A Novel Approach for Contextual Clustering and Retrieval of Behavior Trees to Enrich the Behavior of Social Intelligent Agents

Abstract

Recently, many works have been carried out to find effective ways that can allow for plausibly effective interactions of social intelligent agents (SIAs) in unpredictable environments in a reasonable time. Behavior trees (BTs) allow for knowledge to be modeled as a graph representation and provide a way for SIAs to effectively interact with the received information. BTs can store past social experiences that can then be used by SIAs to provide adequate human-like interactions when facing new social situations (query). One challenge appears when a social agent with vast past experiences—represented as a forest of BTs—tries to retrieve a similar BT to learn from in order to provide plausible interactions in the current situation in a cost-effective manner. Cognitive scripts with their inherent temporal structure can address this challenge where they can facilitate the use of contextual retrieval techniques on BTs represented as cognitive scripts. This paper introduces novel hybrid retrieval techniques that use agglomerative hierarchical clustering (H-clustering) and similarity-based algorithms: map-and-reduce and least common parent (LCP) to effectively retrieve similar BTs to a specific query BT in a reasonable time. The model groups BTs, represented as cognitive scripts, into compact clusters that can then be used to retrieve the most similar BT to a query one in real time without noticeable delay. A comparison was done between the performance of the proposed hybrid-retrieval techniques using a semi-structured dataset of cognitive scripts. The results showed that H-clustering-map-and-reduce is more cost-effective than H-clustering-LCP as it allowed for a low average retrieval time of 8 × 10−3 s compared to 3.1 s, respectively.