An Anytime Algorithm for Decision Making under Uncertainty

01/30/2013
by   Michael C. Horsch, et al.
0

We present an anytime algorithm which computes policies for decision problems represented as multi-stage influence diagrams. Our algorithm constructs policies incrementally, starting from a policy which makes no use of the available information. The incremental process constructs policies which includes more of the information available to the decision maker at each step. While the process converges to the optimal policy, our approach is designed for situations in which computing the optimal policy is infeasible. We provide examples of the process on several large decision problems, showing that, for these examples, the process constructs valuable (but sub-optimal) policies before the optimal policy would be available by traditional methods.

READ FULL TEXT

page 1

page 3

page 4

page 5

page 6

page 7

page 9

page 10

research
09/17/2022

Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control

Flocking control is a challenging problem, where multiple agents, such a...
research
04/17/2023

Designing Policies for Truth: Combating Misinformation with Transparency and Information Design

Misinformation has become a growing issue on online social platforms (OS...
research
07/26/2022

A Learning and Control Perspective for Microfinance

Microfinance in developing areas such as Africa has been proven to impro...
research
07/21/2020

Flow Sampling: Accurate and Load-balanced Sampling Policies

Software-defined networking simplifies network monitoring by means of pe...
research
12/31/2015

Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies

We consider effort allocation in crowdsourcing, where we wish to assign ...
research
11/02/2020

Optimal Policies for the Homogeneous Selective Labels Problem

Selective labels are a common feature of consequential decision-making a...
research
03/14/2022

Optimal Aggregation Strategies for Social Learning over Graphs

Adaptive social learning is a useful tool for studying distributed decis...

Please sign up or login with your details

Forgot password? Click here to reset