We compare the big problems studied in artificial intelligence and related fields in order to understand some major changes--both internal and external--recently suffered by AI. The comparison finds AI with few problems to call its own, and we identify some further major changes that may occur soon. Work in earlier days seemed heroic, filled with the excitement of being first to discover and conquer big problems, while much work today seems a bland pursuit of more of the same. Where, the old hands ask, have all the big ideas gone? This note attempts to respond to this complaint, for the answer sheds light on the nature of artificial intelligence.

The methodological underpinnings of AI are slowly changing. Benchmarks, test beds, and controlled experimentation are becoming more common. Although we are optimistic that this change can solidify the science of AI, we also recognize a set of difficult issues concerning the appropriate use of this methodology. We discuss these issues as they relate to research on agent design. We survey existing test beds for agents and argue for appropriate caution in their use.

However, existing online learning techniques typically applied to robot learning (for example, Hexmoor, Meeden, and Murphy [1997]) differ fundamentally from human learning. Online root learning using robot sensors is not equivalent to autonomous mental development in robots, nor should mental develop-This article describes a workshop on mental development and learning issues that are relevant to both machine and human sciences. It was jointly funded by the National Science Foundation and the Defense Advanced Research Projects Agency and held at Michigan State University on 5 to 7 April 2000. Such systems already exist (for example, systems that use neural network techniques). There is a need, therefore, for increased studies in computational autonomous mental development (CAMD) that are of interest to both machine and human intelligence researchers.

In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is a complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans both on roads and in unstructured zones. An essential part of Boss's success stems from its ability to safely handle both abnormal situations and system glitches.

TACAIR-SOAR flew all U.S. fixed-wing aircraft. The general goal was to generate behavior that "looks human" when viewed by a training audience participating in operational military exercises. Its most dramatic use was in the Synthetic Theater of War 1997 (STOW '97), held 29-31 October 1997 (Ceranowicz, 1998; Laird, Jones, and Nielsen 1998; Laird et al. 1998). STOW '97 was a United States Department of Defense (DoD) Advanced Concept Technology Demonstration (ACTD) that was integrated with the United Endeavor 98-1 (UE 98-1) training exercise. As an ACTD, the overall goal of STOW '97 was to permit an early and inexpensive evaluation of advanced technologies that show promise for improving military effectiveness.

These technologies would not exist today without the sustained federal support of fundamental AI research over the past three decades. This article was written for inclusion in the booklet "Computing Research: A National Investment for Leadership in the 21st Century," available from the Computing Research Association, cra.org/research.impact. Early work in AI focused on using cognitive and biological models to simulate and explain human information processing skills, on "logical" systems that perform commonsense and expert reasoning, and on robots that perceive and interact with their environment. This early work was spurred by visionary funding from the Defense Advanced Research Projects Agency (DARPA) and Office of Naval Research (ONR), which began on a large scale in the early 1960s and continues to this day. By the early 1980s an "expert systems" industry had emerged, and Japan and Europe dramatically increased their funding of AI research.

Of the twenty chapters in the first published book on AI, the 1963 Computers and Thought anthology by Feigenbaum and Feldman, six had been previously published as Rand research reports (Armer, 1962; Feigenbaum, 1961; Newell, Shaw & Simon, 1957, 1958; Newell & Simon, 1961a; Tonge, 1959). Much of this early work in AI was the result of the collaboration of two Rand employees, Allen Newell and Cliff Shaw, and a Rand consultant, Herbert Simon of the Carnegie Institute of Technology (later to become Carnegie-Mellon University). Beginning in the mid-1950s Newell, Shaw, and Simon's research on the logic theory machine, their chess playing program, and the general problem solver (GPS) defined much of the AIrelated research during the first decade of AI. Their work encompassed research areas that are still prominent subfields of artificial intelligence: symbolic processing, heuristic search, problem solving, planning, learning, theorem proving, knowledge representation, and cognitive modeling. It is important to note that this surge of AI activity at Rand did not take place in isolation.

Abstract, This article presents a summary of ongoing, funded artificial intelligence research at North Carolina State University. The primary focus of the research is engineering aspects of artificial int,clligence. Knowledge-based expert systems (KBES) USC expert knowledge and expert methods to conceptualize and reason for the purpose of deriving decisions and inferences from which problem solutions are obtained. The range of expert systcm applications in engineering extends from interpret,ive problrms, where reasoning about the problem is required in light of the knowledge available in that problem's domain, to generative problems, where potential solutions are generated and tested against candidate solutions defined by sets of applicable constraints. From an engineering perspective, expert systems can be viewed as new tools that will enable current computcraided engineering (CAE) systems to be enhanced or, in appropriate situations, as replacements for these systems and their human users.

Artificial Intelligence Laborato y, Wright-Patterson AFl?, Ohio 45433 Abstract The Air Force Institute of Technology [AFIT] provides master's degree education to Air Force and Army Officers in various engineering fields. It is in a unique position to educate and perform research in the area of applications of artificial intelligence to military problems. Its two AI faculty members are the only military officers with Ph D's in Artificial Intelligence. In the past two years, the artificial intelligence Laboratory of the AFIT has become a major focal point for AI research and applications within the government In this article, we describe our ongoing applications research in the areas of automated cockpit systems, natural language understanding, maintenance expert systems, expert systems for planning, and knowledge based software design. In response to the need for rapid training of engineers in artificial intelligence, AFIT has developed a Master's degree curriculum for AI.

Research in the area of expert systems has developed from our experience in building consultation programs in a number of application domains (Weiss, Kulikowski, and Safir, 1978; Lindberg et al., 1980; Kulikowski, Weiss, and Galen, 1981; Kulikowski, 1980). The EXPERT system (Weiss and Kulikowski, 1979) is a generalized scheme for building expert reasoning models, exercising them with individual problems, testing and analyzing their performance on large numbers of problem-types, and improving them by knowledge base refinement techniques. The system has been operational on DEC lo/20 computers since 1978; versions also exist on VAX and IBM computers This system has been used by specialists in medicine, biomedical modeling, oil exploration, and chemistry to build models that capture their expertise in problem solving. In 1981 we complet,ed an interesting technology transfer experiment in which a model for the interpretation of serum protein electrophoresis patterns was automatically translated from its EXPERT representation into algorithmic form, and then automatically translated into assembler code for running on a microprocessor (Weiss, KuIikowski, and Galen, 1981). The EXPERT system is unusual among knowledge-based AI systems in that efficiency is a major design goal.