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<title>AI Memos (1959 - 2004)</title>
<link>http://hdl.handle.net/1721.1/5460</link>
<description/>
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<rdf:li rdf:resource="http://hdl.handle.net/1721.1/45554"/>
<rdf:li rdf:resource="http://hdl.handle.net/1721.1/7341"/>
<rdf:li rdf:resource="http://hdl.handle.net/1721.1/7340"/>
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<dc:date>2013-05-18T05:30:05Z</dc:date>
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<title>The Measurement of Visual Motion</title>
<link>http://hdl.handle.net/1721.1/45554</link>
<description>The Measurement of Visual Motion
Hildreth, Ellen C.; Ullman, Shimon
The analysis of visual motion divides naturally into two stages: the first is the measurement of motion, for example, the assignment of direction and magnitude of velocity to elements in the image, on the basis of the changing intensity pattern; the second is the use of motion measurements, for example, to separate the scene into distinct objects, and infer their three-dimensional structure. In this paper, we present a computational study of the measurement of motion. Similar to other visual processes, the motion of elements is not determined uniquely by information in the changing image; additional constraint is required to compute a unique velocity field. Given this global ambiguity of motion, local measurements from the changing image, such as those provided by directionally-selective simple cells in primate visual cortex, cannot possibly specify a unique local velocity vector, and in fact, specify only one component of velocity. Computation of the full two-dimensional velocity field requires the integration of local motion measurements, either over an area, or along contours in the image. We will examine possible algorithms for computing motion, based on a range of additional constraints. Finally, we will present implications for the biological computation of motion.
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<dc:date>1982-12-01T05:00:00Z</dc:date>
</item>
<item rdf:about="http://hdl.handle.net/1721.1/7341">
<title>Complexity of Human Language Comprehension</title>
<link>http://hdl.handle.net/1721.1/7341</link>
<description>Complexity of Human Language Comprehension
Ristad, Eric Sven
The goal of this article is to reveal the  computational structure of modern principle-and-parameter (Chomskian) linguistic  theories: what computational problems do  these informal theories pose, and what is the  underlying structure of those computations?  To do this, I analyze the computational  complexity of human language  comprehension: what linguistic  representation is assigned to a given sound?  This problem is factored into smaller,  interrelated (but independently statable)  problems. For example, in order to  understand a given sound, the listener must  assign a phonetic form to the sound;  determine the morphemes that compose the  words in the sound; and calculate the  linguistic antecedent of every pronoun in the  utterance. I prove that these and other  subproblems are all NP-hard, and that  language comprehension is itself PSPACE-hard.
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<dc:date>1988-12-01T05:00:00Z</dc:date>
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<item rdf:about="http://hdl.handle.net/1721.1/7340">
<title>The Perception of Subjective Surfaces</title>
<link>http://hdl.handle.net/1721.1/7340</link>
<description>The Perception of Subjective Surfaces
Brady, Michael; Grimson, W. Eric L.
It is proposed that subjective contours are an  artifact of the perception of natural three-dimensional surfaces. A recent theory of  surface interpolation implies that "subjective  surfaces" are constructed in the visual system  by interpolation between three-dimensional  values arising from interpretation of a variety  of surface cues. We show that subjective  surfaces can take any form, including singly  and doubly curved surfaces, as well as the  commonly discussed fronto-parallel planes.  In addition, it is necessary in the context of  computational vision to make explicit the  discontinuities, both in depth and in surface  orientation, in the surfaces constructed by  interpolation. It is proposed that subjective  surfaces and subjective contours are  demonstrated. The role played by figure  completion and enhanced brightness contrast  in the determination of subjective surfaces is  discussed. All considerations of surface  perception apply equally to subjective  surfaces.
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<dc:date>1981-11-01T05:00:00Z</dc:date>
</item>
<item rdf:about="http://hdl.handle.net/1721.1/7339">
<title>Electrical Design: A Problem for Artificial Intelligence Research</title>
<link>http://hdl.handle.net/1721.1/7339</link>
<description>Electrical Design: A Problem for Artificial Intelligence Research
Sussman, Gerald Jay
This report outlines the problem of intelligent  failure recovery in a problem-solver for  electrical design. We want our problem solver  to learn as much as it can from its mistakes.  Thus we cast the engineering design process  on terms of Problem Solving by Debugging  Almost-Right Plans, a paradigm for automatic  problem solving based on the belief that  creation and removal of "bugs" is an  unavoidable part of the process of solving a  complex problem. The process of localization  and removal of bugs called for by the  PSBDARP theory requires an approach to  engineering analysis in which every result has  a justification which describes the exact set of  assumptions it depends upon. We have  developed a program based on Analysis by  Propagation of Constraints which can explain  the basis of its deductions. In addition to  being useful to a PSBDARP designer, these  justifications are used in Dependency-Directed Backtracking to limit the  combinatorial search in the analysis routines.  Although the research we will describe is  explicitly about electrical circuits, we believe  that similar principles and methods are  employed by other kinds of engineers,  including computer programmers.
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<dc:date>1977-06-01T04:00:00Z</dc:date>
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