We present two neural network controller learning schemes based on feedbackerror-learning and modular architecture for recognition and control of multiple manipulated objects. In the first scheme, a Gating Network is trained to acquire of a number of objects (or sets ofobject-specific representations for recognition objects). In the second scheme, an Estimation Network is trained to acquire function-specific, rather than object-specific, representations which directly estimate to identify manipulatedphysical parameters. Both recognition networks are trained objects using somatic and/or visual information. After learning, appropriate motor commands for manipulation of each object are issued by the control networks.

We propose and empirically evaluate a method for the extraction of expertcomprehensible rules from trained neural networks.

I met Allen for the first time when I came for a two semester long visit to Carnegie Mellon University in 1968. This encounter was a distinct factor in my later decision to join the faculty at Carnegie Mellon University.

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.

This article is a slightly modified version of an invited address that was given at the Eighth IEEE Conference on Artificial Intelligence for Applications in Monterey, California, on 2 March 1992. It describes the lessons learned in developing and implementing the Artificial Intelligence Research and Development Program at the National Aeronautics and Space Administration (NASA). These stages are similar to the "ages of artificial intelligence" that Pat Winston described a year before the NASA program was initiated. The final section of the article attempts to generalize some of the lessons learned during the first seven years of the NASA AI program into AI program management heuristics.

The Digital Services Research Group and its predecessor groups and offshoots in Digital Equipment Corporation have been mobilizing leading-edge AI research to bear on real-life problems that face the corporation and its customers. The general strategy of the group is to explore emerging techniques relevant to service and support needs through developing rapid prototypes, deploying these prototypes, and incorporating feedback from users. With over 32 major projects undertaken during the past decade, we have worked on broad spectrum of problems and explored a variety of advanced AI techniques. This article describes the current AI activities in five areas: (1) enterprise advisory systems, (2) natural language processing and textual information retrieval, (3) largescale knowledge base management and access, (4) software configuration management, and (5) intrusion detection.

Indeed, this is true, if for no attraction reaches almost all of us. Fairy stories let us enter an enchanted world. We do Magic abounds, though always in special ways. Villainy is there, certainly danger. We need the hidden guidance of The spell is broken, and the Princess smiles and fairy stories to tell us of the trials we must marries the youth who made her laugh.

NASA's AI program has implemented Rather, it is to attempt to describe the lessons learned in the process of putting the program in setting up and carrying out the first together and carrying it out. Research and Development Program at the Did the plan work? How did National Aeronautics and Space Administration the program readjust? This AI program is sponsored by faced, and how would they be handled differently NASA's Office of Aeronautics and Space Technology. What are the heuristics used to The program conducts research and keep NASA's AI ship afloat in the churning development at the NASA centers (Ames, seas of government politics? It team never got lost in the process of setting also sponsors research in academia and industry, up the AI program, there were a few times primarily through Ames Research Center, when it was temporarily directionally disoriented. There were encounters with the NASA. The AI group at Ames, which is headed unforeseen that called for real-time reactive by Peter Friedland, has particular strengths in replanning.

I met Allen for the first time when I came for a two semester long visit to Carnegie Mellon University in 1968. This encounter was a distinct factor in my later decision to join the faculty at Carnegie Mellon University.