This report describes recent and continuing research in artificial intelligence and related fields being conducted at the University of Pennsylvania. Although AI research takes place primarily in the Department of Computer and Information Science ( in School of Engineering and Applied Science), many aspects of this research are preformed in collaboration with other engineering departments as well as other schools at the University, such as the College of Arts and Sciences, the School of Medicine, and Wharton School.

The Portrait Programs Project grew out of hyperinterdisciplinarianism of the famed Gigabase Sculpture Group, in turn stimulated by recent cutbacks in government support for the arts. The National Endowment for the Humanities and the National Science Foundation had jointly funded the Gigabase Sculpture Project to foster the literary/musical genre of composing genetic codes for novel organisms. Later, artists trained in recombinant DNA technology designed massive Brancusi-esque statues of living cytoplasmic jelly. However, Art For Art's Sake objectives of these giblet sculptors were compromised by precautions necessary after discovery of the "Gogol's-Theorem Bomb" that threatened to get loose and jam all DNA replication in the biosphere; not even viruses would have survived.

I review the research leading from the GUIDON rule-based tutoring system, including the reconfiguration of MYCIN into NEOMYCIN and NEOMYCIN's generalization in the heuristic classification shell, HERACLES. The presentation is organized chronologically around pictures and dialogues that represent conceptual turning points and crystallize the basic ideas. My purpose is to collect the important results in one place, so they can be easily grasped. In the conclusion, I make some observations about our research methodology.

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.