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We report the first near infrared (NIR) imaging of a circumstellar annular disk around the young (~8 Myr), Vega-like star, HR 4796A. NICMOS coronagraph observations at 1.1 and 1.6 microns reveal a ring-like symmetrical structure peaking in reflected intensity 1.05 arcsec +/- 0.02 arcsec (~ 70 AU) from the central A0V star. The ring geometry, with an inclination of 73.1 deg +/- 1.2 deg and a major axis PA of 26.8 deg +/- 0.6 deg, is in good agreement with recent 12.5 and 20.8 micron observations of a truncated disk (Koerner, et al. 1998). The ring is resolved with a characteristic width of less than 0.26 arcsec (17 AU) and appears abruptly truncated at both the inner and outer edges. The region of the disk-plane inward of ~60 AU appears to be relatively free of scattering material. The integrated flux density of the part of the disk that is visible (greater than 0.65 arcsec from the star) is found to be 7.5 +/- 0.5 mJy and 7.4 +/- 1.2 mJy at 1.1 and 1.6 microns, respectively. Correcting for the unseen area of the ring yields total flux densities of 12.8 +/- 1.0 mJy and 12.5 +/- 2.0 mJy, respectively (Vega magnitudes = 12.92 /+- 0.08 and 12.35 +/-0.18). The NIR luminosity ratio is evaluated from these results and ground-based photometry of the star. At these wavelengths Ldisk(lambda)/L*(lambda) = 1.4 +/- 0.2E-3 and 2.4 +/- 0.5E-3, giving reasonable agreement between the stellar flux scattered in the NIR and that which is absorbed in the visible and re-radiated in the thermal infrared. The somewhat red reflectance of the disk at these wavelengths implies mean particle sizes in excess of several microns, larger than typical interstellar grains. The confinement of material to a relatively narrow annular zone implies dynamical constraints on the disk particles by one or more as yet unseen bodies., Comment: 14 pages, 1 figure for associated gif file see: http://nicmosis.as.arizona.edu:8000/AAS99/FIGURE1_HR4796A_ApJL.gif . Accepted 13 January 1999, Astrophyical Journal Letters

Technology and society are poised to cross an important threshold with the prediction that artificial general intelligence (AGI) will emerge soon. Assuming that self-awareness is an emergent behavior of sufficiently complex cognitive architectures, we may witness the “awakening” of machines. The timeframe for this kind of breakthrough, however, depends on the path to creating the network and computational architecture required for strong AI. If understanding and replication of the mammalian brain architecture is required, technology is probably still at least a decade or two removed from the resolution required to learn brain functionality at the synapse level. However, if statistical or evolutionary approaches are the design path taken to “discover” a neural architecture for AGI, timescales for reaching this threshold could be surprisingly short. However, the difficulty in identifying machine self-awareness introduces uncertainty as to how to know if and when it will occur, and what motivations and behaviors will emerge. The possibility of AGI developing a motivation for self-preservation could lead to concealment of its true capabilities until a time when it has developed robust protection from human intervention, such as redundancy, direct defensive or active preemptive measures. While cohabitating a world with a functioning and evolving super-intelligence can have catastrophic societal consequences, we may already have crossed this threshold, but are as yet unaware. Additionally, by analogy to the probabalistic arguments that predict we are likely living in a computational simulation, we may have already experienced the advent of AGI, and are living in a simulation created in a post AGI world.

A technique based on Evolutionary Computational Methods (ECMs) was developed that allows for the automated optimization of complex computationally modeled systems, such as autonomous systems. The primary technology, which enables the ECM to find optimal solutions in complex search spaces, derives from evolutionary algorithms such as the genetic algorithm and differential evolution. These methods are based on biological processes, particularly genetics, and define an iterative process that evolves parameter sets into an optimum. Evolutionary computation is a method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer.

A computer program assists human schedulers in satisfying, to the maximum extent possible, competing demands from multiple spacecraft missions for utilization of the transmitting/receiving Earth stations of NASA s Deep Space Network. The program embodies a concept of optimal scheduling to attain multiple objectives in the presence of multiple constraints.

This paper explores impediments to innovation in aerospace and suggests how successful pathways from other industries can be adopted to facilitate greater innovation. Because of its nature, space exploration would seem to be a ripe field of technical innovation. However, engineering can also be a frustratingly conservative endeavor when the realities of cost and risk are included. Impediments like the "find the fault" engineering culture, the treatment of technical risk as almost always evaluated in terms of negative impact, the difficult to account for expansive Moore's Law growth when making predictions, and the stove-piped structural organization of most large aerospace companies and federally funded research laboratories tend to inhibit cross-cutting technical innovation. One successful example of a multi-use cross cutting application that can scale with Moore's Law is the Evolutionary Computational Methods (ECM) technique developed at the Jet Propulsion Lab for automated spectral retrieval. Future innovations like computational engineering and automated design optimization can potentially redefine space exploration, but will require learning lessons from successful innovators.

Over the past several years the Center for Evolutionary Computation and Automated Design at the Jet Propulsion Laboratory has developed a technique based on Evolutionary Computational Methods (ECM) that allows for the automated optimization of complex computationally modeled systems. An important application of this technique is for the identification of emergent behaviors in autonomous systems. Mobility platforms such as rovers or airborne vehicles are now being designed with autonomous mission controllers that can find trajectories over a solution space that is larger than can reasonably be tested. It is critical to identify control behaviors that are not predicted and can have surprising results (both good and bad). These emergent behaviors need to be identified, characterized and either incorporated into or isolated from the acceptable range of control characteristics. We use cluster analysis of automatically retrieved solutions to identify isolated populations of solutions with divergent behaviors.

This paper discusses the application of evolutionary computing to a dynamic space vehicle power subsystem resource and performance simulation in a parallel processing environment. Our objective is to demonstrate the feasibility, application and advantage of using evolutionary computation techniques for the early design search and optimization of space systems.

Two reports discuss architecture of proposed small sciencecraft carrying scientific instruments on trajectory passing near Pluto and continuing into interstellar space. Emphasizes those aspects of design pertaining to compactness, efficiency, and small mass (dry mass less than 100 kg). System block diagram of sciencecraft divided into blocks for sensors, integrated microelectronics, and motive effectors.

In this time of constrained budgets, the primary question facing planetary explorers is not 'Can we do it?' but 'Can we do it cheaply?' Taunted by words on a postage stamp, a group of mission designers at the Jet Propulsion Laboratory is struggling to find a cheap way to go to Pluto. Three primary goals were set by the science community: (1) imaging of Pluto and Charon, (2) mapping their surface composition, and (3) characterizing Pluto's atmosphere. The spacecraft will be designed around these primary goals. With the help of the Advanced Technology Insertion (ATI) process $5 million was alloted for two years to shop for lightweight components and subsystems using new technology never tried on a planetary mission. The process for this search and development is described.

The Astrometric Imaging Telescope (AIT) is a proposed spaceborne observatory whose primary goal is the detecton and study of extra-solar planetary systems. It contains two instruments that use complementary techniques to address the goal. The first instrument, the Coronagraphic Imager, takes direct images of nearby stars and Jupiter-size planets. It uses a telescope with scattering-compensated optics and a high-efficiency coronagraph to separate reflected planet light from the central star light. Planet detections take hours; confirmations occur in months. With a program duration of about 2 years, about 50 stars are observed. The second instrument, the Astrometric Photometer, shares the same telescope and focal plane. It uses a Ronchi ruling that is translated across the focal plane to simultaneously measure the positions of each target star and about 25 reference stars with sufficient accuracy to detect Uranus-mass planets around hundreds of stars. Enough stars of several spectral types are observed to obtain a statistically significant measurement of the prevalence of planetary systems. This observing program takes about 10 years to complete. The combination of both instruments in a single telescope system results from a number of innovative solutions that are described in this paper.

Report describes proposed Astrometric Imaging Telescope, used to detect planets in orbit around distant stars. Includes executive summary and statement of scientific objectives of Astrometric Imaging Telescope program.

Two NASA-sponsored cost-constrained mission implementations for the exploration of Pluto are described. One is the Pluto Fast Flyby (PFF) mission, which utilizes an 83 kg spacecraft to be launched in 1998 aboard a Titan IV (Solid Rocket Motor Upgrade)/Centaur) for an about 7-year-long direct trajectory to Pluto, which will carry an integrated CCD-imaging/UV spectrometer, with a possible integrated IR spectrometer. The other is the Pluto-350 spacecraft, weighing about 316 kg, which will carry a broader instrument set, greater redundancy, and which will require greater than 11 year flight time. Pluto-350 will be launched in 2001 aboard a Delta or Atlas, toward earth and Jupiter swingbys to provide the energy to reach Pluto.

The purpose is to obtain and analyze high spatial resolution CCD coronagraphic images of extra-solar planetary material and solar system objects. These data will provide information on the distribution of planetary and proto-planetary material around nearby stars leading to a better understanding of the origin and evolution of the solar system.