Abstract. Route directions are instructions, primarily verbal, that explain how to get from one place to another. The current study examines several methods for assessing the quality of verbal route directions by characterizing them in terms of the number of elements (such as landmarks, segments or turns) and by subjective ratings of their goodness. Route directions for routes which were both familiar and unfamiliar to the participant were studied. Subjective ratings of the quality of route directions were reliable and consistent across individuals. More complete route directions were rated as being of higher quality. For all routes, inclusion of more segment and turn mentions were correlated with higher quality route directions. Good route descriptions for familiar versus unfamiliar routes differed in terms of the types of landmarks included.

In this paper we tackle the problem of generating natural route descriptions on the basis of input obtained from a commercially available way-finding system. Our framework and architecture incorporates the use of general principles drawn from the domain of natural language generation. Through examples we demonstrate that it is possible to bridge the gap between underlying data representations and natural sounding linguistic descriptions. The work presented contributes both to the area of natural language generation and to the improvement of way-finding system interfaces.

The term Social Navigation captures every-day behaviour used to find information, people, and places – namely through watching, following, and talking to people. We discuss how to design information spaces to allow for social navigation. We applied our ideas in a recipe recommendation system. In a follow-up user study, subjects state that social navigation adds value to the service: it provides for social affordance, and it helps turning a space into a social place. The study also reveals some unresolved design issues, such as the snowball effect where more and more users follow each other down the wrong path, and privacy issues.

Numerous cognitive studies have indicated that the form and complexity of route instructions may be as important to human navigators as the overall length of route. Almost without exception, automated navigation systems rely on computing the solution to the shortest path problem, and not the problem of finding the "simplest" path. This paper addresses the issue of finding the "simplest" paths through a network, in terms of their ease of description. We propose a "simplest" paths algorithm that is computationally as e#cient as conventional shortest paths algorithms, at least for planar graphs. An empirical study of the algorithm 's performance, based on an established cognitive model of navigation instruction complexity, revealed that the lengths of a simplest path was on average only 16% longer than the length of the corresponding shortest path. In return for marginally longer routes, the simplest path algorithm seems to o#er considerable advantages over shortest paths in terms of their ease of description. The conclusions indicate several areas for future research; in particular cognitive studies are needed to verify these initial computational results. Potentially, the simplest paths algorithm could be used to replace shortest paths algorithms in any automated system for generating human navigation instructions, including in-car navigation systems, Internet driving direction servers, and other location-based services.

In this paper we tackle the problem of generating natural route descriptions on the basis of input obtained from a commercially available way-finding system. Our framework and architecture incorporates the use of generic natural language generation techniques. Through examples we demonstrate that it is possible to bridge the gap between underlying representation and natural sounding descriptions. The work presented contributes both to the area of natural language generation and to the improvement of way-finding system interfaces.

This article presents two studies concerning the role of individual differences in searching through a spatialsemantic virtual environment. In the first study, 10 subjects searched for two topics through a spatial user interface of a semantic space. A strong positive correlation was found between associative memory (MA-1) and search performance (r # 0.855, p # 0.003), but no significant correlation was found between visual memory (MV-1) and search performance. In the second study, 12 subjects participated in a within-subject experimental design. The same spatial user interface and a simple textual user interface were used. The effects of spatial ability (VZ-2), associative memory (MA-1), and on-line experience were tested on a set of interrelated search performance scores. A statistically significant main effect of on-line experience was found, F(6, 4) # 6.213, p # 0.049, two-tailed. In particular, on-line experience has a significant effect on the recall scores with the textual interface. Individuals experienced in on-line search are more likely to have a higher recall score with the textual interface than less experienced individuals. No significant main effects were found for spatial ability and associative memory. Subjects' comments suggest a potentially complex interplay between individuals' mental models and the high-dimensional semantic model. Qualitative and process-oriented studies are, therefore, called for to reveal the complex interaction between individuals' cognitive abilities, domain knowledge, and direct manipulation skills. A recommendation is made that spatial-semantic models should be adaptable to suit individuals and tasks at various levels

We present the results of a study of spatial cognition and its

In this paper, we sketch a model of how people search for information on the World Wide Web. Our interest lies in the cognitive properties and internal representations used in the search for information. We first collected behavioral data from individuals searching for answers to specific questions on the web, and we then analyzed these data to learn what searchers were doing and thinking. One finding was that individuals focus on key nodes when recalling their searches, and that these key nodes help structure memory. A second finding was that people tend to use the same search patterns over and over, and that they recall their searches in terms of their standard patterns--- regardless of what they actually did. Overall, our results suggest that people form cognitive maps of web space in much the same the way that they form cognitive maps of physical space. Introduction When trying to use the vast resources of the World Wide Web to answer specific questions, people often face problems...

The idea of social navigation is to aid users to navigate information spaces through making the collective, aggregated, or individual actions of others visible and useful as a basis for making decisions on where to go next and what to choose. These social markers should also help in turning the navigation experience into a social and pleasurable one rather than the tedious, boring, frustrating, and sometimes even scary experience of a lonely traveler. To evaluate whether it is possible to design for social navigation, we built the food recipe system Kalas. It includes several different forms of aggregated trails of user actions and means of communication between users: recommender system functionality (recommendations computed from others ’ choices), real-time broadcasting of concurrent user activity in the interface, possibilities to comment and vote on recipes, the number of downloads per recipe, and chatting facilities. Recipe author was also included in the recipe description. Kalas was tried with 302 users during six months, and 73 of the users answered a final questionnaire. The overall impression was that users liked and acted on aggregated trails and navigated differently because of them. 18 % of the selected recipes came from the list of recommended recipes. About half of the 73 users understood that recommendations were computed from their own and

In this paper, we propose and investigate a new model for robot navigation in large unstructured environments. Current models, which depend on metric information, have to deal with inherent mechanical and sensory errors. Instead we supply the navigator with qualitative information. Our model consists of two parts, a map-maker and a navigator. Given a source and a goal, the map-maker derives a navigational path based on the topological relationships between landmarks. A navigational path is generated as a combination of "parkway" and "trajectory" paths, both of which are abstractions of the real world into topological data structures. Traversing within a parkway enables the navigator to follow landmarks that are continuously visible. Traversing on a trajectory enables the navigator to move reliably into featureless space, based on local headings formed by visible landmarks that are robust to positional and orientational errors. Reliability measures of parkway and trajectory traversals a...