Year: 2003
Type: article
Abstract: This paper presents a Bayesian satisficing model of when a problem-solver stops planning and begins acting.Existing knowledge about the environment is incrementally updated by new observations, and performance improves as a consequence of better knowledge about the environment.The model aims at bridging the gap between machine learning and cognitive science by adopting the bounded rationality framework (Simon, 1956), which assumes that cognition tends to exploit the characteristics of the environment without engaging in psychologically implausible computations.Empirical studies were conducted when human subjects learned to find the fastest path in a simple map (only one was reported in this paper).The model fit the human learning and performance well and provided insights into the mechanisms behind learning and performance in problem solving.
Author: Wai‐Tat Fu
Institution: Carnegie Mellon University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2767642068