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

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

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

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

Collaborative discovery in a scientific domain

A better understanding of the nature of consultations between professionals engaging in the collaborative process of solving complex problems — expertise in use — offers the potential to reshape our ideas about how to design computer systems that can engage in collaborative problem solving with their human cohorts. The research reported here has sought to account for key behaviors contributing to successful consultation, as identified by a cognitive task assessment of human-human consultation discourse in the medical teaching rounds setting. W e have come to view the communication acts of the presenter/investigator as evidence of his deliberate intention to indirectly construct a particular model of the patient's case — his model — in the expert's mind, resulting in two separate but related diagnostic tasks for the expert: one at the patient level and one at the presenter/investigator level. This dual-diagnostic theory of expert understanding of the presenter/investigator's communication actions is partially implemented in the RUMINATE program. The theory provides insights into the expert's capacity to model aspects of the presenter/investigator's competence — insights that contribute to our understanding of expertise embedded in the context of collaborative problem solving discourse

Imagery as Process Representation in Problem Solving

In this paper, we describe the characteristics of imagery phenomena in problem solving, develop a model for the process of forming and observing mental images in problem solving, and check the model against data obtained from subjects. Then, we describe the interaction between imaging and problem solving observed in our experiments, and discuss the use of our model to simulate it. We also discuss the relation between mental models and mental image briefly