Session No. 8 Robots and Integrated Systems 385 TRAJECTORY CONTROL OF A COMPUTER ARM* by Richard Paul Stanford Artificial Intelligence Project Stanford University Stanford, California USA This paper describes the programming of a computer controlled arm. The programming is divided logically into planning and execution Communication between planning and execution is by a data file which specifies the arm trajectory with reapect to time, and actions that the arm should perform. The servo program which moves the arm along the trajectory is based on Legrangian mechanics and takes into account coupling between links, and the variation of inertial loading with change of arm configuration. Key words: INTRODUCTION arm, trajectory, servo We are Interested in driving a computer controlled arm such as the one shown in Figure 1. This arm [1] has six degrees of freedom with a vise grip hand and a useiuL working area about equivalent to that of a human arm. The arm is powered by printed circuit eietric motors with harmonic drive gear reductions.

MENTAL can be used as a guestion-answering system with formatted input /output, as a vehicle for experimenting with various theories of semantic structures or as the memory management portion of a natural language question-answering system. 1. Introduction In order to develop machines capable of "understanding" natural language, it is extremely valuable, if not necessary, to design a method of organizing a corpus of data to facilitate the storage and retrieval of information on many subjects, some in depth, some in breadth; to facilitate the storage, retrieval and use of the many complex relationships among real-world concepts; to facilitate the storage, retrieval and use of information which tells how other information in the corpus may be used to further explicate implied relationships among concepts; and to facilitate the identification from the vast corpus of data of those pieces of information most directly relevant to any given topic. This paper describes a data structure (MENS) and procedures for manipulating it The research reported herein was partially supported by a grant from the National Science Foundation (GJ-583) and partially by USAF Proj.

Session No. 2 Applications 59 CHALLENGE TO ARTIFICIAL INTELLIGENCE: PROGRAMMING PROBLEMS TO BE SOLVED Abstract J. E. Sammet IBM Corporation Cambridge, Mass. U. S. A. This paper is in the nature of a challenge to artificial intelligence experts. It suggests that the techniques of artificial intelligence should be applied to some realistic problems which exist in the programming and data processing fields. After a brief review of the little related existing work which has been done, the characteristics of programming problems which make them suitable for the application of artificial intelligence techniques are given. Specific illustrations of problems are provided under the broad categories of data structure and organization, program structure and organization, improvements and corrections of programs, and language. Descriptors artificial intelligence applications programming heuristic techniques I. INTRODUCTION It has been over 15 years since computers were first used for anything resembling "artificial intelligence". The pioneering work of Newell, Shaw, and Simon on proving theorems in the propositional calculus is so well known as not to need discussion for the people knowledgeable in the field of artificial intelligence.

Early programs were written to demonstrate that a particular task could be accomplished and could not periorm other tasks, even if quite similar, without being extensively rewritten. Generality unnecessary for the task at hand was sacrificed to keep the programs as *Currently on leave to The University of Jerusalem **Now at Computer Science Department, Rutgers University ***Is now at NIH, Bethesda, Maryland ****With Lockheed Palo Alto Research Labs //This research was supported by the Advanced research Projects Agency of the Department of Defense under Contract No. SD-183. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Advanced Research Projects Agency of the U.S. Government. Bmall as possible so they would fit the core limitations of our computer. The main result of this research was the development of programs which could find and stack cubes, either sorting them by size (1), or ordering them by voice command (2).

The general strategy of Meta-DENDRAL is to reason from data to plausible generalizations and then to organize the generalizations into a unified theory. Three main subprobleras are discussed: (1) explain the experimental data for each individual chemical structure, (2) generalize the results from each structure to all structures, and (3) organize the generalizations into a unified theory. The program is built upon the concepts and programmed routines already available in the Heuristic DENDRAL performance program, but goes beyond the performance program in attempting to formulate the theory which the performance program will use. I. Introduction Theory formation in science embodies many elements of creativity which make it both an interesting and challenging task for artificial intelligence research. One of the goals of the Heuristic DENDRAL project has long been the study of processes underlying theory formation.

This edge follower could easily find the outlines of white cubes on a black table, but was prone to error in less carefully controlled environments. Our studies of its inadequacies have stimulated the development of a more powerful edge follower, which overcomes most of the limitations of the old one. This program is currently the initial stage of visual processing in the Stanford hand-eye system (2). It has demonstrated an ability to track weak edges under adverse lighting conditions 2. HARDWARE The edge follower uses a standard vidicon television camera, modified to provide computer control of orientation (a pan-tilt head), focal length (a lens turret), color filter, focus, and target voltage. The lens iris is set manually. The pan-tilt head, lens turret, and focus motor *This research was supported by the Advanced research Projects Agency of the Department of Defense under Contract No. SD-183. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the Advanced Research Projects Agency of the U.S. Government.

This system accepts as input a scene represented as a line drawing. Based on a set of known object models, the program attempts to determine the identity and location of each object viewed. The most significant feature of the system is its ability to deal with imperfect input data. This ability appears essential in light of our current stock of preprocessing techniques and the variation that is possible in real world data. INTRODUCTION A hand-eye system is a problem solving system with an eye (camera) for input and a hand (manipulator) for output. Such a system must have at least 1) a set of scene analysis (perception) programs which interpret the real world in a meaningful way, 2) a set of manipulation programs which control movement of the hand in 3-space, and 3) an executive (problem solver, strategy) program which directs the perceptual and motor processes toward a desired goal.

In R. J. O'Brien (Ed.), Report of the twenty-second annual round table meeting on linguistics and language studies. (Monograph Series on Languages and Linguistics, No. 24.) Washington, D.C.: Georgetown University Press, 35-56.

Ph.D. dissertation "Bi-directional and heuristic search in path problems" (Stanford, Computer Science, 1970) summarized in this article in Machine Intelligence 6 (1971).In the uni-directional algorithms, the search proceeds from an initial nodeforward until the goal node is encountered. Problems for which the goal nodeis explicitly known can be searched backward from the goal node. Analgorithm combining both search directions is bi-directional.This method has not seen much use because book-keeping problems werethought to outweigh the possible search reduction. The use of hashingfunctions to partition the search space provides a solution to some of theseimplementation problems. However, a more serious difficulty is involved.To realize significant savings in bi-directional search, the forward andbackward search trees must meet in the 'middle' of the space. The potentialbenefits from this technique motivates this paper's examination of thetheoretical and practical problems in using bi-directional search.

This paper explores new approaches to the symmetric traveling-salesman problem in which 1-trees, which are a slight variant of spanning trees, play an essential role. A 1-tree is a tree together with an additional vertex connected to the tree by two edges. We observe that (i) a tour is precisely a 1-tree in which each vertex has degree 2, (ii) a minimum 1-tree is easy to compute, and (iii) the transformation on “intercity distances” cij → Cij + πi + πj leaves the traveling-salesman problem invariant but changes the minimum 1-tree. Operations Research, 18, 1138–1162.