Self-Optimizing Agents Using Mixed Initiative Behavior Trees

Abstract

Fast paced industry requirements call for fast and easy robot programming, especially for Small and Medium sized Enterprises (SME) that often lack robot programming experience. Even with the advancement of graphical activity representation languages such as Behaviour Trees (BTs), it can still be time consuming to program robots for new behaviors due to the shifting product specifications and the dynamic production environments. This paper presents an extension of BTs that offers more flexibility as well as higher reactivity and robustness by introducing Mixed Initiative Planning (MIP) to BTs using Dynamic Sequence Nodes (DSNs). DSNs reduce the human effort needed to design a BT as well as the number of nodes to achieve a certain task while maintaining robustness, readability, and modularity of the tree. Additionally, it introduces run-time optimization to BTs, as opposed to tree synthesis approaches that guarantee convergence but overlook performance.