One to organize the construction teams. One to hack the planning system. How many AI people does it take to change a lightbulb? One to get Westinghouse to sponsor the research. One to indicate about how the robot mimics human motor A. At least 55: The knowledge engineering group (6): One to define the goal state.

Cambridge, can read this material and gain insight into some of the Massachusetts: The MIT Press, 1985. The treatment is not detailed enough to be used as a text on The Connection Machine introduces a new type of parallel architecture design but it is illuminating and interesting computer which may lead to radically new ways of to read. Once the reader has been introduced to the basics of The author, Daniel Hillis, is the designer of the Connection Machine architecture, the author presents a machine and the founder of Thinking Machines Corporation, description of a prototype called CMl; a machine with a company committed to building "Connection Machines." Hillis discusses the Hillis' book describes the Connection Machine custom VLSI chip, details of the simple processor cells, and and the issues surrounding its design. At made up of thousands, potentially millions, of small, simple, times the Connection Machine appears so different from processors working simultaneously, each with its own current computers that it seems more akin to science fiction tiny memory.

The first blackboard system was the Hearsay-II speech-understanding system (Erman et al. 1980), which evolved between 1971 and 1976. Subsequently, many systems have been built that have similar system organization and run-time behavior. The objectives of this document (a part of a retrospective monograph on the AGE Project currently in preparation) are (1) to define what is meant by blackboard systems and (2) to show the richness and diversity of blackboard system designs. In Part 1 we discussed the underlying concept behind all blackboard systems -- the blackboard model of problem solving. In order to bridge the gap between the model and working systems, we introduced and discussed the blackboard framework. We also traced the history of ideas and designs of some application systems that helped shape the blackboard model. In Part 2, we describe and contrast existing blackboard systems. Blackboard systems can generally be divided into two categories: application systems and skeletal systems. In application systems, the blackboard system components are integrated into the domain knowledge required to solve the problem at hand.

During the past two years, the Dempster-Shafer theory of evidence has attracted considerable attention within the AI community as a promising method of dealing with uncertainty in expert systems. As presented in the literature, the theory is hard to master. In a simple approach that is outlined in this paper, the Dempster-Shafer theory is viewed in the context of relational databases as the application of familiar retrieval techniques to second-order relations in first normal form. The relational viewpoint clarifies some of the controversial issues in the Dempster-Shafer theory and facilities its use in AI-oriented applications.

Research and applications work in AI is being conducted by several groups at Johnson Space Center (JSC). In the Research and Engineering Directorate, these groups include (1) the Artificial Intelligence and Information Sciences Office, (2) the Simulation and Avionics Integration Division, (3) the Avionics Systems Division (ASD), and (4) the Tracking and Communications Division. In the Space Operations Directorate, these groups include (1) the Mission Planning and Analysis Division - Technology Development and Applications Branch, (2) the Spacecraft Software Division, and (3) the Systems Division-Systems Support Section. The first part of the article describes the AI work in Research and Engineering Directorate.

The objectives of this article are (1) to define what is meant by "blackboard systems" and (2) to show the richness and diversity of blackboard system designs. The article begins with a discussion of the underlying concept behind all blackboard systems, the blackboard model of problem solving. In Section 2 the history of ideas is traced, and the designs of some application systems that helped shape the blackboard model are detailed. Part 2 of this article which will appear in the next issue of AI Magazine, describes and contrasts some blackboard systems and discusses the characteristics of application problems suitable for the blackboard method of problem solving.

We have implemented a knowledge system that integrates the many computational programs ( technology codes) Boeing aerospace vehicle designers use, thereby expediting design analysis. Because this system separates facts about attributes of the current set of technology codes from general knowledge about running the codes, those who maintain the system can keep it continuously up to date at low cost. In contrast, three conventional approaches failed because they could not be maintained easily.

This article contains reports from the various research groups in the SUNY Buffalo Department of Computer Science, Vision Group, and Graduate group in Cognitive Science. It is organized by the different research topics. However, it should be noted that the individual projects might also be organized around the methodologies and tools used in the research, and, of course, many of the projects fall under more than one category.

The possibility of constructing a personal AI raises many ethical and religious questions that have been dealt with seriously only by imaginative works of fiction; they have largely been ignored by technical experts and by philosophical and theological ethicists. Arguing that a personal AI is possible in principle, and that its accomplishments could be adjudicated by the Turing Test, the article suggests some of the moral issues involved in AI experimentation by comparing them to issues in medical experimentation. Finally, the article asks questions about the capacities and possibilities of such an artifact for making moral decisions. It is suggested that much a priori ethical thinking is necessary and that, that such a project cannot only stimulate our moral imaginations, but can also tell us much about our moral thinking and pedagogy, whether or not it is ever accomplished in fact.

This report is derived from the IJCAI panel titled U.S and Japanese Cooperation in AI and R&D Opportunities, held August 23, 1986 at the University of California at Los Angeles. This panel discussed the sensitive topic of alternatives to nationalistic competitive strategies that have contributed to an extreme trade deficit surpassing $40 billion in 1986. Each country has designated AI research as a key to unlock years of generations of technology and has directed billions of dollars to fund this development. The most recognized projects are the U.S. Microelectronics Technology Computer Consortium (MCC) and Japan's Fifth Generation Computer Project (ICOT).