ALBERT

Description: In construction is a knowledge-based system that will aid astronauts in the performance of vestibular experiments in two ways: it will provide real-time monitoring and control of signals and it will optimize the quality of the data obtained, by helping the mission specialists and payload specialists make decisions that are normally the province of a principal investigator, hence the name PI-in-a-box. An important and desirable side-effect of this tool will be to make the astronauts more productive and better integrated members of the scientific team. The vestibular experiments are planned by Prof. Larry Young of MIT, whose team has already performed similar experiments in Spacelab missions SL-1 and D-1, and has experiments planned for SLS-1 and SLS-2. The knowledge-based system development work, performed in collaboration with MIT, Stanford University, and the NASA-Ames Research Center, addresses six major related functions: (1) signal quality monitoring; (2) fault diagnosis; (3) signal analysis; (4) interesting-case detection; (5) experiment replanning; and (6) integration of all of these functions within a real-time data acquisition environment. Initial prototyping work has been done in functions (1) through (4).

Description: Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

Description: Few human endeavors can be viewed both as extremely successful and unsuccessful at the same time. This is typically the case when goals have not been well defined or have been shifting in time. This has certainly been true of Artificial Intelligence (AI). The nature of intelligence has been the object of much thought and speculation throughout the history of philosophy. It is in the nature of philosophy that real headway is sometimes made only when appropriate tools become available. Similarly the computer, coupled with the ability to program (at least in principle) any function, appeared to be the tool that could tackle the notion of intelligence. To suit the tool, the problem of the nature of intelligence was soon sidestepped in favor of this notion: If a probing conversation with a computer could not be distinguished from a conversation with a human, then AI had been achieved. This notion became known as the Turing test, after the mathematician Alan Turing who proposed it in 1950. Conceptually rich and interesting, these early efforts gave rise to a large portion of the field's framework. Key to AI, rather than the 'number crunching' typical of computers until then, was viewed as the ability to manipulate symbols and make logical inferences. To facilitate these tasks, AI languages such as LISP and Prolog were invented and used widely in the field. One idea that emerged and enabled some success with real world problems was the notion that 'most intelligence' really resided in knowledge. A phrase attributed to Feigenbaum, one of the pioneers, was 'knowledge is the power.' With this premise, the problem is shifted from 'how do we solve problems' to 'how do we represent knowledge.' A good knowledge representation scheme could allow one to draw conclusions from given premises. Such schemes took forms such as rules,frames and scripts. It allowed the building of what became known as expert systems or knowledge based systems (KBS).

Description: Perhaps the scarcest resource for manned flight experiments - on Spacelab or on the Space Station Freedom - will continue to be crew time. To maximize the efficiency of the crew and to make use of their abilities to work as scientist collaborators as well as equipment operators, normally requires more training in a wide variety of disciplines than is practical. The successful application of on-board expert systems, as envisioned by the Principal Investigator in a Box program, should alleviate the training bottleneck and provide the astronaut with the guidance and coaching needed to permit him or her to operate an experiment according to the desires and knowledge of the PI, despite changes in conditions. The Protocol Manager module of the system is discussed. The Protocol Manager receives experiment data that has been summarized and categorized by the other modules. The Protocol Manager acts on the data in real-time, by employing expert system techniques. Its recommendations are based on heuristics provided by the Principal Investigator in charge of the experiment. This prototype was developed on a Macintosh II by employing CLIPS, a forward-chaining rule-based system, and HyperCard as an object-oriented user interface builder.

Description: The Astronaut Science Advisor (ASA, also known as Principal-Investigator-in-a-Box) is an advanced engineering effort to apply expert systems technology to experiment monitoring and control. Its goal is to increase the scientific value of information returned from experiments on manned space missions. The first in-space test of the system will be in conjunction with Professor Larry Young's (MIT) vestibulo-ocular 'Rotating Dome' experiment on the Spacelab Life Sciences 2 mission (STS-58) in the Fall of 1993. In a cost-saving effort, off-the-shelf equipment was employed wherever possible. Several modifications were necessary in order to make the system flight-worthy. The software consists of three interlocking modules. A real-time data acquisition system digitizes and stores all experiment data and then characterizes the signals in symbolic form; a rule-based expert system uses the symbolic signal characteristics to make decisions concerning the experiment; and a highly graphic user interface requiring a minimum of user intervention presents information to the astronaut operator. Much has been learned about the design of software and user interfaces for interactive computing in space. In addition, we gained a great deal of knowledge about building relatively inexpensive hardware and software for use in space. New technologies are being assessed to make the system a much more powerful ally in future scientific research in space and on the ground.

Description: Simulation and Artificial Intelligence share a fertile common ground both from a practical and from a conceptual point of view. Strengths and weaknesses of both Knowledge Based System and Modeling and Simulation are examined and three types of systems that combine the strengths of both technologies are discussed. These types of systems are a practical starting point, however, the real strengths of both technologies will be exploited only when they are combined in a common knowledge representation paradigm. From an even deeper conceptual point of view, one might even argue that the ability to reason from a set of facts (i.e., Expert System) is less representative of human reasoning than the ability to make a model of the world, change it as required, and derive conclusions about the expected behavior of world entities. This is a fundamental problem in AI, and Modeling Theory can contribute to its solution. The application of Knowledge Engineering technology to a Distributed Processing Network Simulator (DPNS) is discussed.

Description: Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.

Description: Principal Investigator (PI) in a box is a computer system designed to help optimize the scientific results of experiments that are performed in space. The system will assist the astronaut experimenters in the collection and analysis of experimental data, recognition and pursuit of 'interesting' results, optimal use of the time allocated to the experiment, and troubleshooting of the experiment apparatus. This document discusses the problems that motivate development of 'PI-in-a-box', and presents a high- level system overview and a detailed description of each of the modules that comprise the current version of the system.

Description: On the path from inanimate to animate matter, a key step was the self-organization of molecules into protocells - the earliest ancestors of contemporary cells. Studies of the properties of protocells and the mechanisms by which they maintained themselves and reproduced are an important part of astrobiology. These studies also have the potential to greatly impact research in nanotechnology and computer science. Previous studies of protocells have focussed on self-replication. In these systems, Darwinian evolution occurs through a series of small alterations to functional molecules whose identities are stored. Protocells, however, may have been incapable of such storage. We hypothesize that under such conditions, the replication of functions and their interrelationships, rather than the precise identities of the functional molecules, is sufficient for survival and evolution. This process is called non-genomic evolution. Recent breakthroughs in experimental protein chemistry have opened the gates for experimental tests of non-genomic evolution. On the basis of these achievements, we have developed a stochastic model for examining the evolutionary potential of non-genomic systems. In this model, the formation and destruction (hydrolysis) of bonds joining amino acids in proteins occur through catalyzed, albeit possibly inefficient, pathways. Each protein can act as a substrate for polymerization or hydrolysis, or as a catalyst of these chemical reactions. When a protein is hydrolyzed to form two new proteins, or two proteins are joined into a single protein, the catalytic abilities of the product proteins are related to the catalytic abilities of the reactants. We will demonstrate that the catalytic capabilities of such a system can increase. Its evolutionary potential is dependent upon the competition between the formation of bond-forming and bond-cutting catalysts. The degree to which hydrolysis preferentially affects bonds in less efficient, and therefore less well-ordered, peptides is also critical to evolution of a non-genomic system. Based on these results, a new computational object called a "molnet" is defined. Like a neural network, it is formed of interconnected units that send "signals" to each other. Like molecules, neural networks have a specific function once their structure is defined. The difference between a molnet and traditional neural networks, is that input to molnets is not simply passed along and processed from input to output units, but rather it is utilized to form and break connections(bonds), and thus to form new structures. Molnets represent a powerful tool that can be used to understand the conditions under which chemical systems can form large molecules, such as proteins, and display ever more complex functions. This has direct applications, for example to the design of smart,synthetic fabrics. Additional information is contained in the original.