human social intelligence.In this work, we investigate whether Large Language Models (LLMs) exhibit ToM capabilities in the context of strategic interaction.We frame opponent modeling in negotiation as a grounded and interpretable ToM task, where a model must infer an agent's preferences by observing offer exchanges during the negotiation.We guide LLMs to interpret offer histories and infer latent utility representations, including issue and value weights.We conduct a comprehensive evaluation of state-of-the-art LLMs across multiple negotiation domains.Our results show that LLMs can successfully recover opponents unknown preferences and in some cases even outperform classical opponent modeling baselines, even without task-specific training.These findings offer new evidence of LLMs' emerging capacity for social reasoning and position opponent modeling as a practical benchmark for evaluating Theory-of-Mind in foundation models.

Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some factors, such as emotional states and arguments. For this purpose, we introduce an extended taxonomy of argument types capturing human speech acts during the negotiation. We propose an argument-based automated negotiating agent that can extract human arguments from a chat-based environment using a hierarchical classifier. Consequently, the proposed agent can understand the received arguments and adapt its strategy accordingly while negotiating with its human counterparts. We initially conducted human-agent negotiation experiments to construct a negotiation corpus to train our classifier. According to the experimental results, it is seen that the proposed hierarchical classifier successfully extracted the arguments from the given text. Moreover, we conducted a second experiment where we tested the performance of the designed negotiation strategy considering the human opponent’s arguments and emotions. Our results showed that the proposed agent beats the human negotiator and gains higher utility than the baseline agent.

In an age where data is a pivotal asset for businesses, the ethical acquisition and use of personal information has become increasingly more significant. Empowering data providers with greater autonomy over their personal data is more important than ever. To address this, we propose a novel negotiation-based information-sharing framework that empowers individuals to actively negotiate the terms of their data sharing, addressing privacy concerns and ethical data usage. The framework enables users to determine what personal information they share and under what conditions, fostering a more balanced and transparent data exchange process. Our system allows data consumer agents to negotiate with their human users and can operate fully automatically, with agents representing data providers negotiating based on elicited preferences and needs. We propose novel preference modeling approaches and a negotiation framework to facilitate the bilateral sharing of information and incentives between data consumers and providers. User experiments demonstrate the efficacy of our negotiation approach and the effectiveness of the proposed preference models. Empirical results validate the benefits of the proposed framework.

The awareness about healthy lifestyles is increasing, opening to personalized intelligent health coaching applications. A demand for more than mere suggestions and mechanistic interactions has driven attention to nutrition virtual coaching systems (NVC) as a bridge between human-machine interaction and recommender, informative, persuasive, and argumentation systems. NVC can rely on data-driven opaque mechanisms. Therefore, it is crucial to enable NVC to explain their doing (i.e., engaging the user in discussions (via arguments) about dietary solutions/alternatives). By doing so, transparency, user acceptance, and engagement are expected to be boosted. This study focuses on NVC agents generating personalized food recommendations based on user-specific factors such as allergies, eating habits, lifestyles, and ingredient preferences. In particular, we propose a user-agent negotiation process entailing run-time feedback mechanisms to react to both recommendations and related explanations. Lastly, the study presents the findings obtained by the experiments conducted with multi-background participants to evaluate the acceptability and effectiveness of the proposed system. The results indicate that most participants value the opportunity to provide feedback and receive explanations for recommendations. Additionally, the users are fond of receiving information tailored to their needs. Furthermore, our interactive recommendation system performed better than the corresponding traditional recommendation system in terms of effectiveness regarding the number of agreements and rounds.

Abstract This paper introduces a negotiation framework to solve the Multi-Agent Path Finding (MAPF) Problem for self-interested agents in a decentralized fashion. The framework aims to achieve a good trade-off between the privacy of the agents and the effectiveness of solutions. Accordingly, a token-based bilateral negotiation protocol and two negotiation strategies are presented. The experimental results over four different settings of the MAPF problem show that the proposed approach could find conflict-free path solutions albeit suboptimally, especially when the search space is large and high-density. In contrast, Explicit Estimation Conflict-Based Search (EECBS) struggles to find optimal solutions. Besides, deploying a sophisticated negotiation strategy that utilizes information about local density for generating alternative paths can yield remarkably better solution performance in this negotiation framework.

By examining the patterns of solutions obtained for various instances, one can gain insights into the structure and behavior of combinatorial optimization (CO) problems and develop efficient algorithms for solving them. Machine learning techniques, especially Graph Neural Networks (GNNs), have shown promise in parametrizing and automating this laborious design process. The inductive bias of GNNs allows for learning solutions to mixed-integer programming (MIP) formulations of constrained CO problems with a relational representation of decision variables and constraints. The trained GNNs can be leveraged with primal heuristics to construct high-quality feasible solutions to CO problems quickly. However, current GNN-based end-to-end learning approaches have limitations for scalable training and generalization on larger-scale instances; therefore, they have been mostly evaluated over small-scale instances. Addressing this issue, our study builds on supervised learning of optimal solutions to the downscaled instances of given large-scale CO problems. We introduce several improvements on a recent GNN model for CO to generalize on instances of a larger scale than those used in training. We also propose a two-stage primal heuristic strategy based on uncertainty-quantification to automatically configure how solution search relies on the predicted decision values. Our models can generalize on 16x upscaled instances of commonly benchmarked five CO problems. Unlike the regressive performance of existing GNN-based CO approaches as the scale of problems increases, the CO pipelines using our models offer an incremental performance improvement relative to CPLEX. The proposed uncertainty-based primal heuristics provide 6-75% better optimality gap values and 45-99% better primal gap values for the 16x upscaled instances and brings immense speedup to obtain high-quality solutions. All these gains are achieved through a computationally efficient modeling approach without sacrificing solution quality.

Human-agent negotiation has recently attracted researchers' attention due to its complex nature and potential usage in daily life scenarios. While designing intelligent negotiating agents, they mainly focus on the interaction protocol (i.e., what to exchange and how) and strategy (i.e., how to generate offers and when to accept). Apart from these components, the embodiment may implicitly influence the negotiation process and outcome. The perception of a physically embodied agent might differ from the virtually embodied one; thus, it might influence human negotiators' decisions and responses. Accordingly, this work empirically studies the effect of physical and virtual embodiment in human-agent negotiations. We designed and conducted experiments where human participants negotiate with a humanoid robot in one setting, whereas they negotiate with a virtually embodied replica of that robot in another setting. The experimental results showed that social welfare was statistically significantly higher when the negotiation was held with a virtually embodied robot rather than a physical robot. Human participants took the negotiation more seriously against physically embodied agents and made more collaborative moves in the virtual setting. Furthermore, their survey responses indicate that participants perceived our robot as more humanlike when it is physically embodied.

Abstract Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.

With the improvement of intelligent systems and robotics, social robots are becoming part of our society. To accomplish complex tasks, robots and humans may need to collaborate, and when necessary, they need to negotiate with each other. While designing such socially interacting robots, it is crucial to consider human factors such as facial expression, emotions, and body language. Since gestures play a crucial role in interaction, this article studies the effect of gestures in human–robot negotiation experiments. Additionally, it compares the performance of variants of the well-known negotiation tactics (i.e., time-based and behavior-based) in automated negotiation literature in the context of human–robot negotiations. Our experimental results support the finding in automated negotiation. That is, the robot gained higher utility when it imitates its opponent’s bidding strategy than employing a time-based negotiation strategy. When adopting a behavior-based technique, there is a statistically significant effect of gestures on the underlying negotiation process, and, therefore, on negotiation outcome.

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier's predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors.

Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p -value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p -value and large effect size in particular settings) toward reaching an agreement.

We present the results of the 2ndAnnual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year's results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted “black-box” agents in human-agent negotiation and provide a state-of-the-art benchmark for human-agent design.

We present the results of the first annual Human-Agent League of ANAC. By introducing a new human-agent negotiating platform to the research community at large, we facilitated new advancements in human-aware agents. This has succeeded in pushing the envelope in agent design, and creating a corpus of useful human-agent interaction data. Our results indicate a variety of agents were submitted, and that their varying strategies had distinct outcomes on many measures of the negotiation. These agents approach the problems endemic to human negotiation, including user modeling, bidding strategy, rapport techniques, and strategic bargaining. Some agents employed advanced tactics in information gathering or emotional displays and gained more points than their opponents, while others were considered more "likeable" by their partners.

The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work andto challenge itself. The benchmark problems and evaluation results and the protocols and strategies developed are available to the wider research community.

Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The workshop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013.

We investigate Web service composition as a planning problem and use the input-output parameter relations in order to select the constituent services that make up the composite service. Furthermore, we make use of ontological information between the input-output parameters such that a more specific concept can be used instead of a general concept to make the process more flexible. Our proposed approach is based on constructing a dependency graph including the service parameters and Web services themselves. By using this dependency graph, we perform backward chaining starting to search from the desired output parameters, which is in fact the goal, to the available input parameters. In addition to using semantic information through the search, our approach considers non-functional attributes of the services such as service quality. Considering the quality measures, we find the constituent services by making use of depth first search. After finding the required services, our algorithm generates a plan that shows the execution order of each service.