Predictive monitoring research: Summary of the PREMON system

Traditional approaches to monitoring are proving inadequate in the face of two important issues: the dynamic adjustment of expectations about sensor values when the behavior of the device is too complex to enumerate beforehand, and the selective but effective interpretation of sensor readings when the number of sensors becomes overwhelming. This system addresses these issues by building an explicit model of a device and applying common-sense theories of physics to model causality in the device. The resulting causal simulation of the device supports planning decisions about how to efficiently yet reliably utilize a limited number of sensors to verify correct operation of the device

Learning in Tele-autonomous Systems using Soar

Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques

The Classification, Detection and Handling of Imperfect Theory Problems

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOffice of Naval Research / N00014-86-K-030

Robo-Soar: An Integration of External Interaction, Planning, and Learning using Soar

This chapter reports progress in extending the Soar architecture to tasks that involve interaction with external environments. The tasks are performed using a Puma arm and a camera in a system called Robo-Soar. The tasks require the integration of a variety of capabilities including problem solving with incomplete knowledge, reactivity, planning, guidance from external advice, and learning to improve the efficiency and correctness of problem solving. All of these capabilities are achieved without the addition of special purpose modules or subsystems to Soar

Extending Explanation-Based Learning: Failure-Driven Schema Refinement

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryOffice of Naval Research / N00014-86-K-030

Analyzing Variable Cancellations to Generalize Symbolic Mathematical Calculations

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / NSF IST 85-1154

On the efficiency of meta-level inference

In this thesis we will be concerned with a particular type of architecture for reasoning systems, known as meta-level architectures. After presenting the arguments for such architectures (chapter 1), we discuss a number of systems in the literature that provide an explicit meta-level architecture (chapter 2), and these systems are compared on the basis of a number of distinguishing characteristics. This leads to a classification of meta-level architectures (chapter 3). Within this classification we compare the different types of architectures, and argue that one of these types, called bilingual meta-level inference systems, has a number of advantages over the other types. We study the general structure of bilingual meta-level inference architectures (chapter 4), and we discuss the details of a system that we implemented which has this architecture (chapter 5). One of the problems that this type of system suffers from is the overhead that is incurred by the meta-level effort. We give a theoretical model of this problem, and we perform measurements which show that this problem is indeed a significant one (chapter 6). Chapter 7 discusses partial evaluation, the main technique available in the literature to reduce the meta-level overhead. This technique, although useful, suffers from a number of serious problems. We propose two further techniques, partial reflection and many-sorted logic (chapters 8 and 9), which can be used to reduce the problem of meta-level overhead without suffering from these problems

Proceedings of the NASA Conference on Space Telerobotics, volume 3

The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Compilation-based performance improvement for generative planners

When input a model of a planning domain and a problem to be solved, generative planning systems endeavour to generate a plan that is a solution to the input problem. There is a trade-off between the generality and efficiency of these systems which means that general planners tend to be inefficient. Our research has focussed on improving the efficiency of general planners. A popular approach to tackling this “efficiency versus generality” problem has been to embed systems with on-line and post-event learning components that use current and previous planning experience to increase subsequent performance. However, in this thesis we argue for a move away from this approach towards what we call off-line compilation, where all processing is independent of on-line planning. In support of this we have created two novel complementary techniques for off-line compilation, which we call PRECEDE and PROPOSE. In the course of our work they have been precisely defined, implemented and evaluated, and all aspects of this work are described in the thesis. PRECEDE is a method for generating goal orders that are based on necessary interactions between goals. PROPOSE is a method for generating iterative macro operators for all sorts of objects in a modelled planning domain, ft also generates sequences of constants that can be used to unwind the iterative parts of the macros. The PRE-CEDE goal orders can be used to select goals to achieve next and the PROPOSE iterative macros and enumerated sequences can be used to select ground operators for goal achievement during plan generation. These methods are off-line in the sense that all processing is performed a priori direct from the model of a planning domain. The compilation methods were evaluated statically, empirically and in comparison with established methods of on-line and post-event learning. For the empirical testing of off-line compilation an example planner was selected and extended so that it could use domain models that had been compiled by PRECEDE and PROPOSE. This planner was then used in a series of empirical tests which revealed that the techniques, both individually and in combination, consistently improve the performance of the planner (a speed-up of between 2 and 5 times faster across a sample of planning domains). Also, the results of static and dynamic comparison with established methods show that off-line compilation with PRECEDE and PROPOSE compares favourably with existing post-event and on-line learning methods