The Workshop on Integrating Neural and Symbolic Processes (the Cognitive Dimension), sponsored by the Association for the Advancement of Artificial Intelligence, was held on 16 July 1992 at the San Jose Convention Center, San Jose, California. The workshop addressed the cognitive aspects of integrating neural and symbolic processes through the comparison, the categorization, and the examination of existing and new approaches. The workshop attracted a large audience from both academia and industry. The presentation of 16 papers, 3 invited talks, and a summary panel, as well as open discussions, helped to shed much new light on technical issues and future directions in this area.

Sensor abuse is a serious and debilitating condition. However, one must remember that it is a disease, not a crime.1 As such, it can be treated. This article presents a case study in sensor abuse. This particular subject was lucky enough to pull himself out of his pitiful condition, but others are not so lucky. The article also describes a 12- step behavior-modification program modeled on this and other successful case studies.

The camera is mounted on a rotating table that allows it to turn 360 degrees independently of robot motion. Interestingly, the two teams processor (Z80) controls the robot's used vastly different approaches in the design wheel speed and direction. 's software design is hierarchical in The final scores for the robots, based solely structure. At the top level is a supervising on competition-day performance, constitute planning system that decides when to call only a rough evaluation of the merits of the subordinate modules for movement, vision, various systems. This article provides a technical or the recalibration of the robot's position.

This article informally examines the role of stored internal state (that is, memory) in the control of autonomous mobile robots. The difficulties associated with using stored internal state are reviewed. It is argued that the underlying cause of these problems is the implicit predictions contained within the state, and, therefore, many of the problems can be solved by taking care that the internal state contains information only about predictable aspects of the environment. One way of accomplishing this is to maintain internal state only at a high level of abstraction. The resulting information can be used to guide the actions of a robot but should not be used to control these actions directly; local sensor information is still necessary for immediate control. A mechanism to detect and recover from failures is also required. A control architecture embodying these design principles is briefly described. This architecture was successfully used to control real-world and simulated real-world autonomous mobile robots performing complex navigation tasks. The architecture is able to incorporate standard AI planning and world-modeling algorithms into a real-time situated framework.

Although knowledge representation is one of the central and, in some ways, most familiar concepts in AI, the most fundamental question about it -- What is it? -- has rarely been answered directly. Numerous papers have lobbied for one or another variety of representation, other papers have argued for various properties a representation should have, and still others have focused on properties that are important to the notion of representation in general. In this article, we go back to basics to address the question directly. We believe that the answer can best be understood in terms of five important and distinctly different roles that a representation plays, each of which places different and, at times, conflicting demands on the properties a representation should have. We argue that keeping in mind all five of these roles provides a usefully broad perspective that sheds light on some longstanding disputes and can invigorate both research and practice in the field.

Although they then cite a "slow, rigorous proof" by K. Hornik et al. that implies the superiority of neural systems Put our 27 years experience placing technical In practice, since neural nets and Turing-equivalent systems professionals to work for you. If you earn over $35,000, we have a better, more rewarding job for you... right depend on the arithmetic of real numbers rather now. Call (301) 231-9000 or send your resume in infinite precision fashion. These computations can be confidence to: Dept. The details of what this superiority entails remain unclear.

The Association for the Advancement of Artificial Intelligence held its 1992 Fall Symposium Series on October 23-25 at the Royal Sonesta Hotel in Cambridge, Massachusetts. This article contains summaries of the five symposia that were conducted: Applications of AI to Real-World Autonomous Mobile Robots, Design from Physical Principles, Intelligent Scientific Computation, Issues in Description Logics: Users Meet Developers, and Probabilistic Approaches to Natural Language.

The first Robotics Exhibition and Competition sponsored by the Association for the Advancement of Artificial Intelligence was held in San Jose, California, on 14-16 July 1992 in conjunction with the Tenth National Conference on AI. This article describes the history behind the competition, the preparations leading to the competition, the threedays during which 12 teams competed in the three events making up the competition, and the prospects for other such competitions in the future.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.

The name of a or selecting models of a target physical embodied in a model is defined as a target system provides access to a system for a given qualitative reasoning position taken in each of the possible description of the system topology of task. It was motivated by two dimensions. Perspective taking as a the target circuit--a network of circuit observations regarding modeling in process is defined as formulating or components and their connections general and work in qualitative selecting a scenario model of a target by nodes. This information is physics in particular.