Interactive graph search (IGS) has emerged as a powerful paradigm for information retrieval across diverse applications. The goal of IGS is to identify the most appropriate (i.e., deepest) node within a hierarchy for an unknown object, typically leveraging human intelligence such as crowdsourcing as the oracle. Existing IGS algorithms usually rely on reachability queries, such as "is the target node reachable from node x ?", and assume that correct answers are always available. However, in practice, answering such queries is challenging due to the requirement for domain-specific knowledge, resulting in frequent errors in the oracle's responses. As a consequence, the reachability-query-based approaches would perform poorly. In this paper, we propose a practical solution to the IGS problem, leveraging the power of large language models (LLMs) to tackle the issue of reachability queries. Specifically, we formally analyze the inherent properties of real-world hierarchies with the notion of ambiguous nodes and overlapping nodes to debunk the difficulty of reachability queries. In addition, we develop a practical oracle based on LLMs that can answer reachability queries on (near) leaf nodes accurately. Building on the LLM oracle, we propose a similarity-based upward search algorithm, namely SuS, to address the IGS problem. We further enhance SuS with layer-wise search and fast initialization techniques. We evaluate SuS on two real-world datasets against four baseline methods, and the experimental results clearly demonstrate the superiority of our solution.

The structure design of robotic arms is of great importance on completing industrial tasks successfully. This is a typical multi-task optimization problem when considering different constraints as different tasks. However, mainstream methods for multi-task optimization such as evolutionary multitasking and Multi-task MAP-Elites algorithms tend to encounter problems such as high computational cost and slow convergence when solving large-scale robotic arm tasks. To this end, this paper proposes a new framework based on the MAP-Elites algorithms for solving large-scale robot arm design tasks, called Multi-task MAP-Elites with Knowledge Transfer (MMKT). Specifically, this paper designs the group-based knowledge transfer process for large-scale task optimization in which all tasks are classified into different groups according to their similarity to generate multiple knowledge transfer areas; and knowledge transfer strategies are designed to enhance the quality of solutions with low fitness value. We test the effectiveness of the MMKT framework in planar robotic arm experiments (2000, 5000, and 10,000 tasks; 10, 15-dimensional search space). The experimental results prove that the MMKT outperforms the MME, CMA-ES, and classical ES algorithms.