Hierarchical Psychologically Inspired Planning for Human-Robot Interaction Tasks

This paper presents a new algorithm for hierarchical case-based behavior planning in a coalition of agents – HierMAP. The considered algorithm, in contrast to the well-known planners HEART, PANDA, and others, is intended primarily for use in multi-agent tasks. For this, the possibility of dynamically distributing agent roles with different functionalities was realized. The use of a psychologically plausible approach to the representation of the knowledge by agents using a semiotic network allows applying HierMAP in groups in which people participate as one of the actors. Thus, the algorithm allows us to represent solutions of collaborative problems, forming human-interpretable results at each planning step. Another advantage of the proposed method is the ability to save and reuse experience of planning – expansion in the field of case-based planning. Such extension makes it possible to consider information about the success/ failure of interaction with other members of the coalition. Presenting precedents as a special part of the agent’s memory (semantic network on meanings) allows to significantly reduce the planning time for a similar class of tasks. The paper deals with smart relocation tasks in the environment. A comparison is made with the main hierarchical planners widely used at present.