This paper presents the primary research challenges and key findings from the 15th International Automated Negotiating Agents Competition (ANAC 2025), one of the official competitions of IJCAI 2025. We focus on two critical domains: multi-deal negotiations and the development of agents capable of concurrent negotiation within complex supply chain management environments. Furthermore, this work analyzes the results of the competition and outlines strategic directions for future iterations.

This paper presents the primary research challenges and key findings from the 15th International Automated Negotiating Agents Competition (ANAC 2025), one of the official competitions of IJCAI 2025. We focus on two critical domains: multi-deal negotiations and the development of agents capable of concurrent negotiation within complex supply chain management environments. Furthermore, this work analyzes the results of the competition and outlines strategic directions for future iterations.

Estimating the outcome of a negotiation before it is finished allows a party to take effective actions, e.g., exploring outside options, or reporting progress to a human user. However, estimating the outcome is difficult as many (uncertain) factors affect the course of a negotiation. Accordingly, this paper presents a method for predicting the outcome of ongoing bilateral negotiations called PrONeg. We predict the future trajectories of an agent’s own bids and its opponent’s bids using time series forecasting methods. These forecasts are used to determine the agent’s outcome utility distribution, along with the probability of reaching an agreement by the end of the negotiation. Finally, we predict the most likely outcome of the negotiation by combining the outcome utility distribution with preference information available in the negotiation scenario. Our experiments show that Gaussian processes perform best in most settings, including balancing predicting true breakoffs without misclassifying agreements. With its ability to predict the outcome of a negotiation, PrONeg can potentially serve as a negotiation support system in hybrid negotiations.

This paper introduces the main research challenges and results of the 15th International Automated Negotiating Agents Competition (ANAC 2024). The main challenges addressed are learning the reservation value in bilateral negotiation and designing a factory agent employing concurrent negotiation in supply chain management. Additionally, it outlines the future directions for the competition.

One-to-many negotiations are widely applied in various domains, contributing to efficient resource allocation and effective decision making. This wide variety of applications also brings a wide variety of implemented protocols, terminology and utility functions, which makes it hard to compare and improve strategies using existing solutions. We introduce a meta-model of negotiations, which characterizes almost all one-to-many negotiation research, bringing a unified description of the negotiations. This meta-model allows us to identify different classes of interdependency based on utility functions. We show how existing one-to-many negotiations are related to each other, finding new insights and identifying knowledge gaps. We suggest that a general utility function framework and benchmark scenarios for one-to-many negotiations could accommodate future advancement in this field.

The complexity of automated negotiation research calls for dedicated, user-friendly research frameworks that facilitate advanced analytics, comprehensive loggers, visualization tools, and auto-generated domains and preference profiles. This paper introduces NegoLog, a platform that provides advanced and customizable analysis modules to agent developers for exhaustive performance evaluation. NegoLog introduces an automated scenario and tournament generation tool in its Web-based user interface so that the agent developers can adjust the competitiveness and complexity of the negotiations. One of the key novelties of the NegoLog is an individual assessment of preference estimation models independent of the strategies.

Negotiating multiple deals is an essential day-to-day activity for many businesses. Procurement, for instance, typically represents one of the largest expense items for businesses worldwide. Today, 95% of European businesses are still negotiating their goods and services entirely unaided by computers, which has been shown to lead to significantly less efficient outcomes, increased costs, and highly labor-intensive processes. Enabling the automation of general-purpose multi-deal negotiations would therefore have an enormous impact on the competitiveness and profitability of businesses world-wide. However, currently available algorithms are not yet capable of performing multiple complex and interdependent negotiations at the same time. This so far underexplored research challenge calls for solutions and methods beyond the state-of-the-art research in auctions, game theory, or bilateral negotiation. It requires new asynchronous negotiation strategies that can reach multiple interdependent deals, as well as novel mathematical coordination mechanisms that are able to steer pro-actively toward a desirable aggregate outcome. The challenges of multi-deal negotiation call for 1) a mathematical model and protocol for multi-deal negotiation algorithms that can strike multiple partial deals with multiple partners; 2) coordination techniques for making optimal trade-offs regarding expected agreement utility; and 3) multi-deal negotiation strategies that can provide online probability estimates of the expected outcome. Altogether, such a research endeavor would deliver the fundamental underpinnings for general-purpose multi-deal negotiation algorithms, thereby paving the way for future systems for domains ranging from procurement and energy to ethics and transportation.

Automated negotiation is a well-known mechanism for autonomous agents to reach agreements. To realize beneficial agreements quickly, it is key to employ a good bidding strategy. When a negotiating agent has a good back-up plan, i.e., a high reservation value, failing to reach an agreement is not necessarily disadvantageous. Thus, the agent can adopt a risk-seeking strategy, aiming for outcomes with a higher utilities. Accordingly, this paper develops an optimal bidding strategy called MIA-RVelous for bilateral negotiations with private reservation values. The proposed greedy algorithm finds the optimal bid sequence given the agent's beliefs about the opponent in O(n^2D) time, with D the maximum number of rounds and n the number of outcomes. The results obtained here can pave the way to realizing effective concurrent negotiations, given that concurrent negotiations can serve as a (probabilistic) backup plan.

This work presents several new and efficient algorithms that can be used by negotiating agents to explore very large outcome spaces. The proposed algorithms can search for bids close to a utility target or above a utility threshold, and for win-win outcomes. While doing so, these algorithms strike a careful balance between being rapid, accurate, diverse, and scalable, allowing agents to explore spaces with as many as 10^250 possible outcomes on very run-of-the-mill hardware. We show that our methods can be used to respond to the most common search queries employed by 87% of all agents from the Automated Negotiating Agents Competition between 2010 and 2021. Furthermore, we integrate our techniques into negotiation platform GeniusWeb in order to enable existing state-of-the-art agents (and future agents) to handle very large outcome spaces.

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.

An automated negotiating agent must take into account the preferences of its user to negotiate effectively. In practice, these preferences are not always fully known; therefore the agent needs to support user preference uncertainty. We present a general framework to tackle the problem of user preference uncertainty in automated negotiation. We model the user's preferences as a utility function that is unknown to the representative agent. The utility is parametrizable by a finite dimensional real vector. The agent possesses a prior belief on this parameter and can query the user for information. We are interested in determining which queries will most reduce uncertainty of the belief through what we call their information potential. We propose an optimization problem with the goal of finding a sequence of queries maximizing the information potential. We present an application of this framework to a special type of linear additive utilities defined on a multi issue negotiation domain. We establish optimal querying algorithms for this application, and experimentally assess the quality of their robust guarantees.

. Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in real-world applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems have brought about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field.

This work presents BIDS (Bidding using Diversified Search), an algorithm that can be used by negotiating agents to search very large outcome spaces. BIDS provides a balance between being rapid, accurate, diverse, and scalable search, allowing agents to search spaces with as many as $$10^{250}$$ possible outcomes on very run-of-the-mill hardware. We show that our algorithm can be used to respond to the three most common search queries employed by 87% of all agents from the Automated Negotiating Agents Competition. Furthermore, we validate one of our techniques by integrating it into negotiation platform GeniusWeb, to enable existing state-of-the-art agents (and future agents) to scale their use to very large outcome spaces.

Many service providers require permissions to access privacy-sensitive data that are not necessary for their core functionality. To support users’ privacy management, we propose a novel agent-based negotiation framework to negotiate privacy permissions between users and service providers using a new multi-issue alternating-offer protocol based on exchanges of partial and complete offers. Additionally, we introduce a novel approach to learning users’ preferences in negotiation and present two variants of this approach: one variant personalised to each individual user, and one personalised depending on the user’s privacy type. To evaluate them, we perform a user study with participants, using an experimental tool installed on the participants’ mobile devices. We compare the take-it-or-leave-it approach, in which users are required to accept all permissions requested by a service, to negotiation, which respects their preferences. Our results show that users share personal data 2.5 times more often when they are able to negotiate while maintaining the same level of decision regret. Moreover, negotiation can be less mentally demanding than the take-it-or-leave-it approach and it allows users to align their privacy choices with their preferences. Finally, our findings provide insight into users’ data sharing strategies to guide the future of automated and negotiable privacy management mechanisms.

This work presents the Autonomous Bidding & Coordinated Acceptance framework (ABCA): an agent-team design that allows general bilateral agents to engage in oneto-many negotiations in a setting where (possibly overlapping) deals with multiple opponents are desirable. We propose also a coordinated acceptance strategy that uses the estimated outcomes of its bilateral negotiations while deciding to accept a deal.

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.

This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches.

The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supply-chain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.

The Automated Negotiating Agents Competition (ANAC) is annually organized competition to facilitate the research on automated negotiation. This paper presents the ANAC 2017 Repeated Multilateral Negotiation League. As human negotiators do, agents are supposed to learn from their previous negotiations and improve their negotiation skills over time. Especially, when they negotiate with the same opponent on the same domain, they can adopt their negotiation strategy according to their past experiences. They can adjust their acceptance threshold or bidding strategy accordingly. In ANAC 2017, participants aimed to develop such a negotiating agent. Accordingly, this paper describes the competition settings and results with a brief description of the winner negotiation strategies.

This is an extension from a selected paper from JSAI2019. There are a number of research challenges in the field of Automated Negotiation. The Ninth International Automated Negotiating Agent Competition encourages participants to develop effective negotiating agents, which can negotiate with multiple opponents more than once. This paper discusses research challenges for such negotiations as well as presenting the competition set-up and results. The results show that winner agents mostly adopt hybrid bidding strategies that take their opponents’ preferences as well as their strategy into account.