Syntactic parsing technologies have become significantly more robust thanks to advancements in their underlying statistical and Deep Neural Network (DNN) techniques: most modern syntactic parsers can produce a syntactic parse tree for almost any sentence, including ones that may not be strictly grammatical. Despite improved robustness, such parsers still do not reflect the alternatives in parsing that are intrinsic in syntactic ambiguities. Two most notable such ambiguities are prepositional phrase (PP) attachment ambiguities and pronoun coreference ambiguities. In this paper, we discuss PatchComm, which uses commonsense knowledge to help resolve both kinds of ambiguities. To the best of our knowledge, we are the first to propose the general-purpose approach of using external commonsense knowledge bases to guide syntactic parsers. We evaluated PatchComm against the state-of-the-art (SOTA) spaCy parser on a PP attachment task and against the SOTA NeuralCoref module on a coreference task. Results show that PatchComm is successful at detecting syntactic ambiguities and using commonsense knowledge to help resolve them.

Logo is more than a programming language. It is a learning environment where children explore mathematical ideas and create projects of their own design. Logo, the first computer language explicitly designed for children, was invented by Seymour Papert, Wallace Feurzeig, Daniel Bobrow, and Cynthia Solomon in 1966 at Bolt, Beranek and Newman, Inc. (BBN). Logo’s design drew upon two theoretical frameworks: Jean Piaget’s constructivism and Marvin Minsky’s artificial intelligence research at MIT. One of Logo’s foundational ideas was that children should have a powerful programming environment. Early Lisp served as a model with its symbolic computation, recursive functions, operations on linked lists, and dynamic scoping of variables. Logo became a symbol for change in elementary mathematics education and in the nature of school itself. The search for harnessing the computer’s potential to provide new ways of teaching and learning became a central focus and guiding principle in the Logo language development as it encompassed a widening scope that included natural language, music, graphics, animation, story telling, turtle geometry, robots, and other physical devices.

Graphical visualization has demonstrated enormous power in helping people to understand complexity in many branches of science. But, curiously, AI has been slow to pick up on the power of visualization. Alar is a visualization system intended to help people understand and control symbolic inference. Alar presents dynamically controllable node-and-arc graphs of concepts, and of assertions both supplied to the system and inferred. Alar is useful in quality assurance of knowledge bases (finding false, vague, or misleading statements; or missing assertions). It is also useful in tuning parameters of inference, especially how “liberal vs. conservative” the inference is (trading off the desire to maximize the power of inference versus the risk of making incorrect inferences). We present a typical scenario of using Alar to debug a knowledge base.

Text-based counseling and support systems have seen an increasing proliferation in the past decade. We present Fathom, a natural language interface to help crisis counselors on Crisis Text Line, a new 911-like crisis hotline that takes calls via text messaging rather than voice. Text messaging opens up the opportunity for software to read the messages as well as people, and to provide assistance for human counselors who give clients emotional and practical support. Crisis counseling is a tough job that requires dealing with emotionally stressed people in possibly life-critical situations, under time constraints. Fathom is a system that provides topic modeling of calls and graphical visualization of topic distributions, updated in real time. We develop a mixed-initiative paradigm to train coherent topic and word distributions and use them to power real-time visualizations aimed at reducing counselor cognitive overload. We believe Fathom to be the first real-time computational framework to assist in crisis counseling.

How can a new user learn an unfamiliar application, especially if it is a high-functionality (hi-fun) application, like Photoshop, Excel, or programming language IDEfi Many applications provide introductory videos, illustrative examples, and documentation on individual operations. Tests show, however, that novice users are likely to ignore the provided help, and try to learn by exploring the application first. In a hi-fun application, though, the user may lack understanding of the basic concepts of an application's operation, even though they were likely explained in the (ignored) documentation. This paper introduces steptorials ("stepper tutorials"), a new interaction strategy for learning hi-fun applications. A steptorial aims to teach the user how to work through a simple, but nontrivial, example of using the application. Steptorials are unique because they allow varying the autonomy of the user at every step. A steptorial has a control structure of a reversible programming language stepper. The user may choose, at any time, to be shown how to do a step, be guided through it, use the application interface without constraint, or to return to a previous step. It reduces the risk in either trying new operations yourself, or conversely, the risk of ceding control to the computer. It introduces a new paradigm of mixed-initiative learning of application interfaces.

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace , a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.

This editorial introduction describes the aims and scope of the special issue on Common Sense for Interactive Systems of the ACM Transactions on Interactive Intelligent Systems. It explains why the common sense knowledge problem is crucial for both artificial intelligence and human-computer interaction, and it shows how the four articles selected for this issue fit into the theme.

Social media enables people to share personal experiences, often through real-time media such as chat. People also record their life experiences in media collections, with photos and video. However, today's social media force a choice between real-time communication, and authoring a coherent story illustrated with digital media. There is simply not enough time in real-time communication to select and compose coherent multimedia stories.

People who are not professional storytellers usually have difficulty composing travel photos and videos from a mundane slideshow into a coherent and engaging story, even when it is about their own experiences. However, consider putting the same person in a conversation with a friend - suddenly the story comes alive.

When editing a story from a large collection of media, such as photos and video clips captured from daily life, it is not always easy to understand how particular scenes fit into the intent for the overall story. Especially for novice editors, there is often a lack of coherent connections between scenes, making it difficult for the viewers to follow the story.

People interact with interfaces to accomplish goals, and knowledge about human goals can be useful for building intelligent user interfaces. We suggest that modeling high, human-level goals like "repair my credit score", is especially useful for coordinating workflows between interfaces, automated planning, and building introspective applications.

In AI, we often need to make sense of data that can be measured in many different dimensions -- thousands of dimensions or more -- especially when this data represents natural language semantics. Dimensionality reduction techniques can make this kind of data more understandable and more powerful, by projecting the data into a space of many fewer dimensions, which are suggested by the computer. Still, frequently, these results require more dimensions than the human mind can grasp at once to represent all the meaningful distinctions in the data.

We propose a new way to raise the level of discourse in the programming process: permit ambiguity, but manage it by linking it to unambiguous examples. This allows programming environments to work with high-level descriptions that lack precise semantics, such as natural language descriptions or conceptual diagrams, without requiring programmers to formulate their ideas in a formal language first. As an example of this idea, we present Zones, a code search and reuse interface that connects code with ambiguous natural language annotations about its purpose. The backend, called ProcedureSpace, induces relationships between these purpose annotations, static code analysis features, and a variety of natural language background knowledge. ProcedureSpace can search for code given purpose descriptions or vice versa, and can even find code that was never annotated or commented. Since completed Zones searches become annotations, the system learns from user interaction. Users in a preliminary study found that reasoning jointly over natural language and programming language helped them reuse code.

In the world of visual storytelling, narrative development relies on a particular temporal ordering of shots and sequences and scenes. Rarely is this ordering cast in stone. Rather, the particular ordering of a story reflects a myriad of interdependent decisions about the interplay of structure, narrative arc and character development. For storytellers, particularly those developing their narratives from large documentary archives, it would be helpful to have a visualization system partnered with them to present suggestions for the most compelling story path.

In order to be helpful to people, the intelligent interfaces of the future will have to acquire, represent, and infer simple knowledge about everyday life and activities. While much work in AI has represented this knowledge at the word, sentence, and logical assertion level, we see a growing need to understand it at a larger granularity, that of stories.

We present a game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process to learn about the most salient characteristics of a particular concept. We use statistical classification methods to discover the most informative characteristics in the Open Mind Common Sense knowledge base, and use these characteristics to play a game of 20 Questions with the user. Our interface also allows users to enter knowledge more quickly than a more traditional knowledge-acquisition interface. An evaluation showed that users enjoyed the game and that it increased the speed of knowledge acquisition.

There is a mutually beneficial relationship between user interfaces and common sense reasoning and acquisition. Common sense knowledge enables interfaces to better understand and to be more grounded in the world of the user, thus improving the user's overall experience with the interface. This would not be possible without large sources of common sense knowledge, which likewise benefit from intelligent interfaces designed to make the knowledge acquisition processes more productive and enjoyable for the contributor. These two complementary interface types and their interaction are explored in this workshop.

Electronic Commerce on the Web is thriving, but consumers still have trouble finding products that will meet their needs and desires. AI has offered many kinds of Recommender Systems [11], but they are all oriented toward searching based on concrete attributes of the product (e.g. price, color) or the user (as in Collaborative Filtering). Based on commonsense reasoning technology, we introduce a novel recommendation technique, Scenario-Oriented Recommendation, which helps users by mapping their daily scenarios to product attributes, and works even when users don't know exactly what products they are looking for.

The need for more effective communication between people of different countries has increased as travel and communications bring more of the world’s people together. Communication is often difficult because of both language differences and cultural differences. Attempts to bridge these differences include many attempts to perform ma-chine translation or provide language resources such as dictionaries or phrase books; however, many problems related to cultural and conceptual differences still remain. Automated mechanisms to analyze cultural similarities and differences might be used to improve traditional machine translators and as aids to cross-cultural communica-tion. This article presents an approach to automatically compute cultural differences by comparing databases of common-sense knowledge in different languages and cultures. GlobalMind provides an interface for acquiring databases of common-sense knowledge from users who speak different languages. It implements inference modules to compute the cultural similarities and differences between these databases. In this article, the design of the GlobalMind databases, the implementation of its inference modules, as well as an evaluation of GlobalMind are described.

Many users are familiar with the interesting but limited functionality of Data Detector interfaces like Microsoft's Smart Tags and Google's AutoLink. In this paper we significantly expand the breadth and functionality of this type of user interface through the use of large-scale knowledge bases of semantic information. The result is a Web browser that is able to generate personalized semantic hypertext, providing a goal-oriented browsing experience.We present (1) Creo, a Programming by Example system for the Web that allows users to create a general-purpose procedure with a single example, and (2) Miro, a Data Detector that matches the content of a page to high-level user goals.An evaluation with 34 subjects found that they were more efficient using our system, and that the subjects would use features like these if they were integrated into their Web browser.

Networked appliances might make them aware of each other, but interacting with a complex network can be difficult in itself. KitchenSense is a sensor rich networked kitchen research platform that uses Common Sense reasoning to simplify control interfaces and augment interaction. The system's sensor net attempts to interpret people's intentions to create fail-soft support for safe, efficient and aesthetic activity. By considering embedded sensor data together with daily-event knowledge, a centrally-controlled system can develop a shared context across various appliances. The system is a research platform that is used to evaluate augmented intelligent support of work scenarios in physical spaces.

We are reaching a crisis with design of user interfaces for consumer electronics. Flashing 12:00 time indicators, push-and-hold buttons, and interminable modes and menus are all symptoms of trying to maintain a one-to-one correspondence between functions and physical controls, which becomes hopeless as the number of capabilities of devices grows. We propose instead to orient interfaces around the goals that users have for the use of devices.We present Roadie, a user interface agent that provides intelligent context-sensitive help and assistance for a network of consumer devices. Roadie uses a Commonsense knowledge base to map between user goals and functions of the devices, and an AI partial-order planner to provide mixed-initiative assistance with executing multi-step procedures and debugging help when things go wrong.

A principal problem in speech recognition is distinguishing between words and phrases that sound similar but have different meanings. Speech recognition programs produce a list of weighted candidate hypotheses for a given audio segment, and choose the "best" candidate. If the choice is incorrect, the user must invoke a correction interface that displays a list of the hypotheses and choose the desired one. The correction interface is time-consuming, and accounts for much of the frustration of today's dictation systems. Conventional dictation systems prioritize hypotheses based on language models derived from statistical techniques such as n-grams and Hidden Markov Models.We propose a supplementary method for ordering hypotheses based on Commonsense Knowledge. We filter acoustical and word-frequency hypotheses by testing their plausibility with a semantic network derived from 700,000 statements about everyday life. This often filters out possibilities that "don't make sense" from the user's viewpoint, and leads to improved recognition. Reducing the hypothesis space in this way also makes possible streamlined correction interfaces that improve the overall throughput of dictation systems.

Every program tells a story. Programming, then, is the art of constructing a story about the objects in the program and what they do in various situations. So-called programming languages, while easy for the computer to accurately convert into code, are, unfortunately, difficult for people to write and understand.We explore the idea of using descriptions in a natural language as a representation for programs. While we cannot yet convert arbitrary English to fully specified code, we can use a reasonably expressive subset of English as a visualization tool. Simple descriptions of program objects and their behavior generate scaffolding (underspecified) code fragments, that can be used as feedback for the designer. Roughly speaking, noun phrases can be interpreted as program objects; verbs can be functions, adjectives can be properties. A surprising amount of what we call programmatic semantics can be inferred from linguistic structure. We present a program editor, Metafor, that dynamically converts a user's stories into program code, and in a user study, participants found it useful as a brainstorming tool.

One of the principal problems of online help is the mismatch between the specialized knowledge and technical vocabulary of experts who are providing the help, and the relative naivete of novices, who usually are often not in a position to understand solutions expressed by the expert in their own terms. Most of the interfaces are plagued by recurrent key problems: 1) elicitation - how to ask questions that enable the helper to make decisions, and at the same time, are understandable to the novice and 2) explanation - how to explain rationale behind expert decisions in terms that the user can understand. One of the best ways to do this is for the expert to provide analogies in terms of commonsense knowledge, which provide metaphors that help novices learn problem-solving skills. SuggestDesk is a system that acts as an advisor to an online technical support person. It uses a large commonsense knowledge base to search for analogies between known technical problem-solution pairs, and situations and events in everyday life that can be used to explain them.

People are performing increasingly complicated actions on the web, such as automated purchases involving multiple sites. Things often go wrong, however, and it can be difficult to diagnose a problem in a complex process. Information must be integrated from multiple sites before relations among processes and data can be visualized and understood. Once the source of a problem has been diagnosed, it can be tedious to explain the process of diagnosis to others, and difficult to review the steps later.We present a web interface agent, Woodstein, that monitors user actions on the web and retrieves related information to assemble an integrated view of an action. It manages user hypotheses during problem diagnosis by capturing users' judgments of the correctness of data and processes. These hypotheses can be shared with others, including customer service representatives, or accessed later. We will see this feature in the context of diagnosing problems on the web, and discuss its broader applicability to system interfaces in general.

We present a web interface agent, Woodstein, that monitors user actions on the web and retrieves related information to assemble an integrated view of a transaction. It manages user hypotheses during diagnosis by capturing users' judgments of the correctness of data and processes. These hypotheses can be shared with others, such as customer service representatives, or saved for later. We will see this feature in the context of diagnosing problems on the web.

Natural language is imbued with a rich semantics but unfortunately its complex elegance is often mistaken for mere imprecision. Because complete parsers of English are not yet achievable, people assume that it is not feasible to use English directly as a means of instructing computers. However, in this paper, we show that English descriptions of procedures often contain programmatic semantics - linguistic features that can be easily mapped into programming language constructs. Some linguistic features can even inspire new ways of thinking about specifying programs. Far from being hopelessly ambiguous, natural languages exhibit important principles of communication that could be used to make human-computer communication more natural.

One of the biggest unaddressed challenges for the digital economy is what to do when electronic transactions go wrong. Consumers are frustrated by interminable phone menus, and long delays to problem resolution. Businesses are frustrated by the high cost of providing quality customer service.We believe that many simple problems, such as mistyped numbers or lost orders, could be easily diagnosed if users were supplied with end-user debugging tools, analogous to tools for software debugging. These tools can show the history of actions and data, and provide assistance for keeping track of and testing hypotheses. These tools would benefit not only users, but businesses as well by decreasing the need for customer service.

This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.

The experience of a trusted group of colleagues can help users improve the quality and focus of their browsing and searching activities. How could a system provide such help, when and where the users need it, without disrupting their normal work activities? This paper describes Context-Aware Proxy based System (CAPS), an agent that recommends pages and annotates links to reveal their relative popularity among the users colleagues, matched with their automatically computed interest profiles. A Web proxy tracks browsing habits, so CAPS requires no explicit input from the user. We review here CAPS design principles and implementation. We tested user satisfaction with the interface and the accuracy of the ranking algorithm. These experiments indicate that CAPS has high potential to support effective ranking for quality judgment - by users

We demonstrate Woodstein, a software agent that tracks user interaction with e-commerce Web sites through a browser, and relates the browsing events to high-level models of complex, multi-step processes such as purchases or account transfers. Woodstein explains action steps and data in an understandable form, visualizes action history, and aids the user in exploring the causes of errors.

One of the biggest unaddressed challenges for the digital economy is what to do when electronic transactions go wrong. Consumers are frustrated by interminable phone menus and long delays to problem resolution. Businesses are frustrated by the high cost of providing quality customer service.We believe that many simple problems, such as mistyped numbers or lost orders, could be easily diagnosed if users were supplied with end-user tools for e-commerce problem solving. Working analogously to tools for software debugging, these tools show the history of actions and data and allow the user to explore hypotheses about what went wrong.

The Apt Decision agent learns user preferences in the domain of rental real estate by observing the user's critique of apartment features. Users provide a small number of criteria in the initial interaction, receive a display of sample apartments, and then react to any feature of any apartment independently, in any order. Users learn which features are important to them as they discover the details of specific apartments. The agent uses interactive learning techniques to build a profile of user preferences, which can then be saved and used in further retrievals. Because the user's actions in specifying preferences are also used by the agent to create a profile, the result is an agent that builds a profile without redundant or unnecessary effort on the user's part.

When a novice needs help, often the best solution is to find a human expert who is capable of answering the novice's questions. But often, novices have difficulty characterizing their own questions and expertise and finding appropriate experts. Previous attempts to assist expertise location have provided matchmaking services, but leave the task of classifying knowledge and queries to be performed manually by the participants. We introduce Expert Finder, an agent that automatically classifies both novice and expert knowledge by autonomously analyzing documents created in the course of routine work. Expert Finder works in the domain of Java programming, where it relates a user's Java class usage to an independent domain model. User models are automatically generated that allow accurate matching of query to expert without either the novice or expert filling out skill questionnaires. Testing showed that automatically generated profiles matched well with experts' own evaluation of their skills, and we achieved a high rate of matching novice questions with appropriate experts.

The Internet enables groups of people throughout the world to interact to discuss issues, get assistance, learn, and socialize. However, when there are thousands of loosely defined groups in which a user could potentially participate, the problem becomes finding the groups of most interest. In this paper we focus on the domain of Internet Relay Chat real-time text messaging, and describe a “social butterfly” agent called Butterfly that samples available conversational groups and recommends ones of interest. We discuss Butterfly’s motivation, usage, realworld design constraints, implementation, and results. Finally, we introduce work in progress on a multi-agent approach that has grown out of our experience with Butterfly.

No abstract available.

No abstract available.

Web browsing, like most of today's desktop applications, is usually a solitary activity.Other forms of media, such as watching television, are often done by groups of people, such as families or friends.What would it be like to do collaborative Web browsing?Could the computer provide assistance to group browsing by trying to help find mutual interests among the participants?Let's Browse is an experiment in building an agent to assist a group of people in browsing, by suggesting new material likely to be of common interest.It is built as an extension to the singleuser Web browsing agent Letizia.Let's Browse features automatic detection of the presence of users, automated "channel surfing" browsing, and dynamic display of the user profiles and explanation of recommendations.

No abstract available.

In most experiments with user interface agents to date, it has been necessary either to implement both the agent and the application from scratch, or to modify the code of an existing application to enable the necessary communication.Instead, we would like to be able to "attach" an agent to an existing application, while requiring only a minimum of advance planning on the part of the application developer.Commercial applications are increasingly supporting the use of "application programmers' interfaces" and scripting languages as mean of achieving external control of applications, Are these mechanisms sufficient for software agents to achieve communication with applications?

Two branches of the trend towards "agents" that are gaining currency are interface agents, software that actively assists a user in operating an interactive interface, and autonomous agents, software that takes action without user intervention and operates concurrently, either while the user is idle or taking other actions. These two branches are related, but not identical, and are often lumped together under the single term "agent". Much agent work can be classified as either being an interface agent, but not autonomous, or as an autonomous agent, but not operating directly in the interface. We show why it is important to have agents that are both interface agents and autonomous agents. We explore some design principles for such agents, and illustrate these principles with a description of Letizia, an autonomous interface agent that makes real-time suggestions for Web pages that a user might be interested in browsing. Keywords Agents, interface agents, autonomous agents, Web, browsi...

Article Compelling intelligent user interfaces—how much AI? Share on Authors: Larry Birnbaum ILS/Northwestern, 1890 Maple Avenue, Evanston IL ILS/Northwestern, 1890 Maple Avenue, Evanston ILView Profile , Eric Horvitz Microsoft Research, One Microsoft Way, Redmond WA Microsoft Research, One Microsoft Way, Redmond WAView Profile , David Kurlander Microsoft Research, One Microsoft Way, Redmond WA Microsoft Research, One Microsoft Way, Redmond WAView Profile , Henry Lieberman MIT Media Laboratory, 20 Ames Street, Cambridge MA MIT Media Laboratory, 20 Ames Street, Cambridge MAView Profile , Joe Marks MERL, 201 Broadway, Cambridge MA MERL, 201 Broadway, Cambridge MAView Profile , Steve Roth Robotics Institute, Carnegie Mellon University, Pittsburgh PA Robotics Institute, Carnegie Mellon University, Pittsburgh PAView Profile Authors Info & Claims IUI '97: Proceedings of the 2nd international conference on Intelligent user interfacesJanuary 1997 Pages 173–175https://doi.org/10.1145/238218.238319Online:06 January 1997Publication History 16citation637DownloadsMetricsTotal Citations16Total Downloads637Last 12 Months41Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The dynamic nature of virtual display spaces can provide powerful tools for helping people comprehend phenomena that occur over widely disparate spatial scales. This paper presents the macroscope, an interactive technique for browsing very large spaces of displayed information at different scales. The macroscope takes as a point of departure the traditional 2D zoom and pan operations, but introduces multiple translucent layers to avoid the problem of losing visual context. The third dimension is used to visually separate the layers and provide an external point of view for controlling the presentation. The user can manipulate viewfinders in the virtual space, which control the relative scale and position of the layers.

Program debugging can be an expensive, complex and frustrating process. Conventional programming environments provide little explicit support for the cognitive tasks of diagnosis and visualization faced by the programmer. ZStep 94 is a program debugging environment designed to help the programmer understand the correspondence between static program code and dynamic program execution. Some of ZStep 94's innovations include: • An animated view of program execution, using the very same display used to edit the source code • A window that displays values which follows the stepper's focus • An incrementally-generated complete history of program execution and output • "Video recorder " controls to run the program in forward and reverse directions and control the level of detail displayed • One-click access from graphical objects to the code that

No abstract available.

How would you interactively browse a very large display space, for example, a street map of the entire United States? The traditional solution is zoom and pan. But each time a zoom-in operation takes place, the context from which it came is visually lost. Sequential applications of the zoom-in and zoom-out operations may become tedious. This paper proposes an alternative technique, the macroscope, based on zooming and planning in multiple translucent layers. A macroscope display should comfortably permit browsing continuously on a single image, or set of images in multiple resolutions, on a scale of at least 1 to 10,000.

Love playing with those fancy menu-based graphical user interfaces, but afraid to program one yourself for your own application? Do windows seem opaque to you? Are you scared of mice? Like what-you-see-is-what-you-get but don't know how to get what you want to see on the screen?Everyone agrees using systems like graphical document illustrators, circuit designers, and iconic file systems is fun, but programming user interfaces for these systems isn't as much fun as it should be. Systems like the Lisp Machines, Xerox D-Machines, and Apple Macintosh provide powerful graphics primitives, but the casual applications designer is often stymied by the difficulty of mastering the details of window specification, multiple processes, interpreting mouse input, etc.This paper presents a kit called EZWin, which provides many services common to implementing a wide variety of interfaces, described as generalized editors for sets of graphical objects. An individual application is programmed simply by creating objects to represent the interface itself, each kind of graphical object, and each command. A unique interaction style is established which is insensitive to whether commands are chosen before or after their arguments. The system anticipates the types of arguments needed by commands, preventing selection mistakes which are a common source of frustrating errors. Displayed objects are made "mouse-sensitive" only if selection of the object is appropriate in the current context. The implementation of a graphical interface for a computer network simulation is described to illustrate how EZWin works.

Children's coloring books contain line drawings which a child can fill in with a crayon to produce colored pictures. Two dimensional colored areas can be produced on a raster display by an analogous method. After drawing a closed curve with line drawing commands, the graphics system can fill the area bordered by the curve. This paper presents an algorithm for filling in areas of any size or shape. The area may be filled with any color, texture, or “wallpaper” pattern. The algorithm is simple, flexible and efficient, optimized to take advantage of the the memory organization of most current raster graphics systems.