Disparity Gradients and Depth Scaling

The binocular perception of shape and depth relations between objects can change considerably if the viewing direction is changed only by a small angle. We explored this effect psychophysically and found a strong depth reduction effect for large disparity gradients. The effect is found to be strongest for horizontally oriented stimuli, and stronger for line stimuli than for points. This depth scaling effect is discussed in a computational framework of stereo based on a Baysian approach which allows integration of information from different types of matching primitives weighted according to their robustness.

Natural Language Syntax and First Order Preference

We have argued elsewhere that first order inference can be made more efficient by using non-standard syntax for first order logic. In this paper we show how a fragment of English syntax under Montague semantics provides the foundation of a new inference procedure. This procedure seems more effective than corresponding procedures based on either classical syntax of our previously proposed taxonomic syntax. This observation may provide a functional explanation for some of the syntactic structure of English.

Grouping For Recognition

This paper presents a new method of grouping edges in order to recognize objects. This grouping method succeeds on images of both two- and three- dimensional objects. So that the recognition system can consider first the collections of edges most likely to lead to the correct recognition of objects, we order groups of edges based on the likelihood that a single object produced them. The grouping module estimates this likelihood using the distance that separates edges and their relative orientation. This ordering greatly reduces the amount of computation required to locate objects and improves the system's robustness to error.

Computational Consequences of Agreement and Ambiguity in Natural Language

The computer science technique of computational complexity analysis can provide powerful insights into the algorithm-neutral analysis of information processing tasks. Here we show that a simple, theory-neutral linguistic model of syntactic agreement and ambiguity demonstrates that natural language parsing may be computationally intractable. Significantly, we show that it may be syntactic features rather than rules that can cause this difficulty. Informally, human languages and the computationally intractable Satisfiability (SAT) problem share two costly computional mechanisms: both enforce agreement among symbols across unbounded distances (Subject-Verb agreement) and both allow ambiguity (is a word a Noun or a Verb?).

Parallel Computation of Vernier Offsets, Curvature and Chevrons in Humans

A vernier offset is detected at once among straight lines, and reaction times are almost independent of the number of simultaneously presented stimuli (distractors), indicating parallel processing of vernier offsets. Reaction times for identifying a vernier offset to one side among verniers offset to the opposite side increase with the number of distractors, indicating serial processing. Even deviations below a photoreceptor diameter can be detected at once. The visual system thus attains positional accuracy below the photoreceptor diameter simultaneously at different positions. I conclude that deviation from straightness, or change of orientation, is detected in parallel over the visual field. Discontinuities or gradients in orientation may represent an elementary feature of vision.

Automatic Recognition of Tractability in Inference Relations

A procedure is given for recognizing sets of inference rules that generate polynomial time decidable inference relations. The procedure can automatically recognize the tractability of the inference rules underlying congruence closure. The recognition of tractability for that particular rule set constitutes mechanical verification of a theorem originally proved independently by Kozen and Shostak. The procedure is algorithmic, rather than heuristic, and the class of automatically recognizable tractable rule sets can be precisely characterized. A series of examples of rule sets whose tractability is non-trivial, yet machine recognizable, is also given. The technical framework developed here is viewed as a first step toward a general theory of tractable inference relations.

Perceptual Organization, Figure-Ground, Attention and Saliency

Notions of figure-ground, inside-outside are difficult to define in a computational sense, yet seem intuitively meaningful. We propose that "figure" is an attention-directed region of visual information processing, and has a non-discrete boundary. Associated with "figure" is a coordinate frame and a "frame curve" which helps initiate the shape recognition process by selecting and grouping convex image chunks for later matching- to-model. We show that human perception is biased to see chunks outside the frame as more salient than those inside. Specific tasks, however, can reverse this bias. Near/far, top/bottom and expansion/contraction also behave similarly.

Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples

Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization theory. In this note, we extend the theory by introducing ways of dealing with two aspects of learning: learning in the presence of unreliable examples and learning from positive and negative examples. The first extension corresponds to dealing with outliers among the sparse data. The second one corresponds to exploiting information about points or regions in the range of the function that are forbidden.

Shaping Inputs to Reduce Vibration: A Vector Diagram Approach

This paper describes a method for limiting vibration in flexible systems by shaping the system inputs. Unlike most previous attempts at input shaping, this method does not require an extensive system model or lengthy numerical computation; only knowledge of the system natural frequency and damping ratio are required. The effectiveness of this method when there are errors in the system model is explored and quantified. An algorithm is presented which, given an upper bound on acceptable residual vibration amplitude, determines a shaping strategy that is insensitive to errors in the estimated natural frequency. A procedure for shaping inputs to systems with input constraints is outlined. The shaping method is evaluated by dynamic simulations and hardware experiments.

Fault-Tolerant Design for Multistage Routing Networks

As the size of digital systems increases, the mean time between single component failures diminishes. To avoid component related failures, large computers must be fault-tolerant. In this paper, we focus on methods for achieving a high degree of fault-tolerance in multistage routing networks. We describe a multipath scheme for providing end-to-end fault-tolerance on large networks. The scheme improves routing performance while keeping network latency low. We also describe the novel routing component, RN1, which implements this scheme, showing how it can be the basic building block for fault-tolerant multistage routing networks.

The Effect of Indexing on the Complexity of Object Recognition

Many current recognition systems use constrained search to locate objects in cluttered environments. Previous formal analysis has shown that the expected amount of search is quadratic in the number of model and data features, if all the data is known to come from a sinlge object, but is exponential when spurious data is included. If one can group the data into subsets likely to have come from a single object, then terminating the search once a "good enough" interpretation is found reduces the expected search to cubic. Without successful grouping, terminated search is still exponential. These results apply to finding instances of a known object in the data. In this paper, we turn to the problem of selecting models from a library, and examine the combinatorics of determining that a candidate object is not present in the data. We show that the expected search is again exponential, implying that naﶥ approaches to indexing are likely to carry an expensive overhead, since an exponential amount of work is needed to week out each of the incorrect models. The analytic results are shown to be in agreement with empirical data for cluttered object recognition.

The Behavior Language; User's Guide

The Behavior Language is a rule-based real-time parallel robot programming language originally based on ideas from [Brooks 86], [Connell 89], and [Maes 89]. It compiles into a modified and extended version of the subsumption architecture [Brooks 86] and thus has backends for a number of processors including the Motorola 68000 and 68HCll, the Hitachi 6301, and Common Lisp. Behaviors are groups of rules which are activatable by a number of different schemes. There are no shared data structures across behaviors, but instead all communication is by explicit message passing. All rules are assumed to run in parallel and asynchronously. It includes the earlier notions of inhibition and suppression, along with a number of mechanisms for spreading of activation.

Olympic Robot Building Manual

The 1989 AI Lab Winter Olympics will take a slightly different twist from previous Olympiads. Although there will still be a dozen or so athletic competitions, the annual talent show finale will now be a display not of human talent, but of robot talent. Spurred on by the question, "Why aren't there more robots running around the AI Lab?", Olympic Robot Building is an attempt to teach everyone how to build a robot and get them started. Robot kits will be given out the last week of classes before the Christmas break and teams have until the Robot Talent Show, January 27th, to build a machine that intelligently connects perception to action. There is no constraint on what can be built; participants are free to pick their own problems and solution implementations. As Olympic Robot Building is purposefully a talent show, there is no particular obstacle course to be traversed or specific feat to be demonstrated. The hope is that this format will promote creativity, freedom and imagination. This manual provides a guide to overcoming all the practical problems in building things. What follows are tutorials on the components supplied in the kits: a microprocessor circuit "brain", a variety of sensors and motors, a mechanical building block system, a complete software development environment, some example robots and a few tips on debugging and prototyping. Parts given out in the kits can be used, ignored or supplemented, as the kits are designed primarily to overcome the intertia of getting started. If all goes well, then come February, there should be all kinds of new members running around the AI Lab!

Repairing Learned Knowledge Using Experience

Explanation-based learning occurs when something useful is retained from an explanation, usually an account of how some particular problem can be solved given a sound theory. Many real-world explanations are not based on sound theory, however, and wrong things may be learned accidentally, as subsequent failures will likely demonstrate. In this paper, we describe ways to isolate the facts that cause failures, ways to explain why those facts cause problems, and ways to repair learning mistakes. In particular, our program learns to distinguish pails from cups after making a few mistakes.

The Iterate Manual

This is the manual for version 1.1 of Iterate, a powerful iteration macro for Common Lisp. Iterate is similar to Loop but provides numerous additional features, is well integrated with Lisp, and is extensible.