Browsing Computer Science and Artificial Intelligence Lab (CSAIL) by Title
Now showing items 3760-3779 of 3804
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What Are Plans For?
(1989-10-01)What plans are like depends on how they're used. We contrast two views of plan use. On the plan-as-program-view, plan use is the execution of an effective procedure. On the plan-as-communication view, plan use is like ... -
What are principal typings and what are they good for?
(1995-11)We demonstrate the pragmatic value of the principal typing property, a property more general than ML's principal type property, by studying a type system with principal typings. The type system is based on rank 2 intersection ... -
What are principal typings and what are they good for?
(1995-08)We demonstrate the pragmatic value of the principal typing property, a property more general than ML's principal type property, by studying a type system with principal typings. The type system is based on rank 2 intersection ... -
What Corners Look Like
(MIT Artificial Intelligence Laboratory, 1971-06)An algorithm is presented which provides a way of telling what a given trihedral corner will look like if viewed from a particular angle. The resulting picture is a junction of two or more lines each labelled according to ... -
What is a Model of the Lamda Calculus? Expanded Version
(1981-07)An elementary, purely algebraic definition of model for the untypes lambda calculus is given. This definition is shown to be equivalent to the natural semantic definition based on environments. These definitions of model ... -
What is Decidable about Strings?
(2011-02-01)We prove several decidability and undecidability results for the satisfiability/validity problem of formulas over a language of finite-length strings and integers (interpreted as lengths of strings). The atomic formulas ... -
What is Delaying the Manipulator Revolution?
(MIT Artificial Intelligence Laboratory, 1978-02)Despite two decades of work on mechanical manipulators and their associated controls, we do not see wide-spread application of these devices to many of the tasks to which they seem so obviously suited. Somehow, a variety ... -
What Makes a Good Feature?
(1992-04-01)Using a Bayesian framework, we place bounds on just what features are worth computing if inferences about the world properties are to be made from image data. Previously others have proposed that useful features reflect ... -
What Price for Eliminating Expression Side-effects?
(1985-06)Separating a programming language into side-effect-free expressions and effect-only statements should make the language more amenable to axiomatization, as well as providing benefits for style, pedagogy, and implementation ... -
What the Assassin's Guild Taught Me About Distributed Computing
(2006-05-27)Distributed computing and live-action roleplaying share many of thesame fundamental problems, as live-action roleplaying games commonly include simulations carried out by their players.Games run by the MIT Assassin's Guild ... -
What to Read: A Biased Guide to AI Literacy for the Beginner
(MIT Artificial Intelligence Laboratory, 1972-11)This note tries to provide a quick guide to AI literacy for the beginning AI hacker and for the experienced AI hacker or two whose scholarship isn't what it should be. most will recognize it as the same old list of classic ... -
What's in a Tune
(1974-11-01)The work reported here began with two fundamental assumptions: 1) The perception of music is an active process; it involves the individual in selecting, sorting, and grouping the features of the phenomena before her. ... -
What's What
(MIT Artificial Intelligence Laboratory, 1971-03)An outline of the modules used in the copy demonstration, the reasons for doing robotics, and some possible directions for further work. -
Why are There so Few Female Computer Scientists?
(1991-08-01)This report examines why women pursue careers in computer science and related fields far less frequently than men do. In 1990, only 13% of PhDs in computer science went to women, and only 7.8% of computer science ... -
Why Conniving is Better than Planning
(1972-02-01)A higher level language derives its great power form the fact that it tends to impose structure on the problem solving behavior for the user. Besides providing a library of useful subroutines with a uniform calling ... -
Why Conniving is Better than Plannng
(1972-04-01)This paper is a critique of a computer programming language, Carl Hewitts PLANNER, a formalism designed especially to cope with the problems that Artificial Intelligence encounters. It is our contention that the ... -
Why Do We See Three-dimensional Objects?
(1992-06-01)When we look at certain line-drawings, we see three-dimensional objects. The question is why; why not just see two-dimensional images? We theorize that we see objects rather than images because the objects we see are, ... -
Why Stereo Vision is Not Always About 3D Reconstruction
(1993-07-01)It is commonly assumed that the goal of stereovision is computing explicit 3D scene reconstructions. We show that very accurate camera calibration is needed to support this, and that such accurate calibration is difficult ... -
Wicked Problems and Gnarly Results: Reflecting on Design and Evaluation Methods for Idiosyncratic Personal Information Management Tasks
(2008-02-10)This paper is a case study of an artifact design and evaluation process; it is a reflection on how right thinking about design methods may at times result in sub-optimal results. Our goal has been to assess our decision ... -
Wide-Area Egomotion Estimation from Known 3D Structure
(2006-01-09)We describe an algorithm that takes as inputs a coarse3D model of an environment, and a video sequence acquiredwithin the environment, and produces as output an estimateof the cameraÂ’s 6-DOF egomotion expressed in the ...
