Discrete Event Modeling and Simulation of Markov Decision Process.

Emanuele Barbieri

ISBN 10: 6206846695 / ISBN 13: 9786206846697

Verlag: LAP LAMBERT Academic Publishing Nov 2023, 2023

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -Markov Decision Process (MDP) models are widely used to model decision-making problems in many research fields. MDPs can be readily designed through modeling and simulation(M&S) using the Discrete Event System Specification formalism (DEVS) due to its modular and hierarchical aspects, which improve the explainability of the models. In particular, the separation between the agent and the environment components involved in the traditional reinforcement learning (RL) algorithm, such as Q-Learning, is clearly formalized to enhance observability and envision the integration of AI components in the decision-making process. Our proposed DEVS model also improves the trust of decision makers by mitigating the risk of delegation to machines in decision-making processes. The main focus of this work is to provide the possibility of designing a Markovian system with a modeling and simulation formalism to optimize a decision-making process with greater explainability through simulation. Furthermore, the work involves an investigation based on financial process management, its specification as an MDP-based RL system, and its M&S with DEVS formalism. 168 pp. Englisch.

Bestandsnummer des Verkäufers 9786206846697

Bibliografische Details

• Titel: Discrete Event Modeling and Simulation of Markov Decision Process.
• Verlag: LAP LAMBERT Academic Publishing Nov 2023
• Veröffentlichungsdatum: 2023
• Sprache: Englisch
• ISBN-10: 6206846695
• ISBN-13: 9786206846697
• Bindung: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

Markov Decision Process (MDP) models are widely used to model decision-making problems in many research fields. MDPs can be readily designed through modeling and simulation(M&S) using the Discrete Event System Specification formalism (DEVS) due to its modular and hierarchical aspects, which improve the explainability of the models. In particular, the separation between the agent and the environment components involved in the traditional reinforcement learning (RL) algorithm, such as Q-Learning, is clearly formalized to enhance observability and envision the integration of AI components in the decision-making process. Our proposed DEVS model also improves the trust of decision makers by mitigating the risk of delegation to machines in decision-making processes. The main focus of this work is to provide the possibility of designing a Markovian system with a modeling and simulation formalism to optimize a decision-making process with greater explainability through simulation. Furthermore, the work involves an investigation based on financial process management, its specification as an MDP-based RL system, and its M&S with DEVS formalism.