A Boltzmann machine for the organization of intelligent machines

In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine

All-Optical Programmable Disaggregated Data Centre Network realized by FPGA-based Switch and Interface Card

This paper reports an FPGA-based switch and interface card (SIC) and its application scenario in an all-optical, programmable disaggregated data center network (DCN). Our novel SIC is designed and implemented to replace traditional optical network interface cards, plugged into the server directly, supporting optical packet switching (OPS)/optical circuit switching (OCS) or time division multiplexing (TDM)/wavelength division multiplexing (WDM) traffic on demand. Placing the SIC in each server/blade, we eliminate electronics from the top of rack (ToR) switch by pushing all the functionality on each blade while enabling direct intrarack blade-to-blade communication to deliver ultralow chip-to-chip latency. We demonstrate the disaggregated DCN architecture scenarios along with all-optical dimension-programmable N × M spectrum selective Switches (SSS) and an architecture-on-demand (AoD) optical backplane. OPS and OCS complement each other as do TDM and WDM, which can support variable traffic flows. A flat disaggregated DCN architecture is realized by connecting the optical ToR switches directly to either an optical top of cluster switch or the intracluster AoD optical backplane, while clusters are further interconnected to an intercluster AoD for scaling out

Stochastic processes, estimation, and control :The entopy approach /

Indeksxii, 230 hlm. : il. ; 23 cm

Stochastic processes, estimation, and control : The entopy approach

Indeks Bibliografi hlm. Setiap babxii, 230 hlm. :il. ;23 cm

Using Stochastic Grammars to Learn Robotic Tasks

. The paper introduces a reinforcement learning-based methodology for performance improvement of Intelligent Controllers. The translation interfaces of a 3-level Hierarchical Goal-Directed Intelligent Machine (HGDIM) are modeled by a 2-stage Hierarchical Learning Stochastic Automaton (HLSA). The decision probabilities at the two stages are recursively updated from the success and failure signals received by the bottom stage whenever a primitive action of the HGDIM is applied to the environment where the machine operates. The top translation stage and the use of regular stochastic grammars to accomplish the translation of commands into tasks are described here. Under this framework, subsets of conflicting grammar productions represent different task strategies to accomplish a command. At this stage, an LSA is associated to each subset of conflicting grammar productions. Results of simulations show the application of the methodology to an Intelligent Robotic System. 1 Introduction Inte..