Name
Abstract | ||
|---|---|---|
Builders of expert rule-based systems attribute the impressive performance of their programs to the corpus of knowledge they embody: a large network of facts to provide breadth of scope, and a large array of informal judgmental rules (heuristics) which guide the system toward plausible paths to follow and away from implausible ones. Yet what is the nature of heuristics? What is the source of their power? How do they originate and evolve? By examining two case studies, the am and eurisko programs, we are led to some tentative hypotheses: Heuristics are compiled hindsight, and draw their power from the various kinds of regularity and continuity in the world; they arise through specialization, generalization, and—surprisingly often—analogy. Forty years ago, Polya introduced Heuretics as a separable field worthy of study. Today, we are finally able to carry out the kind of computation-intensive experiments which make such study possible. |
Year | DOI | Venue |
|---|---|---|
1982 | 10.1016/0004-3702(82)90036-4 | Artificial Intelligence |
Keywords | Field | DocType |
computer applications,information processing,artificial intelligence,expert system,rule based system,computer programming,high level languages | Information processing,Computer science,Separable space,Heuristics,High-level programming language,Computer Applications,Artificial intelligence,Hindsight bias,Computer programming,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 2 | 0004-3702 |
Citations | PageRank | References |
58 | 69.89 | 4 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Douglas B. Lenat | 1 | 1986 | 895.91 |