Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico
Abstract
:1. Introduction
1.1. Interaction Levels
1.2. Museum User-Exhibition Interaction
2. Related Work
3. Methodology
3.1. Case Study
3.1.1. Room and Exhibition Module Selection
3.1.2. Exhibition Module Interface
3.2. Study Subjects
Evaluation Interaction Parameters
4. The Model
4.1. Modelling User-Exhibition Elements
4.1.1. Representing Interaction Levels Using a Fuzzy Inference System
4.1.2. Implementing the Fuzzy Inference System
4.2. Validating the Fuzzy Inference System
5. Results
5.1. The Intelligent Hybrid System Approach
5.2. The Decision Tree Approach
5.3. The Data Mined Type-1 FIS Approach
5.4. Neuro-Fuzzy System Approach
5.5. Empirical FIS Approach Versus Hybrid FIS Approach
6. Discussion
6.1. ’El Trompo’ as a Complex Sociotechnical System
6.1.1. Human-Agent Interaction
6.1.2. The Intelligent Interactive-Exhibit System
6.1.3. Knowledge-based Agent and Agent Architecture
6.1.4. Agent-Oriented Software Engineering
7. Conclusions and Future Work
Supplementary Materials
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Mendel, J.; Wu, D. Perceptual Computing, Aiding People in Making Subjetive Judgments; IEEE Press: Hoboken, NJ, USA, 2010. [Google Scholar]
- Zadeh, L. Fuzzy sets. In Information and Control; Prentice Hall: Upper Saddle River, NJ, USA, 1965; Volume 8, pp. 338–353. [Google Scholar]
- Zadeh, L. The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 1975, 8, 199–249. [Google Scholar] [CrossRef]
- Bezdek, J. FCM: The fuzzy c-means clustering algorithm. Comput. Geosci. 1984, 10, 191–203. [Google Scholar] [CrossRef]
- Yin, X.; Khoo, L.; Chong, Y. A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain. Comput. Ind. Eng. 1984, 66, 768–780. [Google Scholar] [CrossRef]
- Rantala, J.; Koivisto, H.A.N.N.U. Optimised Subtractive Clustering for Neuro-Fuzzy Models. In Proceedings of the 3rd WSEAS International Conference on Fuzzy Sets and Fuzzy Systems, Interlaken, Switzerland, 11–14 February; 2002; pp. 3971–3976. [Google Scholar]
- Castro, J.R.; Castillo, O.; Melin, P.; Rodríguez-Díaz, A. A hybrid learning algorithm for a class of interval type–2 fuzzy neural networks. J. Inf. Sci. 2008, 179, 2175–2193. [Google Scholar] [CrossRef]
- Gayesky, D.; Williams, D. Interactive Video in Higher Education. In Video in Higher Education; Zubber-Skerrit, O., Ed.; Kogan Page: London, UK, 1984. [Google Scholar]
- Rosenfeld, A.; Agmon, N.; Maksimov, O.; Kraus, S. Intelligent agent supporting human-multi-robot team collaboration. Artif. Intell. 2017, 252, 211–231. [Google Scholar] [CrossRef]
- Rosenfeld, A.; Sarit, K. Providing arguments in discussions on the basis of the prediction of human argumentative behavior. ACM Trans. Interact. Intell. Syst. 2016, 6, 30. [Google Scholar] [CrossRef]
- Casell, J. Embodied Conversational Agents; MIT Press: Cambridge, MA, USA, 2000. [Google Scholar]
- Berg, M.M. Modelling of Natural Dialogues in the Context of Speech-based Information and Control Systems. Ph.D. Thesis, Christian-Albrechts University of Kiel, Kiel, Germany, 2015. [Google Scholar]
- Garruzo, S.; Rosaci, D. Agent Clustering based on Semantic Negotiation. ACM Trans. Auton. Adapt. Syst. 2008, 3, 7. [Google Scholar] [CrossRef]
- Rosaci, D. CILIOS: Connectionist Inductive Learning and Inter-Ontology Similarities for Recommending Information Agents. Inf. Syst. 2007, 32, 793–825. [Google Scholar] [CrossRef]
- Cerekovic, A.; Aran, O.; Gatica-Perez, D.D. Rapport with virtual agents: What do human social cues and personality explain? IEEE Trans. Affect. Comput. 2017, 8, 382–395. [Google Scholar] [CrossRef]
- Tickle-Degnen, D.L.; Rosenthal, R. The nature of rapport and its nonverbal correlates. Psychol. Inq. 1990, 1, 285–293. [Google Scholar] [CrossRef]
- Sanchez-Cortes, D.; Aran, O.; Jayagopi, D.; Mast, M.S.; Gatica-Perez, D. Emergent leaders through looking and speaking: From audio-visual data to multimodal recognition. IEEE J. Multimodal User Interfaces 2017, 7, 39–53. [Google Scholar] [CrossRef]
- Schroder, M.; Bevacqua, E.; Cowie, R.; Eyben, F.; Gunes, H.; Heylen, D.; Ter Maat, M.; McKeown, G.; Pammi, S.; Pantic, M.; et al. Building autonomous sensitive artificial listeners. IEEE Trans. Affect. Comput. 2012, 3, 165–183. [Google Scholar] [CrossRef] [Green Version]
- Rosaci, D.; Sarne, G.M.L. MASHA: A Multi Agent System Handling User and Device Adaptivity of Web Sites. In User Modeling and User-Adapted Interaction (UMUAI); Springer: Berlin, Germany, 2006; Volume 16, pp. 435–462. [Google Scholar]
- Wooldridge, M.; Jennings, N.R.; Kinny, D. The Gaia methodology for agent-oriented analysis and design. Auton. Agents Multi-Agent Syst. 2000, 3, 285–312. [Google Scholar]
- Gilbert, N. Agent-Based Models; SAGE Publications: Newcastle upon Tyne, UK, 2008. [Google Scholar]
- Cioffi-Revilla, C. Simulations I: Methodology. In Introduction to Computational Social Science. Texts in Computer Science; Springer: London, UK, 2014. [Google Scholar]
- Rosales, R.; Castañón-Puga, M.; Lara-Rosano, F.; Evans, R.D.; Osuna-Millan, N.; Flores-Ortiz, M.V. Modelling the Interruption on HCI Using BDI Agents with the Fuzzy Perceptions Approach: An Interactive Museum Case Study in Mexico. Appl. Sci. 2017, 7, 832. [Google Scholar] [CrossRef]
- Barros, L.; Bassanezi, R. Topicos de Logica Fuzzy e Biomatematica. In Universidad de Estadual de Campinas (Unicamp): IMECC; Universidade Estadual de Campinas: Campinas, Brazil, 2006. [Google Scholar]
- Bellifemine, F.; Caire, G.; Greenwood, D. Developing Multi-Agent Systems with JADE; John Wiley and Sons: Hoboken, NJ, USA, 2007. [Google Scholar]
- Sivanandam, S.; Sumathi, S.; Deepa, S. Introduction to Fuzzy Logic Using Matlab; Springer: Berlin/Heidelberg, Germany, 2007. [Google Scholar]
- Castanon-Puga, M.; Castro, J.; Flores-Parra, J.; Gaxiola-Pacheco, C.G.; Martínez-Méndez, L.G.; Palafox-Maestre, L.E. JT2FIS A Java Type-2 Fuzzy Inference Systems Class Library for Building Object-Oriented Intelligent Applications. In Proceedings of the 12th Mexican International Conference on Artificial Intelligence, Mexico City, Mexico, 24–30 November 2013; Volume 8266, pp. 204–215. [Google Scholar]
No | Inference Fuzzy Rules |
---|---|
1 | If (Presence is Very Bad) and (Interactivity is Very Bad) and (Control is Very Bad) and (FeedBack is Very Bad) and (Creativity is Very Bad) and (Productivity is Very Bad) and (Communication is Very BAD) and (Adaptation is Very Bad) then ( Level 0 is High) (Level 1 is Low) (Level 2 is Low) (Level 3 is Low) (Level 4 is Low) (Level 5 is Low). |
2 | If (Presence is Bad) and (Interactivity is Bad) and (Control is Bad) and (FeedBack is Bad) and (Creativity is Bad) and (Productivity is Bad) and (Communication is Bad) and (Adaptation is Bad) then ( Level 0 is Low) (Level 1 is High) (Level 2 is Low) (Level 3 is Low) (Level 4 is Low) (Level 5 is Low). |
3 | If (Presence is Regular) and (Interactivity is Regular) and (Control is Regular) and (FeedBack is Regular) and (Creativity is Regular) and (Productivity is Regular) and (Communication is Regular) and (Adaptation is Regular) then ( Level 0 is Low) (Level 1 is Low) (Level 2 is High) (Level 3 is Low) (Level 4 is Low) (Level 5 is Low). |
4 | If (Presence is Good) and (Interactivity is Good) and (Control is Good) and (FeedBack is Good) and (Creativity is Good) and (Productivity is Good) and (Communication is Good) and (Adaptation is Good) then (Level 0 is Low) (Level 1 is Low) (Level 2 is Low) (Level 3 is High) (Level 4 is Low) (Level 5 is Low). |
5 | If (Presence is Very Good) and (Interactivity is Very Good) and (Control is Very Good) and (FeedBack is Very Good) and (Creativity is Very Good) and (Productivity is Very Good) and (Communication is Very Good) and (Adaptation is Very Good) then ( Level 0 is Low) (Level 1 is Low) (Level 2 is Low) (Level 3 is Low) (Level 4 is High) (Level 5 is Low). |
6 | If (Presence is Excellent) and (Interactivity is Excellent) and (Control is Excellent) and (FeedBack is Excellent) and (Creativity is Excellent) and (Productivity is Excellent) and (Communication is Excellent) and (Adaptation is Excellent) then (Level 0 is Low) (Level 1 is Low) (Level 2 is Low) (Level 3 is Low) (Level 4 is Low)(Level 5 is High). |
Predicted Class | Empirical FIS | Decision Tree | Data Mined Type-1 | Neuro-Fuzzy System |
---|---|---|---|---|
Level 0 | 71.4/28.6 | 50/50 | 0/100 | 60/40 |
Level 1 | 76.9/23.1 | 71.4/28.6 | 68.8/31.2 | 100/0 |
Level 2 | 64.6/35.4 | 47.4/52.6 | 69.2/30.8 | 66.7/33.3 |
Level 3 | 66.7/33.3 | 66.7/33.3 | 66.7/33.3 | 91.7/8.3 |
Level 4 | 74.4/25.6 | 84.5/15.5 | 93.8/6.2 | 95.2/4.8 |
Level 5 | 97.8/2.2 | 83.3/16.7 | 82.6/17.4 | 100/0 |
Overall Accuracy | 76/24 | 75.3/24.7 | 80.7/19.3 | 91.3/8.7 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rosales, R.; Castañón-Puga, M.; Lara-Rosano, F.; Flores-Parra, J.M.; Evans, R.; Osuna-Millan, N.; Gaxiola-Pacheco, C. Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Appl. Sci. 2018, 8, 446. https://doi.org/10.3390/app8030446
Rosales R, Castañón-Puga M, Lara-Rosano F, Flores-Parra JM, Evans R, Osuna-Millan N, Gaxiola-Pacheco C. Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Applied Sciences. 2018; 8(3):446. https://doi.org/10.3390/app8030446
Chicago/Turabian StyleRosales, Ricardo, Manuel Castañón-Puga, Felipe Lara-Rosano, Josue Miguel Flores-Parra, Richard Evans, Nora Osuna-Millan, and Carelia Gaxiola-Pacheco. 2018. "Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico" Applied Sciences 8, no. 3: 446. https://doi.org/10.3390/app8030446