The ability of the higher animals to accept and interpret information from distant objects confers enormous advantages for creatures (or machines) which respond only to immediate stimulation and have no opportunity to anticipate the future. Distance receptors, especially the eyes, serve as early warning systems by giving information of distance events, making it possible to gauge the probable future. The classical biological notion of stimulusresponse applies to creatures limited to touch information. The development of distance-receptors evidently allowed brains to develop to give strategic behaviour. It is unfortunate that the early, now classical, studies of reflexes involving touch and the internal regulation of the body have been so largely taken over to describe brain function, for these concepts are inadequate for describing the central nervous system. They tell nothing about how brains handle information from the eyes, to allow animals and man to see. They tell us nothing about decision-making: how present experience is related to the stored past to predict the immediate future.

Using the Neomycin rule base, and inputting Neomycin's own actions to the action justification generator, the average size of J(.4,) was ten and the maximum size was approximately one hundred. When an Odysseus-generated rule base for the Neomycin domain was used, these set sizes increased by a factor of four to five. After the set J(Ai) is generated, the action justification ranking subsystem of Odysseus determines the likelihood that J(Ai) contains ji, the action justification of the specialist. This involves, first, ranking ji,„ in order of likelihood of being equal to the unknown An example of ranking rule is: given two elements of a J(.4,), where,4, occurs early in the problem solving session, the

LOGIC CROUP KNOWLEDGE SYSTEMS LABORATORY Department of Computer Science Stanford tIniversity Stanford, California 91305 Decision Precedures Abstract Distributed artificial intelligence is the study of how a group of individual intelligent agents can combine to solve a difficult global problem. This paper discusses in very general terms the problems of achieving this global goal by considering simpler, local subproblems; we drop the usual requirement that the agents working on the subproblems do not interact. We are led to a single assumption. An example of a distributed computation using these ideas is presented. Introduction The thrust of research in distributed artificial intelligence (DAI) is the investigation of the possibility of solving a difficult problem by presenting each of a variety of machines with simpler parts of it. The approach that has been taken has been to consider the problem of dividing tho original problem: what:,libtasks should be pursued at any given time? To which available machine should a..iven subtask be assigned? The question of how the individual machines should g9 about solving their subproblems has been left to the non-distributed Al community (or perhaps to a recursive application of DAI techniques). The assumption underlying this approach--that each of the agents involved in the solution of the subproblems can proceed independently of the others--has recently been called into question 12,3,6,7,101. It has been realized that, in a world of limited resources, it is inappropriate to dedicate a substantial fraction of those resources to each processor. The increasing attract:witless of parallel architectures in which processors share memory is an example of this: memory is a scarce resource. Automated factories must inevitably encounter similar difficulties. Are the robots working in such factories to be given distinct bins of component parts, and non-overlapping regions in which to work or to travel from one area of the factory to another?

Some of the more popular alternativo used to build knowledge systems are production systems, backward-chained reasoning, logic programming, heuristic search, and the Blackboard framework. Many of the applications implemented in production systems have been written in the OPS language [8]. In this framework, knowledge is represented as a set of homogeneous rules that are scanned for applicability in a data base that contains the current state of solution. Backward chaining also has a homogeneous set of rules, but the search for applicable rules is driven by a hierarchy of goals and sub-goals. The best known system for implementing this type of program is EMYCIN [4].

The impact of a computer-based data management system on the completeness of clinical trial data was studied before and after the system's introduction in an oncology clinic. Physicians use the system, termed ONCOCIN, to record data during patient visits and to receive advice about treatment and tests required by experimental cancer protocols. Although ONCOCIN does not force the user to enter all data expected by the protocol, after its introduction there was improvement in the recording frequency of such data. The percentage of expected physical findings recorded increased from 74% to 91% (p .05),

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.