Title
Generation of human computational models with knowledge engineering
Abstract
The Ambient Intelligence (AmI) paradigm envisions systems whose central entity is the user. AmI integrates technologies such as Artificial Intelligence, implicit Human Computer Interaction, and Ubiquitous Services. Each capability of AmI systems is oriented towards assistance of humans at work, in the classroom, or even at home. In consequence, the AmI development process usually incorporates the final user since the first stages. However, having users available during all this long process is not always possible. Agent-based social simulations where the [email protected]? role is played by simulated entities can be used to make the AmI development process faster and more effective. In this scenario, the modelling of CMHBs (Computational Models of Human Behaviour) is a major challenge. To address this issue, this paper proposes a methodology whose main contributions are: (1) the use of domain [email protected]? knowledge to create CMHBs; (2) a common methodological framework to develop CMHBs by combining information obtained from [email protected]? perceptions and [email protected]? experiences; and, (3) open source tools to support this development paradigm. The paper also presents a full case of study in a hospital which illustrates: the number of recommendations made by the methodology; the techniques proposed (mainly the use of ontologies and temporal reasoning); and, the potential of the methodology to model the personnel in a hospital.
Year
DOI
Venue
2014
10.1016/j.engappai.2014.06.027
Engineering Applications of Artificial Intelligence
Keywords
Field
DocType
ambient intelligence systems engineering,agent-based social simulation,knowledge acquisition,computational models of humans,smart environments fast prototyping and testing,agent based social simulation
Ontology (information science),Agent-based social simulation,Ambient intelligence,Computer science,Computational model,Knowledge engineering,Artificial intelligence,Perception,Machine learning,Knowledge acquisition
Journal
Volume
Issue
ISSN
35
1
0952-1976
Citations 
PageRank 
References 
1
0.36
47
Authors
4
Name
Order
Citations
PageRank
Francisco Campuzano1565.59
Teresa García-Valverde2626.88
Emilio Serrano3333.96
Juan A. Botía437035.47