Twenty five years is not long in the history of a science--long enough to achieve, short enough to remember. Your esteemed founders are still around -- vigorous, not so young anymore. Out of the cybernetics you came, and information-theoretic psychology. You were born in the early days of modern computing, on hot, bulky hardware with names few now remember, like JOHNNIAC; in strange and wonderful software called list structures, with stacks you could "push down" and "pop-up," bearing arcane acronyms like IPL and FLPL.

The Robotics Project (the "Hand-Eye Project") evolved within the Stanford Artificial Intelligence Laboratory under the guidance of John McCarthy, Les Earnest, Jerry Feldman, and Tom Binford. Major efforts have been undertaken to isolate and solve fundamental problems in computer vision, manipulation, and autonomous vehicles. Stereo vision and texture have been examined. Several generations of robot programming languages have resulted in AL, an intermediate-level language for commanding manipulation.

For intelligent computers to be able to interact with the real world, they must be able to aggregate individual actions into sequences to achieve desired goals. This process is referred to as automatic problem solving, sometimes more casually called automatic planning. The sequences of actions that are generated are called plans.

This overview takes a general look at search in problem solving, indicating some connections with topics considered in other Handbook chapters. The these general ideas are found in programs for natural second section considers algorithms that use these language understanding, information retrieval, automatic representations. In methods, which use information about the nature and this chapter of the Handbook we examine search as a tool structure of the problem domain to limit the search. Most of the Finally, the chapter reviews several well-known early examples considered are problems that are relatively easy programs based on search, together with some related to formalize. The first of these is a may be, however, that the description of a task-domain database, which describes both the current task-domain situation is too large for multiple versions to be stored situation and the goal.

AI research at CMU is closely integrated with other activities in the Computer Science Department, and to a major degree with ongoing research in the Psychology Department. Although there are over 50 faculty, staff and graduate students involved in various aspects of AI research, there is no administratively (or physically) separate AI laboratory. To underscore the interdisciplinary nature of our AI research, a significant fraction of the projects listed below are joint ventures between computer science and psychology.

At the recent AAAI conference at Stanford, it became apparent that many new AI research centers are being established around the country in industrial and governmental settings and in universities that have not paid much attention to AI in the past. At the same time, many of the established AI centers are in the process of converting from older facilities, primarily based on Decsystem-10 and Decsystem-20 machines, to a variety of newer options. At present, unfortunately, there is no simple answer to the question of what machines, operating systems, and languages a new or upgrading AI facility should use, and this situation has led to a great deal of confusion and anxiety on the part of those researchers and administrators who are faced with making this choice. In this article I will survey the major alternatives available at present and those that are clearly visible on the horizon, and I will try to indicate the advantages and disadvantages of each for AI work. This is mostly information that we have gathered at CMU in the course of planning for our own future computing needs, but the opinions expressed are my own.

Finally, abstraction can be extended to involve multiple complexity. In particular, one of the most costly behaviors levels, leading to a hierarchy of plans, each serving as a of the basic problem solving strategies is their inefficiency skeleton for the problem solving process at the next level in dealing with goal descriptions that include conjunctions. of detail. The search process at each level of detail can Because there is usually no good reason for the problem thus be reduced to a sequence of relatively simple solver to prefer to attack one conjunct before another, an subproblems of achieving the preconditions of the next incorrect ordering will often be chosen. This can lead to step in the skeleton plan from an initial state in which the an extensive search for a sequence of actions to try to previous step in the skeleton plan has just been achieved.

Happy Silver Anniversary, Artificial Intelligence! Twenty five years is not long in the history of a science--long enough to achieve, short enough to remember. Your esteemed founders are still around -- vigorous, not so young anymore. Out of the cybernetics you came, and information-theoretic psychology. You were born in the early days of modern computing, on hot, bulky hardware with names few now remember, like JOHNNIAC; in strange and wonderful software called list structures, with stacks you could "push down" and "pop-up," bearing arcane acronyms like IPL and FLPL. Time-sharing? One signed up on the schedule. Interaction? One pushed keys on the console teleype or panel.

I am absolutely delighted to be able to join in this morning to offer my reflections on the occasion of the official beginning of the Robotics Institute. Beginnings are full of promise and potential. This one is no exception. What the Robotics Institute will become -- what effects it will have, both witting and unwitting -- are for the future to tell. What we all have now is a sense of adventure and anticipation.

The Robotics Project (the "Hand-Eye Project") evolved within the Stanford Artificial Intelligence Laboratory under the guidance of John McCarthy, Les Earnest, Jerry Feldman, and Tom Binford. Major efforts have been undertaken to isolate and solve fundamental problems in computer vision, manipulation, and autonomous vehicles. Generalized cones were introduced for modeling the geometry of 3-dimensional objects, and programs were constructed which learned structural descriptions of objects from laser-ranging data ("structured light"). Stereo vision and texture have been examined. Several generations of robot programming languages have resulted in AL, an intermediate-level language for commanding manipulation. A computer-controlled roving vehicle ("the cart") detected obstacles (using 9-eyed stereo) and planned paths to avoid them.