Rational Decision-Making in Business Organizations

Lecture to the memory of Alfred Nobel, December 8, 1978decision making;

Experimentation in machine discovery

KEKADA, a system that is capable of carrying out a complex series of experiments on problems from the history of science, is described. The system incorporates a set of experimentation strategies that were extracted from the traces of the scientists' behavior. It focuses on surprises to constrain its search, and uses its strategies to generate hypotheses and to carry out experiments. Some strategies are domain independent, whereas others incorporate knowledge of a specific domain. The domain independent strategies include magnification, determining scope, divide and conquer, factor analysis, and relating different anomalous phenomena. KEKADA represents an experiment as a set of independent and dependent entities, with apparatus variables and a goal. It represents a theory either as a sequence of processes or as abstract hypotheses. KEKADA's response is described to a particular problem in biochemistry. On this and other problems, the system is capable of carrying out a complex series of experiments to refine domain theories. Analysis of the system and its behavior on a number of different problems has established its generality, but it has also revealed the reasons why the system would not be a good experimental scientist

Reply to “final note” by Benoit Mandelbrot

Dr. Mandelbrot's original objections (1959) to using the Yule process to explain the phenomena of word frequencies were refuted in Simon (1960), and are now mostly abandoned. The present “Reply” refutes the almost entirely new arguments introduced by Dr. Mandelbrot in his “Final Note,” and demonstrates again the adequacy of the models in 1955

The Structure of Ill Structured Problems

The boundary between well structured and ill structured ~roblems is vague, fluid and not susceptible to formalization. Any problem solving process w'iii appear ill structured if the problem solver is a serial machine that has access to a ~w:-yiarge long-term memory of potentially relevant information, and]or access to a very large exterlm! memory that provides information about the actual real-world c~,sequences of problem-~olving actions. There is no reason to suppose that new and hitherto uaknown concepts or teckniques are needed to enable artificial intelligence systems to operate successfully in domains that have these characteristics

Discovering qualitative empirical laws

In this paper we describe GLAUBER, an AI system that models the scientific discovery of qualitative empirical laws. We have tested the system on data from the history of early chemistry, and it has rediscovered such concepts as acids, alkalis, and salts, as well as laws relating these concepts. After discussing GLAUBER we examine the program's relation to other discovery systems, particularly methods for conceptual clustering and language acquisition

Cue recognition and cue elaboration in learning from examples

This paper describes the processes used by students to learn from worked-out examples and by working through problems. Evidence is derived from protocols of students learning secondary school mathematics and physics. The students acquired knowledge from the examples in the form of productions (condition --&gt; action): first discovering conditions under which the actions are appropriate and then elaborating the conditions to enhance efficiency. Students devoted most of their attention to the condition side of the productions. Subsequently, they generalized the productions for broader application and acquired specialized productions for special problem classes.</p