  Machine common sense remains a broad, potentially unbounded problem in
artificial intelligence (AI). There is a wide range of strategies that can be
employed to make progress on this challenge. This article deals with the
aspects of modeling commonsense reasoning focusing on such domain as
interpersonal interactions. The basic idea is that there are several types of
commonsense reasoning: one is manifested at the logical level of physical
actions, the other deals with the understanding of the essence of human-human
interactions. Existing approaches, based on formal logic and artificial neural
networks, allow for modeling only the first type of common sense. To model the
second type, it is vital to understand the motives and rules of human behavior.
This model is based on real-life heuristics, i.e., the rules of thumb,
developed through knowledge and experience of different generations. Such
knowledge base allows for development of an expert system with inference and
explanatory mechanisms (commonsense reasoning algorithms and personal models).
Algorithms provide tools for a situation analysis, while personal models make
it possible to identify personality traits. The system so designed should
perform the function of amplified intelligence for interactions, including
human-machine.
