In this article we study the impact of the negotiation environment on the performance of several intra-team strategies (team dynamics) for agent-based negotiation teams that negotiate with an opponent. An agent-based negotiation team is a group of agents that joins together as a party because they share common interests in the negotiation at hand. It is experimentally shown how negotiation environment conditions like the deadline of both parties, the concession speed of the opponent, similarity among team members, and team size affect performance metrics like the minimum utility of team members, the average utility of team members, and the number of negotiation rounds. Our goal is identifying which intra-team strategies work better in different environmental conditions in order to provide useful knowledge for team members to select appropriate intra-team strategies according to environmental conditions.
Jonker and Treur propose the Agent-Based Market Place (ABMP) model @cite_23 where agents, engage in bilateral negotiations. ABMP is a negotiation model where proposed bids are concessions to previous bids. The amount of concession is regulated by the concession factor (i.e., reservation utility), the negotiation speed, the acceptable utility gap (maximal difference between the target utility and the utility of an offer that is acceptable), and the impatience factor (which governs the probability of the agent leaving the negotiation process).
@cite_23
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In sequential decision-making problems under uncertainty, an agent makes decisions, one after another, considering the current state of the environment where she evolves. In most work, the environment the agent evolves in is assumed to be stationary, i.e., its dynamics do not change over time. However, the stationarity hypothesis can be invalid if, for instance, exogenous events can occur. In this document, we are interested in sequential decision-making in non-stationary environments. We propose a new model named HS3MDP, allowing us to represent non-stationary problems whose dynamics evolve among a finite set of contexts. In order to efficiently solve those problems, we adapt the POMCP algorithm to HS3MDPs. We also present RLCD with SCD, a new method to learn the dynamics of the environments, without knowing a priori the number of contexts. We then explore the field of argumentation problems, where few works consider sequential decision-making. We address two types of problems: stochastic debates (APS ) and mediation problems with non-stationary agents (DMP). In this work, we present a model formalizing APS and allowing us to transform them into an MOMDP in order to optimize the sequence of arguments of one agent in the debate. We then extend this model to DMPs to allow a mediator to strategically organize speak-turns in a debate. A computational prototype of negotiation behavior is presented where the following occurs: (1) agents employ different concession matching tactics; (2) agents are unaware of opponent preferences; (3) agents incur a cost for delaying settlements; (4) agents vary in terms of goal difficulty and initial offer magnitude; and (5) demands and counter-offers are made and evaluated based on the opponent's degree of concession matching. This research explores the impact of the interaction of different agent behaviors on the negotiation process and the outcome of the negotiation. Simulation experiments show that the prototype is able to manifest fundamental patterns and confirms the effectiveness of classical negotiation and mediation strategies, such as ambitious goals and aggressive concession matching tactics. The model reveals some counterintuitive patterns that may shed a new perspective on the effects of time constraints and information availability. This paper presents a decentralized model that allows self-interested agents to reach "win-win" agreements in a multi-attribute negotiation. The model is based on an alternating-offer protocol. In each period, the proposing agent is allowed to make a limited number of offers. The responding agent can select the best out of these offers. In the case of rejection, agents exchange their roles and the negotiation proceeds to the next period. To make counteroffers, an agent first uses the heuristic of choosing the offer on an indifference (or "iso-utility") curve surface that is closest to the best offer made by the opponent in the previous period, and then taking this offer as the seed, chooses several other offers randomly in a specified neighborhood of this seed offer. Experimental analysis shows agents can reach near Pareto optimal agreements in quite general situations following the model where agents may have complex preferences on the attributes and incomplete information. This model does not require the presence of a mediator. There are two ways of handling bilateral multi-issue negotiations -- one is to negotiate all the issues together, and the other is to negotiate them one by one. The order in which issues are negotiated in issue-by-issue negotiation is specified by the agenda, which can be defined in two ways. One way is to decide it exogenously, i.e., before negotiation begins. The other way is to let the players decide which issue they will negotiate next, during the process of negotiation, i.e., the agenda is determined endogenously. Against this background, this paper studies the effect of combining the exogenous and endogenous agendas on the players' utilities. More specifically, we determine whether, decomposing a set of N issues into k stages (for 1 ≤ k ≤ N), determining the issues to be negotiated at each stage exogenously, and negotiating each stage sequentially using an endogenous agenda can improve an agent's utility relative to the utility it gets if the agenda for all the N issues is defined endogenously. For each agent, we find the expected utility for each value of k between 1 and N. The value of k that gives an agent maximum utility is its optimal number of stages.Our study shows that, in some negotiation scenarios, the optimal value of k is identical for the two players, and is greater than one. In other words, in some negotiation scenarios, both the agents can improve their utilities by using the k-stage negotiation relative to the single stage negotiation. However, since the players have incomplete information about the negotiation parameters, they cannot identify such scenarios. We therefore present an extended alternating offers protocol, that allows the agents to identify such scenarios through a mediator, thereby resulting in improved utility to both the agents.