Authors:
Paulo Costa
and
Luis Botelho
Affiliation:
Instituto de Telecomunicações/ISCTE-Instituto Universitario de Lisboa, Portugal
Keyword(s):
Machine learning, Learning algorithms, Learning by observation, Software image, Software agents.
Related
Ontology
Subjects/Areas/Topics:
Agent Models and Architectures
;
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Case-Based Reasoning
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Multi-Agent Systems
;
Pattern Recognition
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
;
Theory and Methods
Abstract:
In a society of similar agents, all of them using the same kind of knowledge representation, learning with others could be achieved through direct transfer of knowledge from experts to apprentices. However, not all agents use the same kind of representation methods, hence learning by direct communication of knowledge is not always possible. In such cases, learning by observation might be of key importance. This paper presents an agent architecture that provides software agents with learning by observation capabilities similar to those observed in superior mammals. The main contribution of our proposal is to let software agents learn by direct observation of the actions being performed by expert agents. This is possible because, using the proposed architecture, agents may see one another.