The two well known learning algorithms of recurrent neural networks are the back-propagation (Rumelhart & el al., Werbos) and the forward propagation (Williamsand Zipser). The main drawback of back-propagation is its off-line backward path in time for error cumulation. This violates the online requirement in many practical applications. Although the forward propagation algorithmcan be used in an online manner, the annoying drawback is the heavy computation load required to update the high dimensional sensitivity matrix(0(fir) operations for each time step). Therefore, to develop a fast forward algorithm is a challenging task.

Allen Newell was one of the founders and truly great scientists of AI. His contributions included foundational concepts and ground-breaking systems. His career was defined by the pursuit of a single, fundamental issue: the nature of the human mind. This article traces his pursuit from his early work on search and list processing in systems such as the LOGIC THEORIST and the GENERAL PROBLEM SOLVER; through his work on problem spaces, human problem solving, and production systems; through his final work on unified theories of cognition and SOAR.

An effective to develop an abstract UI model which solution to this problem must have three encompasses the essence of those systems.

AI research is conducted at a number of academic and research units at the Georgia Institute of Technology. Some of this research is basic in nature, and some has an applied character to it. This article briefly describes basic AI research in the College of Computing at Georgia Tech.

Advocates and critics of AI have long engaged in a debate that has generated a great deal of heat but little light. Whatever the merits of specific contributions to this ongoing debate, the fact that it continues points to the need for a reflective examination of the foundations of AI by its active practitioners. Following the lead of Earl MacCormac, we hope to advance such a reflective examination by considering questions of metaphor in science and the computational metaphor in AI. Specifically, we address three issues: the role of metaphor in science and AI, an examination of the computational metaphor, and an introduction to the possibility and potential value of using alternative metaphors as a foundation for AI theory.

The VLSI cell placement problem is known to be NP-complete. This paper presents a survey of the various approaches and techniques for this problem. It also gives a comprehensive tutorial on the subject, providing an excellent introduction to the terminology and classification of placement algorithms. With the growing diversity of the terms appearing in the literature, I found the explicit warning about synonymous usage of words like module, cell, and element or net, wire, and interconnect to be helpful. The placement algorithms whose emphasis is on standard cell and macro placement fall into five groups, according to their underlying technique: (1) simulated annealing, (2) force-directed, (3) minimum-cut, (4) numerical optimization, and (5) evolution based. The origins of the first two are in physical laws.

Truth maintenance is a collection of techniques for doing belief revision. A truth maintenance system's task is to maintain a set of beliefs in such a way that they are not known to be contradictory and no belief is kept without a reason. Truth maintenance systems were introduced in the late seventies by Jon Doyle and in the last five years there has been an explosion of interest in this kind of systems. In this paper we present an annotated bibliography to the literature of truth maintenance systems, grouping the works referenced according to several classifications.

Automated knowledge-acquisition systems have focused on embedding a cognitive model of a key knowledge worker in their software that allows the system to acquire a knowledge base by interviewing domain experts just as the knowledge worker would. Two sets of research questions arise: (1) What theories, strategies, and approaches will let the modeling process be facilitated; accelerated; and, possibly, automated? If automated knowledge-acquisition systems reduce the bottleneck associated with acquiring knowledge bases, how can the bottleneck of building the automated knowledge-acquisition system itself be broken? (2) If the automated knowledge-acquisition system centers on having an effective cognitive model of the key knowledge worker(s), to what extent does this model account for and attempt to influence human bias in knowledge base rule generation? That is, humans are known to be subject to errors and cognitive biases in their judgment processes. How can an automated system critique and influence such biases in a positive fashion, what common patterns exist across applications, and can models of influencing behavior be described and standardized? This article answers these research questions by presenting several prototypical scenes depicting bias and debiasing strategies.

This article reviews research in the development of plan generation systems. Our goal is to familiarize the reader with some of the important problems that have arisen in the design of planning systems and to discuss some of the many solutions that have been developed in the over 30 years of research in this area. In this article, we broadly cover the major ideas in the field of AI planning and show the direction in which some current research is going. We define some of the terms commonly used in the planning literature, describe some of the basic issues coming from the design of planning systems, and survey results in the area. Because such tasks are virtually never ending, and thus, any finite document must be incomplete, we provide references to connect each idea to the appropriate literature and allow readers access to the work most relevant to their own research or applications.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd and Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.