Reward是表达task对于每个state的偏好，序列决策问题的目标就是最大化获得的reward。Action则是在转移中引...
Decision-making in an environment of uncertainty and imprecision for real-world problems is a complex task. In this paper it is introduced general finite state fuzzy Markov chains that have a finite convergence to a stationary (may be periodic) solution. The Cesaro average and the 伪-potential ...
Markov decision processes exemplify sequential problems, which are defined by a transition model and a reward function, situated in uncertain environments. The environment we will be observing is the grid world, which is analogous to the problem of solving for the best moves in a board game. Our...
Multi-objective Markov decision processes (MOMDPs) provide an effective modeling framework for decision-making problems involving water systems. The tradit... Pianosi,F,Castelletti,... - 《Journal of Hydroinformatics》 被引量: 19发表: 2013年 Validation and optimization of an elevator simulation mode...
task planninguncertaintydecision-makingIn decision theoretic planning, a challenge for Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs) is, many problem domains contain big state spaces and complex tasks, which will result in poor solution performance. We ...
Abstract 背景:马尔可夫决策过程(Markov decision process, MDP)是串联决策问题(sequential decision making)的一种数学化建模;机器学习已经为MDP提供了很多解法,但这些解法没有被严格测试过,或者不真正可靠(Q?) 本文:MDPFuzz Github:https://github.com/Qi-Pang/MDPFuzz ...
tion probabilities themselves are uncertain, and seek a robust decision for it. Our work is motivated by the fact that in many practical problems, the transition matrices have to be estimated from data. This may be a difficult task and the estimation errors may have a huge impact on the...
As one of the major contributions of biology to competitive decision making,evolutionary game theory provides a useful tool for studying the evolution of cooperation.To achieve the optimal solution for unmanned aerial vehicles(UAVs) that are carrying out a sensing task,this paper presents a Markov ...
Active classification, i.e., the sequential decision-making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking. In this work, we study the problem of actively classifying dynamical syst...
In addition, these distributions feature heavily in models of decision making [41], where creatures must select between alternative plans of action. 7. Discussion 7.1. Memory in Markov Blanketed Systems We started this paper by outlining the importance of a Markov blanket. In brief, a blanket ...