We present new techniques for supervised wrapper generation and automated web information extraction, and a system called Lixto implementing these techniques. Our system can generate wrappers which translate relevant pieces of HTML pages into XML. Lixto, of which a working prototype has been implemented, assists the user to semi-automatically create wrapper programs by providing a fully visual and interactive user interface. In this convenient user-interface very expressive extraction programs can be created. Internally, this functionality is reflected by the new logicbased declarative language Elog. Users never have to deal with Elog and even familiarity with HTML is not required. Lixto can be used to create an "XML-Companion" for an HTML web page with changing content, containing the continually updated XML translation of the relevant information. 1

■ A long-standing dream of artificial intelligence has been to put commonsense knowledge into computers—enabling machines to reason about everyday life. Some projects, such as Cyc, have begun to amass large collections of such knowledge. However, it is widely assumed that the use of common sense in interactive applications will remain impractical for years, until these collections can be considered sufficiently complete and commonsense reasoning sufficiently robust. Recently, at the Massachusetts Institute of Technology’s Media Laboratory, we have had some success in applying commonsense knowledge in a number of intelligent interface agents, despite the admittedly spotty coverage and unreliable inference of today’s

Digital devices today have little understanding of their real-world context, and as a result they often make stupid mistakes. To improve this situation we are developing a database of world knowledge called ThoughtTreasure at the same time that we develop intelligent applications. In this paper we present one such application, SensiCal, a calendar with a degree of common sense. We discuss the pieces of common sense important in calendar management and present methods for extracting relevant information from calendar items.

In order to build a new breed of software that can deeply understand people and our problems, so that they can help us to solve them, we are developing at the Media Lab a suite of computational tools to give machines the capacity to learn and reason about everyday life---in other words, to give machines `common sense'. We are building several large-scale commonsense knowledge bases that model broad aspects of the ordinary human world, including descriptions of the kinds of goals people have, the actions we can take and their effects, the kinds of objects that we encounter every day, and so forth, as well as the relationships between such entities. In this article we describe three systems we have built---ConceptNet, LifeNet, and StoryNet---that take unconventional approaches to representing, acquiring, and reasoning with large quantities of commonsense knowledge. Each adopts a different approach: ConceptNet is a large-scale semantic network, LifeNet is a probabilistic graphical model, and StoryNet is a database of story-scripts. We describe the evolution of these three systems, the techniques that underlie their construction and their operation, and conclude with a discussion of how we might combine them into an integrated commonsense reasoning system that uses multiple representations and reasoning methods.

We present an implemented model of story understanding and apply it to the understanding of a children's story. We argue that understanding a story consists of building multirepresentation models of the story and that story models are efficiently constructed using a satisfiability solver. We present a computer program that contains multiple representations of commonsense knowledge, takes a narrative as input, transforms the narrative and representations of commonsense knowledge into a satisfiability problem, runs a satisfiability solver, and produces models of the story as output.

reasoning, reasoning about action and change This paper investigates the use of commonsense reasoning to understand texts involving stereotypical activities or scripts. We present a system that understands news stories involving four terrorism scripts. The system (1) builds a commonsense reasoning problem given an information extraction template representing a terrorist incident, and (2) uses commonsense reasoning and a commonsense knowledge base to build a model of the terrorist incident. The reasoning problem, commonsense knowledge base, and model are expressed in the classical logic event calculus. The system was developed using the MUC3 and MUC4 development data set. We present the results of running the system on the MUC3 and MUC4 test data sets, using manually generated answer key templates and templates generated automatically by two MUC4 information extraction systems. We present a detailed analysis of the models produced by the system given automatically generated templates. We present methods for answering questions based on the models produced by our system. We assess the portability of the system by extending it to handle 10 scripts frequent in Project Gutenberg American literature texts. 1

Natural human-like human-robot interaction (NHL-HRI) requires the robot to be skilled both at recognizing and producing many subtle human behaviors, often taken for granted by humans. We suggest a rough division of these requirements for NHL-HRI into three classes of properties: (1) social behaviors, (2) goal-oriented cognition, and (3) robust intelligence, and present the novel DIARC architecture for complex affective robots for human-robot interaction, which aims to meet some of those requirements. We briefly describe the functional properties of DIARC and its implementation in our ADE system. Then we report results from human subject evaluations in the laboratory as well as our experiences with the robot

Humans naturally share knowledge by telling stories. This is a form of knowledge exchange we engage in right from early childhood, and over time we learn to recall, order and organize our experiences as stories [1]. In this paper we describe the Open Mind Experience(OMEX) system, a web-based knowledge acquisition tool that exploits our natural ability to tell and explain stories in order to build a large-scale commonsense knowledgebase. We built OMEX to gather descriptions and explanations of everyday, 'common sense' experiences from volunteer contribu- tors distributed across the Intemet. We first describe the knowledge from the general public, the Open Mind Common Sense (OMCS) project. The OMCS project focused on collecting largely assertional commonsense knowledge, and we describe some of its products and spin-offs. We then give several motivating reasons for why we now wish to now collect more script-like knowledge. We then explain the features of the new OMEX site and give an evaluation of system based on a preliminary user study. We conclude by discussing our future directions.

Since scripts were proposed in the 1970's as an inferencing mechanism for AI and natural language processing programs, there have been few attempts to build a database of scripts. This paper describes a database and lexicon of scripts that has been added to the ThoughtTreasure commonsense platform. The database provides the following information about scripts: sequence of events, roles, props, entry conditions, results, goals, emotions, places, duration, frequency, and cost. English and French words and phrases are linked to script concepts.

This article examines Marvin Minsky's Society of Mind theory of human cognition. We describe some of the history behind the theory, review several of the specific mechanisms and representations that Minsky proposes, and consider related developments in Artificial Intelligence since the theory's publication.