---
title: "decision making"
type: concept
related: [Uncertainty, Planning, Observe Act Cycle, Reinforcement Learning, Supervised Learning]
source: https://www.jemoka.com/posts/kbhdecision_making/
confidence: high
status: active
---

Key components Task/Objective (&ldquo;Automated Driving to reach destination [here]&rdquo;) Resources (state) (&ldquo;sensors, fuel, etc.&rdquo;) Uncertainties (&ldquo;What in the world is happening&rdquo;) Actions (&ldquo;turn left&rdquo;) In one line: an agent makes decisions via the balance of observation with uncertainty. This is called the observe-act cycle.
See also connectionism
Applications Stock shelving Automated driving Space missions Sports Congestion modeling Online dating Traffic light control decision making methods explicit programming: &ldquo;just code it up&rdquo; &mdash; try this first if you are building something, which should establish a baseline: guess all possible states, and hard code strategies for all of them supervised learning: manually solve representative states, hard code strategies for them, make model interpolate between them optimization: create optimization objective connected to a model of the environment, optimize that objective planning: using model of the environment directly to predict best moves reinforcement learning: make agent interact with environment directly, and optimize its score of success in the environment without a model Method Model Visible? Strategy Hard-Coded? explicit programming yes, all states fully known yes supervised learning no, only a sample of it yes, only a sample of it optimization no, except reward no planning yes no reinforcement learning history see decision making history
