Intelligent decision-making approach based on fuzzy-causal knowledge and reasoning

Authors:
Alejandro Peña-Ayala;Riichiro Mizoguchi
Affiliations:
WOLNM, Leyes Reforma, DF, Mexico,ESIME-Z IPN, Leyes Reforma, DF, Mexico,Institute of Scientific and Industrial Research, Osaka University, Japan;Institute of Scientific and Industrial Research, Osaka University, Japan
Venue:
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Year:
2012

Citing 9
Cited 0

Fuzzy relation equations and causal reasoning

Fuzzy Sets and Systems - Special issue: fuzzy relations, part 2
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
Adaptive and intelligent web based education system: Towards an integral architecture and framework

Expert Systems with Applications: An International Journal
Review: Causal knowledge and reasoning by cognitive maps: Pursuing a holistic approach

Expert Systems with Applications: An International Journal
Bayesian Network Inference with Qualitative Expert Knowledge for Decision Support Systems

SNPD '10 Proceedings of the 2010 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Semantic representation and management of student models: an approach to adapt lecture sequencing to enhance learning

MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Cognitive computing

Communications of the ACM
Using fuzzy cognitive maps as a decision support system for political decisions: the case of Turkey’s integration into the European union

PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Common sense reasoning – from cyc to intelligent assistant

Ambient Intelligence in Everyday Life

Quantified Score

Hi-index	0.00

Visualization

Abstract

Our intelligent decision-making approach (IDMA) is an instance of cognitive computing. It applies causality as common sense reasoning and fuzzy logic as a representation for qualitative knowledge. Our IDMA collects raw knowledge of humans through psychological models to tailor a knowledge-base (KB). The KB manages different repositories (e.g., cognitive maps (CM) and an ontology) to depict the object of study. The IDMA traces fuzzy-causal inferences to simulate causal behavior and estimate causal outcomes for decision-making. In order to test our approach, it is linked to the sequencing module of an intelligent and adaptive web-based educational system (IAWBES). It is used to provide student-centered education and enhance the students' learning by intelligent and adaptive functionalities. The results reveal users of an experimental group reached 17% of better learning than their peers of the control group.