Thomas E. Horton

Model-based evaluation of expert cell phone menu interaction

We describe concepts to support the analysis of cell phone menu hierarchies, based on cognitive models of users and easy-to-use optimization techniques. We present an empirical study of user performance on five simple tasks of menu traversal on an example cell phone. Two of the models applied to these tasks, based on GOMS and ACT-R, give good predictions of behavior. We use the empirically supported models to create an effective evaluation and improvement process for menu hierarchies. Our work makes three main contributions: a novel and timely study of a new, very common HCI task; new versions of existing models for accurately predicting performance; and a search procedure to generate menu hierarchies that reduce traversal time, in simulation studies, by about a third.

Model-based evaluation of cell phone menu interaction

Cell phone interfaces are now ubiquitous. In this paper, we describe concepts to support the analysis of cell phone menu hierarchies. We present an empirical study of user performance on five simple tasks of menu traversal on a cell phone. Two models we tested, based on GOMS and ACT-R, give very good predictions of behavior. We use the study results to motivate an effective evaluation process for menu hierarchies. Our work makes several contributions: a novel and timely study of a new, very common HCI task; new models for accurately predicting performance; novel development tools to support such modeling; and a search procedure to generate menu hierarchies that reduce traversal time, in simulation studies, by about a third.

Characterizing tool use in an interactive drawing environment

The metaphor of tool use for describing the interaction between a human and a computer is pervasive in user interface design. The basic concept of tool use, however, is difficult to define precisely, for HCI purposes or in general. In this paper we argue that a close examination of physical tool use can improve the design of interactive software. We describe a drawing application, HabilisDraw, that incorporates some of the properties we associate with physical tools but are not commonly found in software: persistent tool objects that encapsulate behavior and information, that can be used in conjunction with one another, and that embody rich cues about their appropriate usage. Initial results from formative evaluation suggest that the approach has some promise.

Effective tool use in a habile agent

The intelligent use of tools is a general and important human competence that AI research has not yet examined in depth. Other fields have studied the topic, however, with results we can compile into a broad characterization of habile (tool-using) agents. In this paper we give an overview of research on the use of physical tools, using this information to motivate a set of requirements for building artificial habile agents. We describe the design of a habile robot, based on the Aibo platform, that can pick up a stick and use it as a tool to reach objects otherwise out of its range. We argue that analysis of activities of such tool-using agents offers an informative way to evaluate intelligence.

A tool-based interactive drawing environment

Graphical user interfaces rely heavily on the tool metaphor. In most drawing systems, for example, functions are organized as they might be on a workbench; buttons associated with drawing modes for lines or rectangles are called line-drawing or rectangle-drawing tools; etc. Despite the similarities, however, there remain many differences between software tools and physical tools. This paper gives a concise account of tool use in general, and describes a drawing application, called HabilisDraw, that relies on a detailed correspondence to physical tool behavior.

A Partial Contour Similarity-Based Approach to Visual Affordances in Habile Agents

In a typical tool use task, we can view both the relationship between the agent and the tool and the relationship between the tool and the target in terms of affordances. One set of affordances relates to the ability of the agent to manipulate the tool, while a second set of affordances relates to the ability of the agent to manipulate the target by means of the tool. In both cases, effective tool use is facilitated by the coupling of one object to another: agent-to-tool-to-target. In this paper, we focus on the visual identification of such affordances via contour similarity. Objects with complementary contour segments can fit together, which suggests possible opportunities for effective interactions. We present a system for the identification and evaluation of partial contour-based matches and analyze the system’s behavior. We propose a set of sample tool-use scenarios as part of our analysis. We demonstrate the use of the system in providing guidance to an autonomous robotic agent performing tool selection tasks.