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.

They government contacts indicated the Bonnie Dorr has volunteered to serve also hope to increase the submissions importance of forming a mission as symposium cochair.

Analogical although analogy can help, as note that although still in its infancy reasoning is thus achieved in such well as hamper, learning. The role of and somewhat simplistic in character, systems by mainly keeping the analogy in learning is discussed by connectionist research might prove abstract relational microfeatures. Ann Brown and by Rand Spiro et al., to have an edge in tackling these Rumelhart proposes another way and the role of analogy in knowledge problems. The research described in for achieving analogical reasoning, acquisition is discussed by Brian Ross this book presents a grand challenge that is, "soft clamp," in which input and by John Bransford et al.; Stella and a future prospect for AI clamps can be overridden, and the Vosniadou studies the developmental researchers (traditional or connectionistic) rule of thumb is that the more concrete change in the use of analogy. Because in their endeavor to find a a feature is, the easier it can be part 3 of the book is of marginal better and more cognitively plausible overridden. The system finds the interest to AI, I do not discuss it any representation scheme.

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. We end with a debate on the proper role of experimental methodology in the design and validation of planning agents.

In his recent papers, entitled Intelligence without Representation and Intelligence without Reason, Brooks argues for mobile robots as the foundation of AI research. This article argues that even if we seek to investigate complete agents in real-world environments, robotics is neither necessary nor sufficient as a basis for AI research. The article proposes real-world software environments, such as operating systems or databases, as a complementary substrate for intelligent-agent research and considers the relative advantages of software environments as test beds for AI. First, the cost, effort, and expertise necessary to develop and systematically experiment with software artifacts are relatively low. Second, software environments circumvent many thorny but peripheral research issues that are inescapable in physical environments. Brooks's mobile robots tug AI toward a bottom-up focus in which the mechanics of perception and mobility mingle inextricably with or even supersede core AI research. In contrast, the softbots (software robots) I advocate facilitate the study of classical AI problems in real-world (albeit, software) domains. For example, the UNIX softbot under development at the University of Washington has led us to investigate planning with incomplete information, interleaving planning and execution, and a host of related high-level issues.

This workshop brought together 20 computer scientists, psychologists, and human-computer interaction (HCI) researchers to exchange results and views on human error and judgment bias. Human error is typically studied when operators undertake actions, but judgment bias is an issue in thinking rather than acting. Both topics are generally ignored by the HCI community, which is interested in designs that eliminate human error and bias tendencies. As a result, almost no one at the workshop had met before, and the discussion for most participants was novel and lively. Many areas of previously unexamined overlap were identified. An agenda of research needs was also developed.

The First International Workshop on Rough Sets: State of the Art and Perspectives was held on 2-4 September 1992 in Kiekrz, Poland. To stimulate the discussion, the participation was limited to 40 researchers who are involved in fundamental research in rough set theory and its extensions, logic for approximate reasoning, machine learning, knowledge representation and transfer, and applications of rough set methodology. The workshop focused primarily on applications of the basic idea of the approximate definition of a set and its consequences in other areas of science and engineering. Applications discussed at the workshop included machine learning, medical diagnosis, fault detection, medical image processing, neural net training, database organization, drug research, and digital circuit design.

AAAI News the edge of technology now, but we'll be at the point where we can Terry Weymouth of The University of Michigan was cochair and ringmaster for the performances. Grosz, Gordon conferences carried on with quiet the robots managed to perform this McKay Professor of Computer excitement in Washington in July, reasonably well. Science at Harvard, is the new President, AAAI's Autonomous Mobile Robot The third event proved the hardest, succeeding Patrick Hayes of the Competitions drew a stream of rousing and none of the robots completed the University of Illinois. The of MIT is President-Elect. The two events drew competitors robots were asked to select four "In the last several years," Grosz and spectators.

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.