Perkembangan Perilaku Pembelajaran Peserta Didik dengan Menggunakan Partially Observable Markov Decision Processes

Authors

  • Maxtulus Junedy Nababan Universitas Quality

DOI:

https://doi.org/10.62383/edukasi.v2i1.1034

Keywords:

Markov Model, Learning Behaviours, Multi-Agent

Abstract

The School Community can be considered as an agent in a multi-agent system because there is dynamic interaction in a school system. The important thing in a multi-agent system is managing relationships between agents to achieve coordinated behavior by developing knowledge, attitudes and practices, namely the factors that shape environmental behavior. This research extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the idea of ​​agent models into the state space. The results of this research show that (POMDP) ​​is effective for understanding and managing learning behavior in schools.

References

Boyen, X., & Koller, D. (2013). Tractable inference for complex stochastic processes. ArXiv Preprint ArXiv:1301.7362.

Dean, T., Boutilier, C. T., & Hanks, S. (1999). Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 11, 1–94.

Doucet, A., De Freitas, N., & Gordon, N. J. (2001). Sequential Monte Carlo methods in practice (Vol. 1, Issue 2). Springer.

Hauskrecht, M. (2000). Value-function approximations for partially observable Markov decision processes. Journal of Artificial Intelligence Research, 13, 33–94.

Jennings, N. R. (1996). Coordination techniques for distributed artificial intelligence.

Jensen, D., Atighetchi, M., Vincent, R., & Lesser, V. (1999). Learning quantitative knowledge for multiagent coordination. AAAI/IAAI, 24–31.

Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1–2), 99–134.

Kanazawa, K., & Dean, T. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(2), 142–150.

Koller, D., & Lerner, U. (2001). Sampling in factored dynamic systems. In A. Doucet, N. De Freitas, & N. J. Gordon (Eds.), Sequential Monte Carlo Methods in Practice (pp. 445–464). Springer.

Lesser, V., Decker, K., Wagner, T., Carver, N., Garvey, A., Horling, B., Neiman, D., Podorozhny, R., Prasad, M. N., & Raja, A. (2004). Evolution of the GPGP/TAEMS domain-independent coordination framework. Autonomous Agents and Multi-Agent Systems, 9(1), 87–143.

Li, T., Choi, M., Fu, K., & Lin, L. (2019). Music sequence prediction with mixture hidden Markov models. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 6128–6132).

Metelli, A. M., Mutti, M., & Restelli, M. (2018). Configurable Markov decision processes. In International Conference on Machine Learning (pp. 3491–3500).

Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). Focus article: On the structure of educational assessments. Measurement: Interdisciplinary Research and Perspectives, 1(1), 3–62.

Monahan, G. E. (1982). State of the art—a survey of partially observable Markov decision processes: Theory, models, and algorithms. Management Science, 28(1), 1–16.

Murphy, K., & Russell, S. (2001). Rao-Blackwellised particle filtering for dynamic Bayesian networks. In A. Doucet, N. De Freitas, & N. J. Gordon (Eds.), Sequential Monte Carlo Methods in Practice (pp. 499–515). Springer.

Takikawa, M., D’Ambrosio, B., & Wright, E. (2012). Real-time inference with large-scale temporal Bayes nets. ArXiv Preprint ArXiv:1301.0603.

Wooldridge, M. (1999). Intelligent agents. In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (pp. 27–73).

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Published

2024-12-13

How to Cite

Maxtulus Junedy Nababan. (2024). Perkembangan Perilaku Pembelajaran Peserta Didik dengan Menggunakan Partially Observable Markov Decision Processes. Edukasi Elita : Jurnal Inovasi Pendidikan, 2(1), 289–297. https://doi.org/10.62383/edukasi.v2i1.1034