MACHINE LEARNING FOR INTELLIGENT SUPPORT OF CONFLICT-RESOLUTION
Because negotiation and conflict resolution are complex and unstructured tasks, they need sophisticated decision support. One of the crucial characteristics of such support is systems that are capable of improving their performance, both in terms of efficiency and solution quality, by employing machine learning techniques. A framework for intelligent computer-supported conflict resolution through negotiation/mediation is presented. The model integrates Artificial Intelligence methods (case-based reasoning) and decision theoretic techniques (multi-attribute utilities) to provide enhanced conflict resolution and negotiation support in group problem solving. This model has been implemented in the PER-SUADER, a computer program which operates in the domain of resolution of labor management disputes. The PER-SUADER uses case-based reasoning (CBR) to learn from its experience. In contrast to quantitative models or expert systems that solve each problem from scratch and discard the solution at the end of problem solving, CBR retains the process and results of its computational decisions so that they can be re-used to solve future related problems. CBR is a powerful learning method since it enables a system not only to exploit previous successful decisions, thus short-cutting possibly long reasoning chains, but also to profit from previous failures by using them to recognize similar failures in advance so they can be avoided in the future. As the state of the art in DSS development advances and as DSSs support increasingly more complicated tasks, such machine learning techniques will become an indispensable part of decision support systems.
learning negotiation conflict resolution case-based reasoning failure-driven learning learning from experience multiagent planning group decision support