ALBERT

Description: Significant advances have occurred during the last decade in intelligent systems technologies (a.k.a. knowledge-based systems, KBS) including research, feasibility demonstrations, and technology implementations in operational environments. Evaluation and simulation data obtained to date in real-time operational environments suggest that cost-effective utilization of intelligent systems technologies can be realized for Automated Rendezvous and Capture applications. The successful implementation of these technologies involve a complex system infrastructure integrating the requirements of transportation, vehicle checkout and health management, and communication systems without compromise to systems reliability and performance. The resources that must be invoked to accomplish these tasks include remote ground operations and control, built-in system fault management and control, and intelligent robotics. To ensure long-term evolution and integration of new validated technologies over the lifetime of the vehicle, system interfaces must also be addressed and integrated into the overall system interface requirements. An approach for defining and evaluating the system infrastructures including the testbed currently being used to support the on-going evaluations for the evolutionary Space Station Freedom Data Management System is presented and discussed. Intelligent system technologies discussed include artificial intelligence (real-time replanning and scheduling), high performance computational elements (parallel processors, photonic processors, and neural networks), real-time fault management and control, and system software development tools for rapid prototyping capabilities.

Description: Information on systems autonomy is given in viewgraph form. Information is given on space systems integration, intelligent autonomous systems, automated systems for in-flight mission operations, the Systems Autonomy Demonstration Project on the Space Station Thermal Control System, the architecture of an autonomous intelligent system, artificial intelligence research issues, machine learning, and real-time image processing.

Description: A multiprocessor system, under design for space-station applications, makes use of the latest generation symbolic processor and packaging technology. The result will be a compact, space-qualified system two to three orders of magnitude more powerful than present-day symbolic processing systems.

Description: The concepts and preliminary results for the OASIS testbed which will permit an evolutionary growth with minimum impact to the existing software and hardware environment are presented. Space Station Freedom's data management system is utilized as the application focus for the evaluation of the OASIS concepts. It is concluded that guidelines for the validation and definition of standards for open architectures must be aggressively pursued with attention paid to the integration of these standards with the hardware platforms, graphics interface, and operating system software infrastructure.

Description: Significant research products which have emerged from the core program of NASA's Office of Aeronautics and Space Technology (OAST) are discussed. The Space Station Thermal Control System, the Space Shuttle Integrated Communications Officer Station, the Launch Processing System, the Expert Scheduling System for Pioneer Venus Spacecraft, a Bayesian classification system, and a spaceborne multiprocessor system are included. The technology trends which led to these results are discussed and future developments in technology are forecasted.

Description: Significant advances have occurred during the last decade in knowledge-based engineering research and knowledge-based system (KBS) demonstrations and evaluations using integrated intelligent system technologies. Performance and simulation data obtained to date in real-time operational environments suggest that cost-effective utilization of intelligent system technologies can be realized. In this paper the rationale and potential benefits for typical examples of application projects that demonstrate an increase in productivity through the use of intelligent system technologies are discussed. These demonstration projects have provided an insight into additional technology needs and cultural barriers which are currently impeding the transition of the technology into operational environments. Proposed methods which addresses technology evolution and implementation are also discussed.

Description: The evolution of intelligent computation systems is discussed starting with the Spaceborne VHSIC Multiprocessor System (SVMS). The SVMS is a six-processor system designed to provide at least a 100-fold increase in both numeric and symbolic processing over the i386 uniprocessor. The significant system performance parameters necessary to achieve the performance increase are discussed.

Description: This paper explains how graphical models, for instance Bayesian or Markov networks, can be extended to model problems in data analysis and learning. This provides a unified framework that combines lessons learned from the artificial intelligence, statistical and connectionist communities. This also offers a set of principles for developing a software generator for data analysis, whereby a learning or discovery system can be compiled from specifications. Many of the popular learning algorithms can be compiled in this way from graphical specifications. While in a sense this paper is a multidisciplinary review of learning, the main contribution here is the presentation of the material within the unifying framework of graphical models, and the observation that, as a result, the process of developing learning algorithms can be partly automated.

Description: We describe a Bayesian approach to the untutored discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the "best" set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of a probability distribution or density function, and the locally maximal posterior probability valued function parameters. We rate our classifications with an approximate joint probability of the data and functional form, marginalizing over the parameters. Approximation is necessitated by the computational complexity of the joint probability. Thus, we marginalize w.r.t. local maxima in the parameter space. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model and describe the approximations needed for computational tractability. We instantiate the basic model with the discrete Dirichlet distribution and multivariant Gaussian density likelihoods. Then we show some results for both constructed and actual data.

Description: This paper presents an approach to domain representation and planning that is fundamentally different from traditional methods; an approach based strictly on actions and their interrelationships, rather than on state-based goals and preconditions. In particular, we focus on the action-based planner COLLAGE, describe its methods for plan-construction, and contrast them with more traditional approaches to planning. Experiences with COLLAGE in realistic domains have shown that the action-based approach is not only more natural to use, but can also be more cost-efficient than traditional planning methods.