A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce.

Author List: Oliver, Jim R.;
Journal of Management Information Systems, 1996, Volume 13, Issue 3, Page 83-112.
This paper shows how a system of artificial adaptive agents, using a genetic algorithm-based learning technique, can learn strategies that enable it to effectively participate in stylized business negotiations. The negotiation policies learned are evaluated on several dimensions including joint outcomes, nearness to the efficient frontier, and similarity to outcomes of human negotiations. The results are promising for integrating such agents into practicable electronic commerce systems. What a system might look like is discussed, as are ways in which particular classes of business negotiations could be supported or even entirely automated.
Keywords: electronic commerce; genetic algorithms; machine learning; negotiation support; software agents.
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List of Topics

#37 0.260 intelligence business discovery framework text knowledge new existing visualization based analyzing mining genetic algorithms related techniques large proposed novel artificial
#34 0.213 negotiation negotiations using potential power agreement paper bases partners ending negotiators offers visualization messaging instant audio e-marketplaces provide positions agents
#187 0.140 learning model optimal rate hand domain effort increasing curve result experts explicit strategies estimate acquire learn referral observational skills activities
#251 0.074 implementation erp enterprise systems resource planning outcomes support business associated understanding benefits implemented advice key implementing scope functional post-implementation implementations
#84 0.065 electronic markets commerce market new efficiency suppliers internet changes marketplace analysis suggests b2b marketplaces industry examine easy product making physical
#48 0.060 dimensions electronic multidimensional game transactions relative contrast channels theory sustained model predict dimension mixture evolutionary results unique traditional likely finite