A computer-based cancer chemotherapy protocol advisor, termed ONCOCIN, has been implemented for experimental use in a university oncology clinic. It uses artificial intelligence techniques to guide the treatment of patients enrolled in chemotherapy protocols. The program combines formal protocol guidelines with judgments of cancer experts who have experience adapting such protocols for aberrant clinical situations. The quality of the program's advice is one of several important evaluation questions for a system of this kind. We compared the chemotherapy administered by clinic physicians with the treatment plan that would have been recommended by ONCOCIN in 415 clinic visits for 39 lymphoma patients seen prior to the program's introduction.

An increasing number of Artificial Intelligence (Al) programs are implemented on high-performance workstations with a bitmap display, a mouse input device, and a keyboard. The programming environment (usually a dialect of LISP) generally provides support for multiple, overlapping windows, and various kinds of menus including pop up menus. The user can move, reshape, close, and scroll the windows. Additionally, a programmer can designate arbitrary regions of a window to be selectable with the mouse. This means that a user can invoke an action by pressing and releasing a mouse button while the mouse cursor is in the designated region.

In recent years there has been a great deal of interest in the commercial applications of knowledge-based (KB) systems (commonly called expert systems). Interest in KB systems was spurred on by the development of programs that can solve complex tasks at an expert level.

This paper describes PM, an execution model for automating backward-chainirg deductions on multiple processors The term execution model refers 1-the state, messages and procedures required to perform the computation correctly. The target multiprocessor is char3cterized by (1) a large number of small processors, (2) inter-processor communication via messages, and (3) a distributed database. The key distinguishing feature of PM is simultaneous exploitation of and-parallelism, or-parallelism and pipelining in this scenario Table of Contents 1.

ONCOCIN is a rule-based expert system to advise on cancer chemotherapy. Although shown to provide excellent advice, the program could not be easily adapted to critique a physician's treatment plan without incorporation of additional knowledge of the structure of experimental protocols. A separate effort to automate the encoding of new oncology protocols was impeded by the lack of structural organization in the knowledge base. In both cases, problems arose because ONCOCIN's knowledge representation scheme did not reflect the hierarchy of control knowledge inherent in oncology protocols. The limitations of current knowledge representation techniques in ONCOCIN are discussed. In ONCOCIN is a medical expert system that assists physicians In the treatment of cancer patients enrolled in chemotherapy protocols.

MRS stands for Meta-level Representation System. If your response to this is a knowing nod of understanding you can probably skip the first few chapters. In a sense, MRS is a computer language, in that one enters text in a designated syntax and it gets processed and produces answers (or not). But because MRS is also able to reason with the information you give it, the'program' you enter can be seen more as representing facts than specifying a process. The importance and utility of this difference will become clear.

Optimal construction of expert systems demands a powerful interactive environment for knowledge base management by knowledge engineers. Key requirements include techniques for (a) examining existing information, (b) adding new knowledge and editing preexisting data structures, and (c) examining dynamic internal system behavior to facilitate debugging during consideration of actual cases.

This article presents an experiment in knowledge-intensive programming within a general problemsolv:ng production-system architecture called Soar In Soar, knowledge is encoded within a set of problem spaces.

From attributes 8 3 Implementation 8 3.1 Overview of Meta-Rulegen 3.2 Algorithm 10 3.2.1 Approach from object rule 11 3.2.2 Approach from attributes 14 4 Preliminary Results 15 5 Conclusion 17 ENHANCING PERFORMANCE OF EXPERT SYSTEMS BY AUTOMATED DISCOVERY OF META-RULES Abstract Machine learning can be used to formulate new meta-level knowledge. A small MYCIN-like medical diagnosis system was constructed as a starting point. Two heuristic methods are used in a program called Meta-Rulegen to form meta-rules from the knowledge base in the diagnosis system. In a preliminary study, 63 meta-rules were formed automatically and, by judiciously selecting a set of meta-rules, the efficiency of the diagnosis system can be improved significantly without degrading the quality of advice. This study suggests that meta-rules can be learned automatically to improve the efficiency of rule-based systems.

Stanford -- KSL that Think. Consideration of the of the new 16-bit integrated circuits that phenomenal progress of the past 30 years leaves one with a feeling of have allowed computers oi small size and considerable power to be developed. The only certainty in sight is that scientists. BRUCE G. BUCHANAN is Professor of In addition to game playing early Al work focused on techniques for solving Computer Science Research at Stanford small symbolic reasoning problems. Researchers continue to ponder these problems (Overleat) Illustration by f red Nelson as well.