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Everyday life unfolds in both space and time, with our spatial experiences playing a central role in our interactions with the world. To grasp human cognition, it s essential to understand how we perceive spatial relationships and tackle spatio-temporal challenges. Over the past few decades, research in spatial cognition has made significant strides, particularly in developing computational methods for knowledge representation and reasoning. This special issue explores various approaches to formalizing, implementing, and automating solutions for spatial problems. In this introduction, we provide a current literature review to contextualize the three contributions featured in this issue.

This paper introduces UJI-Butler, an innovative multi-robot framework that blends symbolic and non-symbolic artificial intelligence methods. Unlike previous systems, UJI-Butler integrates large language models (LLMs) with a knowledge base akin to RAG-based systems, while imposing logical reasoning on LLM-generated results. It facilitates multi-modal interaction with human users through speech, sign language, and physical interaction, fostering a human-in-the-loop learning paradigm. By acquiring new knowledge through verbal communication and mastering manipulation skills via human-lead-through programming, UJI-Butler enhances transparency and trust by incorporating human feedback during operations. Experimental results demonstrate that UJI-Butler’s combination of symbolic and non-symbolic AI offers intuitive interaction and accelerates the learning process with experience. It adeptly stores and utilizes knowledge gained from verbal communication, recognizing hand gestures for requests. Additionally, UJI-Butler successfully performs user-taught physical skills and generalizes them to varying object sizes and locations. The explicit nature of acquired knowledge enables seamless transferability to other platforms and modification by human users. The code of the whole project is available on Github, in addition, video demonstrations of the UJI-Butler system are available online in a Youtube Playlist.

Today, anyone feeling lost in a city or unsure about how to navigate can use navigation services to look up routes to where they want to go. Current research investigating these services has primarily focused on how to find an appropriate route and how to best support navigation along it, and not how routes and the maps they are presented on are perceived. What makes one route look more difficult to navigate than another? And how does experience with using navigation services and maps in daily life influence how difficult a route is perceived to be? We explored these questions in a survey study where participants rated the perceived difficulty of pedestrian routes in ten different cities. The results show that routes in more complex urban environments were perceived as more complex than routes in easier environments. At least partly, perceived difficulty seems to follow earlier conceptualizations of route complexity, but open questions remain regarding the interplay of environmental structure, route properties, and the map representation.

Introduction: Paper folding and punched hole tests are used to measure spatial abilities in humans. These abilities are relevant since they are associated with success in STEM (Science, Technology, Engineering, and Mathematics). This study addresses the challenge of teaching spatial reasoning skills using an educational videogame, the Paper Folding Reasoning Game.

Methods: The Paper Folding Reasoning Game is an interactive game which presents activities intended to help users train and understand how to fold a paper to get a specific shape (Part I) and the consequence of punching a hole on a folded paper (Part II). This educational videogame can automatically generate paper-folding-and-punched-hole questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training due to its embedded reasoning mechanisms (Part III).

Results: This manuscript presents the results of analyzing the gameplay data gathered by the Paper Folding Reasoning Game in its three parts. For Parts I and II, the data provided by 225 anonymous unique players are analyzed. For Part III (Mastermode), the data obtained from 894 gameplays by 311 anonymous unique players are analyzed. In our analysis, we found out a significant difference in performance regarding the players who trained (i.e., played Parts I and II) before playing the Mastermode (Part III) vs. the group of players who did not train. We also found a significant difference in players' performance who used the visual help (i.e., re-watch the animated sequence of paper folds) vs. the group of players who did not use it, confirming the effectiveness of the Paper Folding Reasoning Game to train paper-folding-and-punched-hole reasoning skills. Statistically significant gender differences in performance were also found.