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1 thg 7, 2025 · The primary objective is to develop an anomaly identification approach that enhances sensor network security and operational efficiency with a good degree of accuracy.
16 thg 8, 2024 · By employing a factored MDP model, we provide a comprehensive and realistic system representation. We also incorporate incremental updates to an attack response predictor as new data emerges. This ensures an adaptive and robust defense mechanism.
The decision-making module plays a critical role in autonomous vehicles (AVs). There are two main challenges in decision-making for autonomous driving: accurate.
1 thg 7, 2025 · When applying MDP to network attack detection, it is necessary to define the state space, action space, and reward function appropriately and establish a state transition probability that matches the network security environment.
This study proposes an automatic detection model based on MDP, which dynamically analyzes network traffic and system behavior while continuously improving detection accuracy through...
1 thg 5, 2025 · We start with the case of two MDPs and establish a necessary and sufficient condition for the existence of policies that lead to perfect detection. Based on this condition, we then develop an algorithm that efficiently (in time polynomial in the size of the MDPs) determines the existence of policies and synthesizes one when they exist.
Optimal control in non-stationary Markov decision processes (MDP) is a challenging problem. The aim in such a control problem is to maximize the long-term disco.
We demonstrate the effectiveness of our algorithms through two numerical examples, i.e., intruder detection in urban environments and an MDP-based recommendation system.
This chapter first provides the fundamental background and theory of the Markov decision process (MDP), a critical mathematical framework for modeling decision‐
28 thg 12, 2020 · In this paper we have suggested an MDP-based approach to cost sensitive classification, considering both test costs and misclassification costs. The MDP approach provides a sound method for reasoning about future actions, instead of considering only the myopic value of …
28 thg 1, 2022 · The paper presents an approach to the MDP based on the construction of a decision tree that is expected to well approximate the optimal policy while reducing the complexity of computing such an effective policy.
Specifically, we propose a computationally efficient, simple two-threshold strategy to quickly detect model changes, without significant loss in rewards. We show that such an approach is superior to the existing methods in the literature.
24 thg 2, 2025 · Markov Decision Processes provide a powerful and flexible framework for modeling decision-making problems in uncertain environments. Their relevance to Reinforcement Learning cannot be overstated, as MDPs underpin the theoretical foundation of RL algorithms.
29 thg 9, 2024 · This chapter will introduce the most famous, most classical, and most important model in RL: Discrete-Time Markov Decision Process (DTMDP).
28 thg 5, 2025 · In artificial intelligence Markov Decision Processes (MDPs) are used to model situations where decisions are made one after another and the results of actions are uncertain.
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