Paper Info
Title
Real-time sensory pattern mining for autonomous agents
Abstract
Autonomous agents are systems situated in dynamic environments. They pursue goals and satisfy their needs by responding to external events from the environment. In these unpredictable conditions, the agents' adaptive skills are a key factor for their success. Based on previous interactions with its environment, an agent must learn new knowledge about it, and use that information to guide its behavior throughout time. In order to build more believable agents, we need to provide them with structures that represent that knowledge, and mechanisms that update them overtime to reflect the agents' experience. Pattern mining, a subfield of data mining, is a knowledge discovery technique which aims to extract previously unknown associations and causal structures from existing data sources. In this paper we propose the use of pattern mining techniques in autonomous agents to allow the extraction of sensory patterns from the agent's perceptions in realtime. We extend some structures used in pattern mining and employ a statistical test to allow an agent of discovering useful information about the environment while exploring it.
Year
DOI
Venue
2010
10.1007/978-3-642-15420-1_7
ADMI
Keywords
Field
DocType
data mining,sensory pattern,new knowledge,autonomous agent,knowledge discovery technique,dynamic environment,real-time sensory pattern mining,believable agent,pattern mining,data source,pattern mining technique,statistical test,autonomous agents,adaptive learning,satisfiability,adaptation,real time,knowledge discovery
Situated,Data mining,Autonomous agent,Computer science,Artificial intelligence,Knowledge extraction,Sensory system,Perception,Statistical hypothesis testing,Machine learning
Conference
Volume
ISSN
ISBN
5980
0302-9743
3-642-15419-0
Citations 
PageRank 
References 
2
0.39
9
Authors
2
Name
Order
Citations
PageRank
Pedro Sequeira112714.48
Cláudia Antunes216116.57