Jonker and Treur propose the Agent-Based Market Place (ABMP) model [19] 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).
Lai et al. [23] propose a decentralized bilateral negotiation model where agents are allowed to propose up to k different
offers at each negotiation round. Offers are proposed from the current iso-utility curve according to a similarity mechanism
that selects the most similar offer to the last offer received from the opponent. The selected similarity heuristic is the Euclidean
distance since it is general and does not require domain-specific knowledge and information regarding the opponent’s
utility function. Results showed that the strategy is capable of reaching agreements that are very close to the Pareto Frontier.
Sanchez-Anguix et al. [36] proposed an enhancement for this strategy in environments where computational resources are
very limited and utility functions are complex. It relies on genetic algorithms to sample offers that are interesting for the
agent itself and creates new offers during the negotiation process that are interesting for both parties. Results showed that
the model is capable of obtaining statistically equivalent results to similar models that had the full iso-utility curve sampled,
while being computationally more tractable. As commented above, some of our intra-team strategies use similarity heuristics
to satisfy team members’ preferences and the opponent’s preferences.