Conference proceeding
Concept Convergence in Empirical Domains
DISCOVERY SCIENCE, DS 2010, v 6332, pp 281-295
01 Jan 2010
Abstract
How to achieve shared meaning is a significant issue when more than one intelligent agent is involved in the same domain. We define the task of concept convergence, by which intelligent agents can achieve a shared, agreed-upon meaning of a concept (restricted to empirical domains). For this purpose we present a framework that, integrating computational argumentation and inductive concept learning, allows a pair of agents to (1) learn a concept in an empirical domain, (2) argue about the concept's meaning, and (3) reach a shared agreed-upon concept definition. We apply this framework to marine sponges, a biological domain where the actual definitions of concepts such as orders, families and species are currently open to discussion. An experimental evaluation on marine sponges shows that concept convergence is achieved, within a reasonable number of interchanged arguments, and reaching short and accurate definitions (with respect to precision and recall).
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Details
- Title
- Concept Convergence in Empirical Domains
- Creators
- Santiago Ontanon - Artificial Intelligence Research InstituteEnric Plaza - Artificial Intelligence Research Institute
- Contributors
- B Pfahringer (Editor)G Holmes (Editor)A Hoffmann (Editor)
- Publication Details
- DISCOVERY SCIENCE, DS 2010, v 6332, pp 281-295
- Series
- Lecture Notes in Artificial Intelligence
- Publisher
- Springer Nature
- Number of pages
- 15
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000312499300020
- Scopus ID
- 2-s2.0-78650120819
- Other Identifier
- 991021869114804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence