Tractable Algorithms for Sequential Decision Making Problems Nikhil Bhat Sequential decision making problems are ubiquitous in a number of research areas such as operations research, finance, engineering and computer science. The main challenge with these problems comes from the fact that, firstly, there...
Sequential Problems State Uncertainty Collaborative Agents Appendices A: Mathematical Concepts B: Probability Distributions C: Computational Complexity D: Neural Representations E: Search Algorithms F: Problems G: Julia 【斯坦福新书】决策算法,464页pdf,Algorithms for Decision Makingwww.zhuanzhi.ai/vip/01...
In data science, researchers typically deal with data that contain noisy observations. An important problem explored by data scientists in this context is the problem of sequential decision making. This is commonly known as a "stochastic multi-armed bandit"(stochastic MAB). Here, an intelligent age...
When choices are made in a decentralized manner by a set of decision makers, the problem can be modeled as a decentralized partially observable Markov decision process (DEC-POMDP). While POMDPs and DEC-POMDPs offer rich frameworks for sequential decision making under uncertainty, the computational...
Dynamic Programming (DP): DP is an optimization technique that is used to solve problems that can be broken down into smaller subproblems. It is particularly useful for problems that involve sequential decision-making, such as scheduling energy storage systems or optimizing the operation of a micro...
The MDP (Chang et al. [14], Panigrahi and Bhatnagar [8,9]) has been introduced to model hierarchical decision making problems. Chang et al. [14] propose a model called the multi-time scale Markov decision process (MMDP) for hierarchically structured sequential decision making processes. Show...
For instance, innovative AI models like the Spatio-Temporal Artificial Intelligence Network (STAIN) demonstrated the integration of CNN with RNN to analyze sequential images over time, providing a comprehensive analysis of COPD patient data in a temporal and spatial context. Additionally, some isolated...
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art
Neat!Solving the dual problem with SMOSMO (Sequential Minimal Optimization) is a type of coordinate ascent algorithm,but adapted to SVM so that the solution always stays within the feasibleregion.Start with (6). Lets say you want to hold 2, . . . , m fixed and take a coordinatestep in...
signal processing and computational statistics is the minimization of non-convex objective functions that may be non-differentiable at the relative boundary of the feasible set. This paper proposes a new family of first- and second-order interior-point methods for non-convex optimization problems with...