Although to-do lists are a ubiquitous form of personal task management, there has been no work on intelligent assistance to automate, elaborate, or coordinate a user’s to-dos. Our research focuses on three aspects of intelligent assistance for to-dos. We investigated the use of intelligent agents to automate to-dos in an office setting. We collected a large corpus from users and developed a paraphrase-based approach to matching agent capabilities with to-dos. We also investigated to-dos for personal tasks and the kinds of assistance that can be offered to users by elaborating on them on the basis of substep knowledge extracted from the Web. Finally, we explored coordination of user tasks with other users through a to-do management application deployed in a popular social networking site. We discuss the emergence of Social Task Networks, which link users‘ tasks to their social network as well as to relevant resources on the Web. We show the benefits of using common sense knowledge to interpret and elaborate to-dos. Conversely, we also show that to-do lists are a valuable way to create repositories of common sense knowledge about tasks.

Many research efforts are addressing the problem of enabling automatic summarization of opinions and assessments stated on the web in product reviews, discussion forums, and blogs. One key difficulty is that relevant assessments scattered throughout web pages are obscured by variations in natural language. In this paper, we focus on a novel aspect of enabling aggregations of assessments of degree to which a given property holds for a given entity (for instance, how touristy is Boston). We present GrainPile, a user interface for extracting from the web, aggregating and quantifying degree assessments of unconstrained topics. The interface provides a variety of functions: a) identification of dimensions of comparison (properties) relevant to a particular entity or set of entities, b) comparisons of like entities on user-specified properties (for example, which university is more prestigious, Yale or Cornell), c) tracing the derived opinions back to their sources (so that the reasons for the opinions can be found). A central contribution in GrainPile is the evaluated demonstration of feasibility of mapping the recognized expressions (such as fairly, very, extremely, and so on) to a common scale of numerical values and aggregating across all the extracted assessments to derive an overall assessment of degree. GrainPile's novel assessment and aggregation of degree expressions is shown to strongly outperform an interpretation-free, co-occurrence based method.

A new generation of intelligent applications can be enabled by broad-coverage knowledge repositories about everyday objects. We distill lessons in design of intelligent user interfaces which collect such broad-coverage knowledge from untrained volunteers. We motivate the knowledge-driven template-based approach adopted in Learner2, a second generation proactive acquisition interface for eliciting such knowledge. We present volume, accuracy, and recall of knowledge collected by fielding the system for 5 months. Learner2 has so far acquired 99,018 general statements, emphasizing knowledge about parts of and typical uses of objects.

When designing mixed-initiative systems, full formalization of all potentially relevant knowledge may not be cost-effective or practical. This paper motivates the need for semi-formal representations that combine machine-processable structures with free text statements, and discusses the need to design them in a way that makes the free text more amenable to automated structuring and processing. Our work is done in the context of argumentation systems, and has explored a range of tradeoffs in combining informal free-text statements with formal connectors. The paper compares alternative argument representations which combine structured argument connectors with free text. We discuss merits of the systems based on a variety of analysis structures that we have collected from Web users to date.

Extensive and deep paraphrase corpora are important for a variety of natural language processing and user interaction tasks. In this paper, we present an approach which i) collects multiple paraphrases per given item from volunteers and ii) incentivises responsible contributions by volunteer contributors. Our approach is to solicit paraphrases from Web volunteers, both collecting new paraphrases with no prompting and asking contributors to guess partially obfuscated paraphrases. To test the approach, we have implemented an online game, 1001 Paraphrases (http://ai-games.org/paraphrase.html), and deployed it to collect 20,944 entries focused on paraphrases of 400 statements. The approach complements existing text extraction methods and has some inherent unique advantages. We present and motivate our design as well as share preliminary observations and lessons learned about the performance of the approach.

An emerging approach to knowledge acquisition is to collect statements from volunteer contributors over the Web. In this approach, the design of the acquisition interface is key to focusing on statements of interest, avoiding spurious entries, retaining the contributors, etc. Several such volunteer-contribution-based systems have been deployed to date, each with its own idiosyncratic interface. This paper discusses some key challenges faced by volunteer collection interfaces, and outlines the design features that we have found effective in addressing some aspects of those challenges. The paper discusses how these features have been implemented in deployed collection systems, and reflects on the data collected to extract lessons for future work in this research area.

One of the long-term goals of Artificial Intelligence is construction of a machine that is capable of reasoning about the everyday world the way humans are. In this paper, I first argue that construction of a large collection of statements about everyday world (a repository of commonsense knowledge) is a valuable step towards this long-term goal. Then, I point out that volunteer contributors over the Internet --- a frequently overlooked source of knowledge --- can be tapped to construct such a knowledge repository. To operationalize construction of a large commonsense knowledge repository by volunteer contributors, I then introduce cumulative analogy, a class of analogy-based reasoning algorithms that leverage existing knowledge to pose knowledge acquisition questions to the volunteer contributors. The algorithms have been implemented and deployed as the Learner system. To date, about 3,400 volunteer contributors have interacted with the system over the course of 11 months, increasing a starting collection of 47,147 statements by 362% to a total of 217,971. The deployed system and the growing collection of knowledge it acquired are publicly available from http://teach-computers.org. The knowledge is beginning to be used in follow-on research to address some well-recognized and novel reasoning tasks.