Induction: Processes of Inference, Learning, and Discovery

@article{Holland1987InductionPO,
  title={Induction: Processes of Inference, Learning, and Discovery},
  author={John H. Holland and Keith J. Holyoak and Richard E. Nisbett and Paul Thagard and Stephen W. Smoliar},
  journal={IEEE Expert},
  year={1987},
  volume={2},
  pages={92-93}
}
Two psychologists, a computer scientist, and a philosopher have collaborated to present a framework for understanding processes of inductive reasoning and learning in organisms and machines. Theirs is the first major effort to bring the ideas of several disciplines to bear on a subject that has been a topic of investigation since the time of Socrates. The result is an integrated account that treats problem solving and induction in terms of rule-based mental models. Induction is included in the… 

Computational Models in the Philosophy of Science

  • P. Thagard
  • Computer Science
    PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association
  • 1986
A computational model of problem solving and learning that has been used to simulate several kinds of scientific reasoning is described.

Learning via Model Construction and Criticism

Evidence is used from case studies to argue that nonformal reasoning processes that are neither deductive nor inductive can play an important role in scientific model construction.

Reasoning and learning by analogy.

Analogy is a powerful cognitive mechanism that people use to make inferences and learn new abstractions. The history of work on analogy in modern cognitive science is sketched, focusing on

Learning without limits: from problem solving towards a Unified Theory of Learning

Learning is usually studied on the basis of binary distinctions like implicit vs. explicit learning, using instance vs. using rules, connectionist vs. symbolist, etc. In this thesis it is argued that

A Cognitive Model of Learning by Doing

In this paper an approach to learning cognitive skills from problem solving experience is presented – addressing some phenomena well known from human learning but seldom covered together in machine

Cognitive Architectures

  • In K. Frankish
  • Biology, Art
    Intelligent Systems, Control and Automation: Science and Engineering
  • 2019
It is argued that advances in neuroscience hold the promise for producing a general cognitive theory that encompasses the advantages of both rule-based and connectionist architectures, as well as a complete unified general theory of cognition.

Children, Adults, and Machines as Discovery Systems

1. IntroductionThis is a summary of recent research on the discovery process in which a commonframework for both psychological studies and machine learning (ML) approaches toscientific discovery is

What is the role of induction and deduction in reasoning and scientific inquiry

A long-standing and continuing controversy exists regarding the role of induction and deduction in reasoning and in scientific inquiry. Given the inherent difficulty in reconstructing reasoning

Implicit learning and tacit knowledge

I examine the phenomenon of implicit learning, the process by which knowledge about the ralegoverned complexities of the stimulus environment is acquired independently of conscious attempts to do so.
...