This report stems from an April 1994 meeting, organized by AAAI at the suggestion of Steve Cross and Gio Wiederhold.1 The purpose of the meeting was to assist ARPA in defining an agenda for foundational AI research. Prior to the meeting, the fellows and officers of AAAI, as well as the report committee members, were asked to recommend areas in which major research thrusts could yield significant scientific gain -- with high potential impact on DOD applications -- over the next ten years. At the meeting, these suggestions and their relevance to current national needs and challenges in computing were discussed and debated. An initial draft of this report was circulated to the fellows and officers.

Representatives of universities and businesses were chosen by the Japan Technology Evaluation Center to investigate the state of the technology in Japan relative to the United States. The panel's report focused on applications, tools, and research and development in universities and industry and on major national projects.

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?

The Association for the Advancement of Artificial Intelligence held its 1993 Fall Symposium Series on October 22-24 in Raleigh, North Carolina. This article contains summaries of the six symposia that were conducted: Automated Deduction in Nonstandard Logics; Games: Planning and Learning; Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice; Instantiating Intelligent Agents; and Machine Learning and Computer Vision: What, Why, and How?

Science deals with knowledge of the material world based on objective reality. It is under constant attack by those who need magic, that is, concepts based on imagination and desire, with no basis in objective reality. A convenient target for such people is speculation on the machinery and method of operation of the human mind, questions that are still obscure in 1994. In The Emperor's New Mind, Roger Penrose attempts to look beyond objective reality for possible answers, using, in his argument, the theory that computers will never be able to duplicate the human experience. This article attempts to show where Penrose is in error by reviewing the evolution of men and computers and, based on this review, speculates about where computers might and might not imitate human perception. It then warns against the dangers of passive acceptance when respected scientists venture into the occult.

Similarity in Cognition: A Review of Similarity and Analogical Reasoning

In AI, psychology, and education, a growing body of research supports the view that learning is a goal-directed process. Psychological experiments show that people with varying goals process information differently, studies in education show that goals have a strong effect on what students learn, and functional arguments in machine learning support the necessity of goal-based focusing of learner effort. At the Fourteenth Annual Conference of the Cognitive Science Society, a symposium brought together researchers in AI, psychology, and education to discuss goal-driven learning. This article presents the fundamental points illuminated at the symposium, placing them in the context of open questions and current research directions in goal-driven learning.

In his model, requirements needs, it must be able to represent is done unintentionally; a problem for filling system knowledge solver attempting to solve a gaps also direct explanation generation what these needs are. Ram proposed problem simply stores a trace of its by guiding retrieval and revision representations that include processing without attention to its of explanations during case-based the desired knowledge (possibly partially future relevance. However, Ng's previously explanation construction (Leake specified) and the reason that mentioned studies show that 1992). In the context of analogical the knowledge is sought. Leake for a different class of task, learning mapping, Thagard pointed out that focused on the representation of the goals have a strong effect on the goals, semantic constraints, and syntactic knowledge required to resolve anomalies learning performance of human constraints all affect analogical (which depends on a vocabulary learners. A future question is to identify mapping (Holyoak and Thagard 1989) of anomaly characterization structures the limits of goal-driven processing and the retrieval of potential analogs to describe the information in human learners.

We have expanded review criteria for the technical program, effectively increasing the number of ways in which a submitted paper can qualify for acceptance. Most importantly, we have revised the review procedure to encourage acceptance of a larger number and broader range of papers, as discussed below.

This article contains an overview of recent and ongoing projects at Boeing's Huntsville Advanced Computing Group (ACG). In addition, it contains an overview of some of the work being conducted by Boeing's Advanced Civil Space Systems Group. One aspect of ACG's charter is to support the efforts of other groups at Boeing. Thus, AI is not considered a stand-alone field but, instead, is considered an area that can be used to find both long- and short-term solutions for Boeing and its customers.