Dependency-Directed Localization of Software Bugs

Software bugs are violated specifications. Debugging is the process that culminates in repairing a program so that it satisfies its specification. An important part of debugging is localization, whereby the smallest region of the program that manifests the bug is found. The Debugging Assistant (DEBUSSI) localizes bugs by reasoning about logical dependencies. DEBUSSI manipulates the assumptions that underlie a bug manifestation, eventually localizing the bug to one particular assumption. At the same time, DEBUSSI acquires specification information, thereby extending its understanding of the buggy program. The techniques used for debugging fully implemented code are also appropriate for validating partial designs.

Hypothesizing Device Mechanisms: Opening Up the Black Box

I describe an approach to forming hypotheses about hidden mechanism configurations within devices given external observations and a vocabulary of primitive mechanisms. An implemented causal modelling system called JACK constructs explanations for why a second piece of toast comes out lighter, why the slide in a tire gauge does not slip back inside when the gauge is removed from the tire, and how in a refrigerator a single substance can serve as a heat sink for the interior and a heat source for the exterior. I report the number of hypotheses admitted for each device example, and provide empirical results which isolate the pruning power due to different constraint sources.

Automatic Qualitative Analysis of Ordinary Differential Equations Using Piecewise Linear Approximations

This paper explores automating the qualitative analysis of physical systems. It describes a program, called PLR, that takes parameterized ordinary differential equations as input and produces a qualitative description of the solutions for all initial values. PLR approximates intractable nonlinear systems with piecewise linear ones, analyzes the approximations, and draws conclusions about the original systems. It chooses approximations that are accurate enough to reproduce the essential properties of their nonlinear prototypes, yet simple enough to be analyzed completely and efficiently. It derives additional properties, such as boundedness or periodicity, by theoretical methods. I demonstrate PLR on several common nonlinear systems and on published examples from mechanical engineering.

Three-Dimensional Recognition of Solid Objects from a Two-Dimensional Image

This thesis addresses the problem of recognizing solid objects in the three-dimensional world, using two-dimensional shape information extracted from a single image. Objects can be partly occluded and can occur in cluttered scenes. A model based approach is taken, where stored models are matched to an image. The matching problem is separated into two stages, which employ different representations of objects. The first stage uses the smallest possible number of local features to find transformations from a model to an image. This minimizes the amount of search required in recognition. The second stage uses the entire edge contour of an object to verify each transformation. This reduces the chance of finding false matches.

Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.

Control of Vibration in Mechanical Systems Using Shaped Reference Inputs

Dynamic systems which undergo rapid motion can excite natural frequencies that lead to residual vibration at the end of motion. This work presents a method to shape force profiles that reduce excitation energy at the natural frequencies in order to reduce residual vibration for fast moves. Such profiles are developed using a ramped sinusoid function and its harmonics, choosing coefficients to reduce spectral energy at the natural frequencies of the system. To improve robustness with respect to parameter uncertainty, spectral energy is reduced for a range of frequencies surrounding the nominal natural frequency. An additional set of versine profiles are also constructed to permit motion at constant speed for velocity-limited systems. These shaped force profiles are incorporated into a simple closed-loop system with position and velocity feedback. The force input is doubly integrated to generate a shaped position reference for the controller to follow. This control scheme is evaluated on the MIT Cartesian Robot. The shaped inputs generate motions with minimum residual vibration when actuator saturation is avoided. Feedback control compensates for the effect of friction Using only a knowledge of the natural frequencies of the system to shape the force inputs, vibration can also be attenuated in modes which vibrate in directions other than the motion direction. When moving several axes, the use of shaped inputs allows minimum residual vibration even when the natural frequencies are dynamically changing by a limited amount.

Residual Vibration Reduction in Computer Controlled Machines

Control of machines that exhibit flexibility becomes important when designers attempt to push the state of the art with faster, lighter machines. Three steps are necessary for the control of a flexible planet. First, a good model of the plant must exist. Second, a good controller must be designed. Third, inputs to the controller must be constructed using knowledge of the system dynamic response. There is a great deal of literature pertaining to modeling and control but little dealing with the shaping of system inputs. Chapter 2 examines two input shaping techniques based on frequency domain analysis. The first involves the use of the first deriviate of a gaussian exponential as a driving function template. The second, acasual filtering, involves removal of energy from the driving functions at the resonant frequencies of the system. Chapter 3 presents a linear programming technique for generating vibration-reducing driving functions for systems. Chapter 4 extends the results of the previous chapter by developing a direct solution to the new class of driving functions. A detailed analysis of the new technique is presented from five different perspectives and several extensions are presented. Chapter 5 verifies the theories of the previous two chapters with hardware experiments. Because the new technique resembles common signal filtering, chapter 6 compares the new approach to eleven standard filters. The new technique will be shown to result in less residual vibrations, have better robustness to system parameter uncertainty, and require less computation than other currently used shaping techniques.

Computation and Pre-Parametric Design

My work is broadly concerned with the question "How can designs bessynthesized computationally?" The project deals primarily with mechanical devices and focuses on pre-parametric design: design at the level of detail of a blackboard sketch rather than at the level of detail of an engineering drawing. I explore the project ideas in the domain of single-input single-output dynamic systems, like pressure gauges, accelerometers, and pneumatic cylinders. The problem solution consists of two steps: 1) generate a schematic description of the device in terms of idealized functional elements, and then 2) from the schematic description generate a physical description.

Theories of Comparative Analysis

Comparative analysis is the problem of predicting how a system will react to perturbations in its parameters, and why. For example, comparative analysis could be asked to explain why the period of an oscillating spring/block system would increase if the mass of the block were larger. This thesis formalizes the task of comparative analysis and presents two solution techniques: differential qualitative (DQ) analysis and exaggeration. Both techniques solve many comparative analysis problems, providing explanations suitable for use by design systems, automated diagnosis, intelligent tutoring systems, and explanation based generalization. This thesis explains the theoretical basis for each technique, describes how they are implemented, and discusses the difference between the two. DQ analysis is sound; it never generates an incorrect answer to a comparative analysis question. Although exaggeration does occasionally produce misleading answers, it solves a larger class of problems than DQ analysis and frequently results in simpler explanations.

Heuristics for Job-Shop Scheduling

Two methods of obtaining approximate solutions to the classic General Job-shop Scheduling Program are investigated. The first method is iterative. A sampling of the solution space is used to decide which of a collection of space pruning constraints are consistent with "good" schedules. The selected space pruning constraints are then used to reduce the search space and the sampling is repeated. This approach can be used either to verify whether some set of space pruning constraints can prune with discrimination or to generate solutions directly. Schedules can be represented as trajectories through a Cartesian space. Under the objective criteria of Minimum maximum Lateness family of "good" schedules (trajectories) are geometric neighbors (reside with some "tube") in this space. This second method of generating solutions takes advantage of this adjacency by pruning the space from the outside in thus converging gradually upon this "tube." One the average this methods significantly outperforms an array of the Priority Dispatch rules when the object criteria is that of Minimum Maximum Lateness. It also compares favorably with a recent relaxation procedure.

Contact Sensors for Dexterous Robotic Hands

This thesis examines a tactile sensor and a thermal sensor for use with the Utah-MIT dexterous four fingered hand. Sensory feedback is critical or full utilization of its advanced manipulatory capabilities. The hand itself provides tendon tensions and joint angles information. However, planned control algorithms require more information than these sources can provide. The tactile sensor utilizes capacitive transduction with a novel design based entirely on silicone elastomers. It provides an 8 x 8 array of force cells with 1.9 mm center-to-center spacing. A pressure resolution of 8 significant bits is available over a 0 to 200 grams per square mm range. The thermal sensor measures a material's heat conductivity by radiating heat into an object and measuring the resulting temperature variations. This sensor has a 4 x 4 array of temperature cells with 3.5 mm center-to-center spacing. Experiments show that the thermal sensor can discriminate among material by detecting differences in their thermal conduction properties. Both sensors meet the stringent mounting requirements posed by the Utah-MIT hand. Combining them together to form a sensor with both tactile and thermal capabilities will ultimately be possible. The computational requirements for controlling a sensor equipped dexterous hand are severe. Conventional single processor computers do not provide adequate performance. To overcome these difficulties, a computational architecture based on interconnecting high performance microcomputers and a set of software primitives tailored for sensor driven control has been proposed. The system has been implemented and tested on the Utah-MIT hand. The hand, equipped with tactile and thermal sensors and controlled by its computational architecture, is one of the most advanced robotic manipulatory devices available worldwide. Other ongoing projects will exploit these tools and allow the hand to perform tasks that exceed the capabilities of current generation robots.

The Synthesis of Stable Force-Closure Grasps

This thesis addresses the problem of synthesizing grasps that are force-closure and stable. The synthesis of force-closure grasps constructs independent regions of contact for the fingertips, such that the motion of the grasped object is totally constrained. The synthesis of stable grasps constructs virtual springs at the contacts, such that the grasped object is stable, and has a desired stiffness matrix about its stable equilibrium. A grasp on an object is force-closure if and only if we can exert, through the set of contacts, arbitrary forces and moments on the object. So force-closure implies equilibrium exists because zero forces and moment is spanned. In the reverse direction, we prove that a non-marginal equilibrium grasp is also a force-closure grasp, if it has at least two point contacts with friction in 2D, or two soft-finger contacts or three hard-finger contacts in 3D. Next, we prove that all force-closure grasps can be made stable, by using either active or passive springs at the contacts. The thesis develops a simple relation between the stability and stiffness of the grasp and the spatial configuration of the virtual springs at the contacts. The stiffness of the grasp depends also on whether the points of contact stick, or slide without friction on straight or curved surfaces of the object. The thesis presents fast and simple algorithms for directly constructing stable fore-closure grasps based on the shape of the grasped object. The formal framework of force-closure and stable grasps provides a partial explanation to why we stably grasp objects to easily, and to why our fingers are better soft than hard.

Learning by Failing to Explain

Explanation-based Generalization requires that the learner obtain an explanation of why a precedent exemplifies a concept. It is, therefore, useless if the system fails to find this explanation. However, it is not necessary to give up and resort to purely empirical generalization methods. In fact, the system may already know almost everything it needs to explain the precedent. Learning by Failing to Explain is a method which is able to exploit current knowledge to prune complex precedents, isolating the mysterious parts of the precedent. The idea has two parts: the notion of partially analyzing a precedent to get rid of the parts which are already explainable, and the notion of re-analyzing old rules in terms of new ones, so that more general rules are obtained.

Error Detection and Recovery for Robot Motion Planning with Uncertainty

Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions.

TYPICAL: A Knowledge Representation System for Automated Discovery and Inference

TYPICAL is a package for describing and making automatic inferences about a broad class of SCHEME predicate functions. These functions, called types following popular usage, delineate classes of primitive SCHEME objects, composite data structures, and abstract descriptions. TYPICAL types are generated by an extensible combinator language from either existing types or primitive terminals. These generated types are located in a lattice of predicate subsumption which captures necessary entailment between types; if satisfaction of one type necessarily entail satisfaction of another, the first type is below the second in the lattice. The inferences make by TYPICAL computes the position of the new definition within the lattice and establishes it there. This information is then accessible to both later inferences and other programs (reasoning systems, code analyzers, etc) which may need the information for their own purposes. TYPICAL was developed as a representation language for the discovery program Cyrano; particular examples are given of TYPICAL's application in the Cyrano program.