Taxonomic Syntax for First-Order Inference

Most knowledge representation languages are based on classes and taxonomic relationships between classes. Taxonomic hierarchies without defaults or exceptions are semantically equivalent to a collection of formulas in first order predicate calculus. Although designers of knowledge representation languages often express an intuitive feeling that there must be some advantage to representing facts as taxonomic relationships rather than first order formulas, there are few, if any, technical results supporting this intuition. We attempt to remedy this situation by presenting a taxonomic syntax for first order predicate calculus and a series of theorems that support the claim that taxonomic syntax is superior to classical syntax.

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Gait Dynamics for Recognition and Classification

This paper describes a representation of the dynamics of human walking action for the purpose of person identification and classification by gait appearance. Our gait representation is based on simple features such as moments extracted from video silhouettes of human walking motion. We claim that our gait dynamics representation is rich enough for the task of recognition and classification. The use of our feature representation is demonstrated in the task of person recognition from video sequences of orthogonal views of people walking. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times, and under varying lighting environments. In addition, preliminary results are shown on gender classification using our gait dynamics features.

Tense, Aspect and the Cognitive Representation of Time

This paper explores the relationships between a computation theory of temporal representation (as developed by James Allen) and a formal linguistic theory of tense (as developed by Norbert Hornstein) and aspect. It aims to provide explicit answers to four fundamental questions: (1) what is the computational justification for the primitive of a linguistic theory; (2) what is the computational explanation of the formal grammatical constraints; (3) what are the processing constraints imposed on the learnability and markedness of these theoretical constructs; and (4) what are the constraints that a linguistic theory imposes on representations. We show that one can effectively exploit the interface between the language faculty and the cognitive faculties by using linguistic constraints to determine restrictions on the cognitive representation and vice versa. Three main results are obtained: (1) We derive an explanation of an observed grammatical constraint on tense?? Linear Order Constraint??m the information monotonicity property of the constraint propagation algorithm of Allen's temporal system: (2) We formulate a principle of markedness for the basic tense structures based on the computational efficiency of the temporal representations; and (3) We show Allen's interval-based temporal system is not arbitrary, but it can be used to explain independently motivated linguistic constraints on tense and aspect interpretations. We also claim that the methodology of research developed in this study??oss-level" investigation of independently motivated formal grammatical theory and computational models??a powerful paradigm with which to attack representational problems in basic cognitive domains, e.g., space, time, causality, etc.

PILOT: A Step Toward Man-Computer Symbiosis

PILOT is a programming system constructed in LISP. It is designed to facilitate the development of programs by easing the familiar sequence: write some code, run the program, make some changes, write some more code, run the program again, etc. As a program becomes more complex, making these changes becomes harder and harder because the implications of changes are harder to anticipate. In the PILOT system, the computer plays an active role in this evolutionary process by providing the means whereby changes can be effected immediately, and in ways that seem natural to the user. The user of PILOT feels that he is giving advice, or making suggestions, to the computer about the operation of his programs, and that the system then performs the work necessary. The PILOT system is thus an interface between the user and his program, monitoring both in the requests of the user and operation of his program. The user may easily modify the PILOT system itself by giving it advice about its own operation. This allows him to develop his own language and to shift gradually onto PILOT the burden of performing routine but increasingly complicated tasks. In this way, he can concentrate on the conceptual difficulties in the original problem, rather than on the niggling tasks of editing, rewriting, or adding to his programs. Two detailed examples are presented. PILOT is a first step toward computer systems that will help man to formulate problems in the same way they now help him to solve them. Experience with it supports the claim that such "symbiotic systems" allow the programmer to attack and solve more difficult problems.

Natural Object Categorization

This thesis addresses the problem of categorizing natural objects. To provide a criteria for categorization we propose that the purpose of a categorization is to support the inference of unobserved properties of objects from the observed properties. Because no such set of categories can be constructed in an arbitrary world, we present the Principle of Natural Modes as a claim about the structure of the world. We first define an evaluation function that measures how well a set of categories supports the inference goals of the observer. Entropy measures for property uncertainty and category uncertainty are combined through a free parameter that reflects the goals of the observer. Natural categorizations are shown to be those that are stable with respect to this free parameter. The evaluation function is tested in the domain of leaves and is found to be sensitive to the structure of the natural categories corresponding to the different species. We next develop a categorization paradigm that utilizes the categorization evaluation function in recovering natural categories. A statistical hypothesis generation algorithm is presented that is shown to be an effective categorization procedure. Examples drawn from several natural domains are presented, including data known to be a difficult test case for numerical categorization techniques. We next extend the categorization paradigm such that multiple levels of natural categories are recovered; by means of recursively invoking the categorization procedure both the genera and species are recovered in a population of anaerobic bacteria. Finally, a method is presented for evaluating the utility of features in recovering natural categories. This method also provides a mechanism for determining which features are constrained by the different processes present in a multiple modal world.

View-Based Strategies for 3D Object Recognition

A persistent issue of debate in the area of 3D object recognition concerns the nature of the experientially acquired object models in the primate visual system. One prominent proposal in this regard has expounded the use of object centered models, such as representations of the objects' 3D structures in a coordinate frame independent of the viewing parameters [Marr and Nishihara, 1978]. In contrast to this is another proposal which suggests that the viewing parameters encountered during the learning phase might be inextricably linked to subsequent performance on a recognition task [Tarr and Pinker, 1989; Poggio and Edelman, 1990]. The 'object model', according to this idea, is simply a collection of the sample views encountered during training. Given that object centered recognition strategies have the attractive feature of leading to viewpoint independence, they have garnered much of the research effort in the field of computational vision. Furthermore, since human recognition performance seems remarkably robust in the face of imaging variations [Ellis et al., 1989], it has often been implicitly assumed that the visual system employs an object centered strategy. In the present study we examine this assumption more closely. Our experimental results with a class of novel 3D structures strongly suggest the use of a view-based strategy by the human visual system even when it has the opportunity of constructing and using object-centered models. In fact, for our chosen class of objects, the results seem to support a stronger claim: 3D object recognition is 2D view-based.

Face Detection in Still Gray Images

We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.

New Architectural Models for Visibly Controllable Computing: The Relevance of Dynamic Object Oriented Architectures and Plan Based Computing Models

Traditionally, we've focussed on the question of how to make a system easy to code the first time, or perhaps on how to ease the system's continued evolution. But if we look at life cycle costs, then we must conclude that the important question is how to make a system easy to operate. To do this we need to make it easy for the operators to see what's going on and to then manipulate the system so that it does what it is supposed to. This is a radically different criterion for success. What makes a computer system visible and controllable? This is a difficult question, but it's clear that today's modern operating systems with nearly 50 million source lines of code are neither. Strikingly, the MIT Lisp Machine and its commercial successors provided almost the same functionality as today's mainstream sytsems, but with only 1 Million lines of code. This paper is a retrospective examination of the features of the Lisp Machine hardware and software system. Our key claim is that by building the Object Abstraction into the lowest tiers of the system, great synergy and clarity were obtained. It is our hope that this is a lesson that can impact tomorrow's designs. We also speculate on how the spirit of the Lisp Machine could be extended to include a comprehensive access control model and how new layers of abstraction could further enrich this model.

Failure-Oblivious Computing and Boundless Memory Blocks

Memory errors are a common cause of incorrect software execution and security vulnerabilities. We have developed two new techniques that help software continue to execute successfully through memory errors: failure-oblivious computing and boundless memory blocks. The foundation of both techniques is a compiler that generates code that checks accesses via pointers to detect out of bounds accesses. Instead of terminating or throwing an exception, the generated code takes another action that keeps the program executing without memory corruption. Failure-oblivious code simply discards invalid writes and manufactures values to return for invalid reads, enabling the program to continue its normal execution path. Code that implements boundless memory blocks stores invalid writes away in a hash table to return as the values for corresponding out of bounds reads. he net effect is to (conceptually) give each allocated memory block unbounded size and to eliminate out of bounds accesses as a programming error. We have implemented both techniques and acquired several widely used open source servers (Apache, Sendmail, Pine, Mutt, and Midnight Commander).With standard compilers, all of these servers are vulnerable to buffer overflow attacks as documented at security tracking web sites. Both failure-oblivious computing and boundless memory blocks eliminate these security vulnerabilities (as well as other memory errors). Our results show that our compiler enables the servers to execute successfully through buffer overflow attacks to continue to correctly service user requests without security vulnerabilities.