This paper explores the difference between Connectionist proposals for cognitive architecture and the sorts of models that have traditionally been assumed in cognitive science. We claim that the major distinction is that, while both Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a `language of thought': i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the `systematicity' of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or `abstract neurological') structures in which Classical cognitive architecture is implemented. We survey a number of the standard arguments that have been offered in favor of Connectionism, and conclude that they are coherent only on this interpretation. Connectionist or PDP models are catching on. There are conferences and new books nearly every day, and the popular science press hails this new wave of theorizing as a breakthrough in understanding the mind (a typical example is the article in the May issue of Science 86, called "How we think: A new theory"). There are also, inevitably, descriptions of the emergence of --------------------- 1. This paper is base...

Abstract. An Information Dispersal Algorithm (IDA) is developed that breaks a file F of length L = ( F ( into n pieces F,, 1 5 i 5 n, each of length ( F, 1 = L/m, so that every m pieces suffice for reconstructing F. Dispersal and reconstruction are computationally efficient. The sum of the lengths ( F, 1 is (n/m). L. Since n/m can be chosen to be close to I, the IDA is space eflicient. IDA has numerous applications to secure and reliable storage of information in computer networks and even on single disks, to fault-tolerant and efficient transmission of information in networks, and to communi-cations between processors in parallel computers. For the latter problem provably time-efftcient and highly fault-tolerant routing on the n-cube is achieved, using just constant size buffers. Categories and Subject Descriptors: E.4 [Coding and Information Theory]: nonsecret encoding schemes

This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

Ant algorithms are multi-agent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many types of problems, ranging from the classical traveling salesman

This paper describes how evolutionary techniques of variation and selection can be used to create complex simulated structures, textures, and motions for use in computer graphics and animation. Interactive selection, based on visual perception of procedurally generated results, allows the user to direct simulated evolutions in preferred directions. Several examples using these methods have been implemented and are described. 3D plant structures are grown using fixed sets of genetic parameters. Images, solid textures, and animations are created using mutating symbolic lisp expressions. Genotjps consisting of symbolic expressions are presented as an attempt to surpass the limitations of fixed-length genotypes with predefine expression rules. his proposed that artificial evolution has potential as a powerful tool for achieving flexible complexity with a minimum of user input and knowledge of details. 2

Second generation multicomputers use wormhole routing, allowing a very low channel set-up time and drastically reducing the dependency between network latency and internode distance. Deadlock-free routing strategies have been developed, allowing the implementation of fast hardware routers that reduce the communication bottleneck. Also, adaptive routing algorithms with deadlock-avoidance or deadlockrecovery techniques have been proposed for some topologies, being very effective and outperforming static strategies. This paper develops the theoretical background for the design of deadlock-free adaptive routing algorithms for wormhole networks. Some basic definitions and two theorems are proposed, developing conditions to verify that an adaptive algorithm is deadlock-free, even when there are cycles in the channel dependency graph. Also, two design methodologies are proposed. The first one supplies algorithms with a high degree of freedom, without increasing the number of physical channels...

As the performance of interconnection networks becomes increasingly limited by physical constraints in high-speed multiprocessor systems, the parameters of high-performance network design must be reevaluated, starting with a close examination of assumptions and requirements. This paper models network latency, taking both switch and wire delays into account. A simple closed form expression for contention in buffered, direct networks is derived and is found to agree closely with simulations. The model includes the effects of packet size and communication locality. Network analysis under various constraints (such as fixed bisection width, fixed channel width, and fixed node size) and under different workload parameters (such as packet size, degree of communication locality, and network request rate) reveals that performance is highly sensitive to these constraints and workloads. A twodimensional network has the lowest latency only when switch delays and network contention are ignored, but...

In most parallel random-access machine (P-RAM) models, memory references are assumed to take unit time. In practice, and in theory, certain scan operations, also known as prefix computations, can executed in no more time than these parallel memory references. This paper outline an extensive study of the effect of including in the P-RAM models, such scan operations as unit-time primitives. The study concludes that the primitives improve the asymptotic running time of many algorithms by an O(lg n) factor, greatly simplify the description of many algorithms, and are significantly easier to implement than memory references. We therefore argue that the algorithm designer should feel free to use these operations as if they were as cheap as a memory reference. This paper describes five algorithms that clearly illustrate how the scan primitives can be used in algorithm design: a radix-sort algorithm, a quicksort algorithm, a minimumspanning -tree algorithm, a line-drawing algorithm and a mergi...

The Alewife multiprocessor project focuses on the architecture and design of a large-scale parallel machine. The machine uses a low-dimensional direct interconnection network to provide scalable communication bandwidth, while allowing the exploitation of locality. Despite its distributed-memory architecture, Alewife allows efficient shared-memory programming through a multilayered approach to locality management. A new scalable cache-coherence scheme called LimitLESS directories allows the use of caches for reducing communication latency and network bandwidth requirements. Alewife also employs run-time and compile-time methods for partitioning and placement of data and processes to enhance communication locality. While the above methods attempt to minimize communication latency, communication with distant processors cannot be completely avoided. Alewife's processor, Sparcle, is designed to tolerate these latencies by rapidly switching between threads of computation. This paper describe...