Oral history interview with Saul Amarel

Transcript, 75 pp. Audio file available at http://purl.umn.edu/95658Amarel begins the interview with a discussion of his interest in artificial intelligence (AI) and his early research in the field while at Radio Corporation of America. He provides a brief overview AI research at Carnegie-Mellon University and Stanford University in the 1960s and his establishment of the computer science program at Rutgers University in the early 1970s. Amarel also discusses the relationship of AI to computer science. The bulk of the interview concerns the Information Processing Techniques Office's (IPTO) support of research in computer science and artificial intelligence. The primary topics of this discussion are IPTO and Amarel's recruitment as director in 1985, the importance of strategic computing, the creation of the Information Science and Technology Office (ISTO) and the budgeting process for ISTO. Amarel concludes with his thoughts on current directions in AI research

Progress Report for Year 2 of the Research Grant on AI and Design: Computer Aided Productivity (Cap) And Plan For Year 3

Technical report CAP-TR-

Progress Report For Year 2 Of The Research Grant On Ai And Design: Computer Aided Productivity (cap) And Plan For Year 3

ing and decomposing transformations are then applied in order to make the filters and evaluators easier to compute. An abstracting transformation replaces the original CSP problem space with a smaller abstraction space. A decomposing transformation splits a single CSP problem space into two or more subspaces, ignoring any interactions between them. Both types of transformation potentially introduce errors into the initially exact filters and evaluators. The transformations thus implement a tradeoff between the cost of using filters and evaluators, and the accuracy of the heuristic advice they provide. These techniques have been shown capable of synthesizing useful heuristics in domains such as floor- 11 planning and job-scheduling, among others. Ongoing research is aimed at applying and extending these techniques to a VLSI design task: Automatic synthesis of functions that estimate low level resource usage given only an abstract, high level circuit design. This research is part of co..