Acceptance conditions in automated negotiation

In every negotiation with a deadline, one of the negotiating parties has to accept an offer to avoid a break off. A break off is usually an undesirable outcome for both parties, therefore it is important that a negotiator employs a proficient mechanism to decide under which conditions to accept. When designing such conditions one is faced with the acceptance dilemma: accepting the current offer may be suboptimal, as better offers may still be presented. 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. Motivated by the challenges of bilateral negotiations between automated agents and by the results and insights of the automated negotiating agents competition (ANAC), we classify and compare state-of-the-art generic acceptance conditions. We focus on decoupled acceptance conditions, i.e. conditions that do not depend on the bidding strategy that is used. We performed extensive experiments to compare the performance of acceptance conditions in combination with a broad range of bidding strategies and negotiation domains. Furthermore we propose new acceptance conditions and we demonstrate that they outperform the other conditions that we study. In particular, it is shown that they outperform the standard acceptance condition of comparing the current offer with the offer the agent is ready to send out. We also provide insight in to why some conditions work better than others and investigate correlations between the properties of the negotiation environment and the efficacy of acceptance condition

The significance of bidding, accepting and opponent modeling in automated negotiation

Given the growing interest in automated negotiation, the search for effective strategies has produced a variety of different negotiation agents. Despite their diversity, there is a common structure to their design. A negotiation agent comprises three key components: the bidding strategy, the opponent model and the acceptance criteria. We show that this three-component view of a negotiating architecture not only provides a useful basis for developing such agents but also provides a useful analytical tool. By combining these components in varying ways, we are able to demonstrate the contribution of each component to the overall negotiation result, and thus determine the key contributing components. Moreover, we are able to study the interaction between components and present detailed interaction effects. Furthermore, we find that the bidding strategy in particular is of critical importance to the negotiator's success and far exceeds the importance of opponent preference modeling techniques. Our results contribute to the shaping of a research agenda for negotiating agent design by providing guidelines on how agent developers can spend their time most effectively

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

On integrating Theory of Mind in context-aware negotiation agents

Theory of Mind (ToM) is the ability of an agent to represent mental states of other agents including their intentions, desires, goals, models, beliefs, how the environment makes an impact on those beliefs, and the beliefs those agents may have about the beliefs others have about themselves. Integrating arti cial ToM in automated negotiations can provide software agents a key competitive advantage. In this work, we propose integrating ToM into context-aware negotiation agents using Bayesian inference to update each agent's beliefs. Beliefs are about the necessity and risk of the opponent considering hypothesis about how it takes into account contextual variables. A systematic hierarchical approach to combine ToM with using evidence from the opponent actions in an unfolding negotiation episode is proposed. Alternative contextual scenarios are used to argue in favor of incorporating di erent levels of reasoning and modeling the strategic behavior of an opponent.Sociedad Argentina de Informática e Investigación Operativ

On integrating Theory of Mind in context-aware negotiation agents

Theory of Mind (ToM) is the ability of an agent to represent mental states of other agents including their intentions, desires, goals, models, beliefs, how the environment makes an impact on those beliefs, and the beliefs those agents may have about the beliefs others have about themselves. Integrating arti cial ToM in automated negotiations can provide software agents a key competitive advantage. In this work, we propose integrating ToM into context-aware negotiation agents using Bayesian inference to update each agent's beliefs. Beliefs are about the necessity and risk of the opponent considering hypothesis about how it takes into account contextual variables. A systematic hierarchical approach to combine ToM with using evidence from the opponent actions in an unfolding negotiation episode is proposed. Alternative contextual scenarios are used to argue in favor of incorporating di erent levels of reasoning and modeling the strategic behavior of an opponent.Sociedad Argentina de Informática e Investigación Operativ

An Evolutionary Approach for Learning Opponent's Deadline and Reserve Points in Multi-Issue Negotiation

The efficiency of automated multi-issue negotiation depends on the available information about the opponent. In a competitive negotiation environment, agents do not reveal their parameters to their opponents in order to avoid exploitation. Several researchers have argued that an agent's optimal strategy can be determined using the opponent's deadline and reserve points. In this paper, we propose a new learning agent, so-called Evolutionary Learning Agent (ELA), able to estimate its opponent's deadline and reserve points in bilateral multi-issue negotiation based on opponent's counter-offers (without any additional extra information). ELA reduces the learning problem to a system of non-linear equations and uses an evolutionary algorithm based on the elitism aspect to solve it. Experimental study shows that our learning agent outperforms others agents by improving its outcome in term of average and joint utility

Generating Pareto-Optimal Offers in Bilateral Automated Negotiation with One-Side Uncertain Importance Weights

Pareto efficiency is a seminal condition in the bargaining problem which leads autonomous agents to a Nash-equilibrium. This paper investigates the problem of the generating Pareto-optimal offers in bilateral multi-issues negotiation where an agent has incomplete information and the other one has perfect information. To this end, at first, the bilateral negotiation is modeled by split the pie game and alternating-offer protocol. Then, the properties of the Pareto-optimal offers are investigated. Finally, based on properties of the Pareto-optimal offers, an algorithmic solution for generating near-optimal offers with incomplete information is presented. The agent with incomplete information generates near-optimal offers in O(n łog n). The results indicate that, in the early rounds of the negotiation, the agent with incomplete information can generate near-optimal offers, but as time passes the agent can learn its opponents preferences and generate Pareto-optimal offers. The empirical analysis also indicates that the proposed algorithm outperform the smart random trade-offs (SRT) algorithm