A Semantic Future for AI

Computing must become much more human centered--for example, by presenting personalized information to users and by respecting personal preferences when controlling multiple devices or invoking various services. Appropriate representation of the information's semantics and of the functionality of devices and services will be critical to such personalized computing. Symbolic artificial intelligence techniques provide the method of choice for the required semantic-representation and reasoning capabilities.     

Kinematic motion model for jumping scout robots

/sup T/he University of Minnesota's Scout is a small cylindrical robot capable of rolling and jumping. Models describing the robot's motion are developed. These models can be employed for motion prediction and simulation. The results suggest that the determining factor of the Scout's behavior is the length of the winch cable.     

Representation as a fluent: an AI challenge for the next half century

The authors argued that AI systems must be able to manipulate their own internal representations automatically to deal with an infinitely complex and ever-changing world and to scale up to rich, complex applications. Such manipulation must go beyond changing beliefs and learning new concepts in terms of old concepts; it must be able to change ontology's underlying syntax and semantics. Initial progress has been made, but further progress is urgently needed owing to the demands of autonomous multiagent systems. Understanding and implementing this ability must be a major focus of AI for the next 50 years.     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.     

Mechanical behavior of 30–50 nm thick aluminum films under uniaxial tension

We present uniaxial tensile test results for 30–50 nm thick freestanding aluminum films. Young’s modulus and ductility were found to decrease monotonically with grain size. Reverse Hall–Petch behavior was observed with no appreciable room temperature creep. Non-linear elasticity with small irreversible deformation was observed for 50 nm thick specimens.     

DEPOSITIONAL AND DIAGENETIC HISTORY OF THE KERENDAN CARBONATE PLATFORM, OLIGOCENE, CENTRAL KALIMANTAN, INDONESIA

The Kerendan carbonate platform (Oligocene Berai Limestone) covers a subsurface area measuring approximately 11 by 16 km in the westernmost Kutei Basin, Central Kalimantan. Aggradation of the Kerendan platform occurred during a major Oligocene transgression, and is contemporaneous with aggradation and backstepping of the Barito shelf margin which was located approximately 30 km to the south. The Kerendan platform is approximately 1,000 m thick, and comprises three aggrading seismic sequences identified by the downlap of basinal strata at the platform margin and downlap of transgressive strata within the platform. Carbonate deposition started in the Late Eocene, and ended when the upper limestone sequence drowned and was covered with shale in the Late Oligocene (approximately 28.6 Ma). Three depositional areas can be distinguished in seismic sections: (1) a platform interior (lagoon); (2) a slightly raised platform rim (1–2 km wide); and (3) a basinward‐dipping platform margin and slope. The margin of the platform is identified by inflections on the seismic profiles where the relatively flat platform top begins to slope basinward. Depositional models from outcrops combined with core from three Kerendan wells were used to extrapolate depositional facies onto the seismically‐defined platform. Platform‐interior (lagoon) facies consist largely of fossiliferous wackestones and packstones, and porosities are generally lower than 5%. The platform rim is characterized by interbedded bioclastic wackestones, packstones, grainstones and boundstones, with grainstones increasing toward the platform margin. Porosity preferentially occurs in packstones, grainstones and boundstones. The platform‐rim deposits have greater porosity (5–13%) than the platform interior because the platform run is more grainstone‐rich, and because acidic waters compacting out of basinal shales concentrated dissolution near the platform margin. Pore types include vuggy porosity, microporosity within grains, and intercrystalline porosity in dolomite in the upper part of the platform rim. Different pore types have resulted in variable but locally very high permeabilities (greater than 100 mD). Permeabilities decrease with depth as vuggy pores decrease, and microporosity becomes dominant.