AI research at JPL started in 1972 when design and construction of experimental "Mars Rover" began. Early in that effort, it was recognized that rover planning capabilities were inadequate. Research in planning was begun in 1975, and work on a succession of AI expert systems of steadily increasing power has continued to the present. Within the group, we have concentrated our efforts on expert systems, although work on vision and robotics has continued in a separate organizations, with which we have maintained informal contacts. The thrust of our work has been to build expert systems that can be applied in a real-world environment, and to actually put our systems into such environments, taking a consultative responsibility for meeting user requirements. Several supportive tools for AI are also being built. The current computational environment includes a large main-frame as well as high-performance personal LISP machines. A separate group has been engaged in the design of an intelligent work station with advanced graphic displays intended to interface with AI systems.
We are in the midst of an AI boom, with investment and merger and acquisition activity in the sector increasing exponentially. This new frontier raises various challenges for IP law, where numerous questions exist about whether the existing legal framework is fit for purpose in the age of the intelligent machine. "Artificial intelligence" is generally used to refer to technology that carries out tasks that normally need human intelligence. Here, we focus on machine learning, a subset of AI that enables computers to learn from data without being explicitly programmed. A machine learning system typically comprises a computational model based on an algorithm (or algorithm stack) with a dataset to train it.
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.
A longstanding problem in the field of automated reasoning is designing systems that can describe a set of actions (or a plan) that can be expected to allow the system to reach a desired goal. Ideally, this set of actions is then passed to a robot, a manufacturing system, or some other form of effector, which can follow the plan and produce the desired result. The design of such planners has been with AI since its earliest days, and a large number of techniques have been introduced in progressively more ambitious systems over a long period. In addition, planning research has introduced many problems to the field of AI. Some examples are the representation and the reasoning about time, causality, and intentions; physical or other constraints on suitable solutions; uncertainty in the execution of plans; sensation and perception of the real world and the holding of beliefs about it; and multiple agents who might cooperate or interfere.
Thsi 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). In so doing, the article provides a historical perspective of the program in terms of the stages it went through as it matured. 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.