{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Intelligent Agents and Active Inference" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Preliminaries\n", "\n", "- Goal \n", " - Introduction to Active Inference and application to the design of synthetic intelligent agents \n", "- Materials \n", " - Mandatory\n", " - These lecture notes\n", " - Karl Friston - 2016 - [The Free Energy Principle](https://www.youtube.com/watch?v=NIu_dJGyIQI) (video)\n", " - Optional\n", " - Raviv (2018), [The Genius Neuroscientist Who Might Hold the Key to True AI](https://www.wired.com/story/karl-friston-free-energy-principle-artificial-intelligence/).\n", " - Interesting article on Karl Friston, who is a leading theoretical neuroscientist working on a theory that relates life and intelligent behavior to physics (and Free Energy minimization). (**highly recommended**) \n", " - Kirsch (2019), [Theories of Intelligence: Active Inference](http://louiskirsch.com/ai/active-inference) \n", " - A nice tutural blog on active inference.\n", " - Van de Laar and De Vries (2019), [Simulating Active Inference Processes by Message Passing](https://www.frontiersin.org/articles/10.3389/frobt.2019.00020/full)\n", " - How to implement active inference by message passing in a Forney-style factor graph.\n", "\n", " - References\n", " - Friston (2013), [Life as we know it](https://royalsocietypublishing.org/doi/full/10.1098/rsif.2013.0475) \n", " - Conant and Ashby (1970), [Every good regulator of a system must be a model of that system](https://www.tandfonline.com/doi/abs/10.1080/00207727008920220)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Agents\n", "\n", "- In the previous lessons we assumed that a data set was given. \n", "- In this lesson we consider _agents_. An agent is a system that _interacts_ with its environment through both sensors and actuators.\n", "- Crucially, by acting onto the environment, the agent is able to affect the data that it will sense in the future.\n", " - As an example, by changing the direction where I look, I can affect the sensory data that will be sensed by my retina.\n", "- With this definition of an agent, (biological) organisms are agents, and so are robots, self-driving cars, etc.\n", "- In an engineering context, we are particularly interesting in agents that behave with a *purpose* (with a goal in mind), e.g., to drive a car or to design a speech recognition algorithm.\n", "- In this lesson, we will describe how __goal-directed behavior__ by biological (and synthetic) agents can also be interpreted as minimization of a free energy functional $F[q]$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Illustrative Example: Steering a cart to a parking spot\n", "\n", "- In this example, we consider a cart that can move in a 1D space. At each time step the cart can be steered a bit to the left or right by a controller (the \"agent\"). The agent's knowledge about the cart's process dynamics (equations of motion) are known up to some additive Gaussian process noise. The agent also makes noisy observations of the position and velocity of the cart. Your challenge is to design an agent that steers the car to the zero position. (The agent should be specified as a probabilistic model and the control signal should be formulated as a Bayesian inference task). \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "