The Learning of an Opponent's Approximate Preferences in Bilateral Automated Negotiation

Abstract

Autonomous agents can negotiate on behalf of buyers and sellers to make a contract in the e-marketplace. In bilateral negotiation, they need to find a joint agreement by satisfying each other. That is, an agent should learn its opponent's preferences. However, the agent has limited time to find an agreement while trying to protect its payoffs by keeping its preferences private. In doing so, generating offers with incomplete information about the opponent's preferences is a complex process and, therefore, learning these preferences in a short time can assist the agent to generate proper offers. In this paper, we have developed an incremental on-line learning approach by using a hybrid soft-computing technique to learn the opponent's preferences. In our learning approach, first, the size of possible preferences is reduced by encoding the uncertain preferences into a series of fuzzy membership functions. Then, a simplified genetic algorithm is used to search the best fuzzy preferences that articulate the opponent's intention. Experimental results showed that our learning approach can estimate the opponent's preferences effectively. Moreover, results indicate that agents which use the proposed learning approach not only have more chances to reach agreements but also will be able to find agreements with greater joint utility.

Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

“…Minimize negotiation cost [7][8][9]11,50,72,73,90,103,113,137,143,146,149,151,153,155,160,165,166,183,184,188,[205][206][207] In general, it costs time and resources to negotiate. As a consequence, (early) agreements are often preferred over not reaching an agreement.…”

“…Taking advantage of this information to learn aspects of the opponent is called opponent modeling. 1 Having a good opponent model is a key factor in improving the quality of the negotiation outcome and can further increase the benefits of automated negotiation, including the following: reaching win-win agreements [90,123,206]; minimizing negotiation cost by avoiding non-agreement [151,153,183,184]; and finally, avoiding exploitation by adapting to the opponent's behavior during the negotiation [57,85,199]. Experiments have shown that by employing opponent models, automated agents can reach more efficient outcomes than human negotiators [22,124,149].…”

Predicting the Performance of Opponent Models in Automated Negotiation

“…For example Jazayeriy et al [7] introduce such measures for the learning error of issue weights. We have incorporated these measures in our method, and we also apply the same measures to quantify the similarity between two full bid spaces.…”

“…Ranking distance of issue weights [6] Ranking distance between real and estimated issue weights. Average difference between issue weights [7] Average difference between the real and estimated issue weights.…”

“…Other authors prefer to use accuracy measures to evaluate their model (for example [4]- [7]). The main advantage of this approach is that it directly assesses the quality of a model, independent of other factors such as bidding strategy or acceptance strategy.…”