Learning communicative actions of conflicting human agents

One of the main problems to be solved while assisting inter-human conflict resolution is how to reuse the previous experience with similar agents. A machine learning technique for handling scenarios of interaction between conflicting human agents is proposed. Scenarios are represented by directed graphs with labelled vertices (for communicative actions) and arcs (for temporal and causal relationships between these actions and their parameters). For illustrative purposes, classification of a scenario is computed by comparing partial matching of its graph with graphs of positive and negative examples. Nearest Neighbour learning is followed by the JSM-based learning which minimised the number of false negatives and takes advantage of a more accurate way of matching sequences of communicative actions. Developed scenario representation and comparative analysis techniques are applied to the classification of textual customer complaints. It is shown that analysing the structure of communicative actions without context information is frequently sufficient to estimate complaint validity. Therefore, being domain-independent, proposed machine learning technique is a good compliment to a wide range of customer relation management applications where formal treatment of inter-human interactions is required in a decision-support mode.