@aima

In which we explain how to build efficient network models to reason under uncertainty according to the laws of probability theory, and how to distinguish between correlation and causality. Flashcards What is a Bayesian network?? An efficient representation of full joint probability distributions that contain information about the relationships between random variables. Informally, what is a DAG?? A directed tree but nodes are allowed to have multiple parents. In a Bayesian network, what does $X \to Y$ represent?

In which we try to interpret the present, understand the past, and perhaps predict the future, even when very little is crystal clear. Summary The changing state of the world is handled by using a set of random variables to represent the state at each point in time. Representations can be designed to (roughly) satisfy the Markov property, so that the future is independent of the past given the present. Combined with the assumption that the process is _time-homogenous, this greatly simplifies the representation.

In which we show how to represent diverse facts about the real world in a form that can be used to reason and solve problems. Flashcards What is an ontology?? A set of concepts and categories in a subject area or domain that shows their properties and the relations between them. What are general-purpose ontologies?? Ontologies that make sense in lots of special-purpose domains because they are abstract and general. What is the difference between stuff and things?

In which we see how to tame uncertainty with numeric degrees of belief. Flashcards Why do we need probability theory?? Because it provides a reasonable framework for dealing with uncertainty. What is the decision theory “equation”?? $$ \text{decision theory} = \text{probability theory} + \text{utility theory} $$ What is the principle of maximum expected utility (MEU)?? An agent is rational if and only if it chooses the action that yields the highest expected utility, averaged over all possible outcomes of the action.

Flashcards What language is used to encode planning problems?? PDDL What is a hierarchical task network (HTN)?? A structure used to create plans where you can reason about high level actions without having to worry about every single step. What is an online planning agent?? One that replans in order to recover from unexpected situations. Backlinks [[AI: A Modern Approach]]N Metadata date: 2021-04-04 10:41 tags: - '@?notes' - '@?aima' - '@?public' title: 'AIMA: Automated Planning'

In which we define effective procedures for answering questions posed in first-order logic. This chapter was basically about how you can write inference procedures for first-order logic, like you could in propositional logic. I didn’t spend enough time reading it (I basically skimmed the last 15 pages) so my understanding here isn’t great. No flashcards :( Backlinks [[AI: A Modern Approach]]N Metadata date: 2021-04-04 10:41 tags: - '@?aima' - '@?

Flashcards What is the Sapir-Whorf hypothesis?? Our understanding of the world is strongly influenced by the language we speak. What three things does first-order logic assume exists in the world?? Facts Objects Relations What one thing does propositional logic assume exists in the world?? Facts What are the ontological commitments of a logic?? What it assumes about how reality is constructed. What is a relation in first-order logic?? Some relationship or property expressed by one or more objects.

Flashcards How do knowledge-based agents represent their knowlege?? Using a knowledge base. What is a knowledge base (KB) made out of?? Sentences. How are sentences represented in a knowledge base?? In a knowledge representation language. What in the notation for $\alpha$ entailing $\beta$?? $$ \alpha \models \beta $$ What is a model in logic?? A set of assignments for true and false. What does it mean for a sentence to be valid?? Given true premises, the sentence is always true.

In which we explore environments where other agents are plotting are against us. Games offer good places to explore search algorithms in practice since they model the uncertainties and difficulties of real life: the branching factor is high, there’s elements of chance, you might have partial observability, and maybe because a lot of the time you’re working against people. Flashcards What is the name of algorithm used to make optimal decision in perfect information games?

Flashcards What sort of representation do CSPs use in comparison to the atomic representation used by search algorithms?? A factored representation. What is the domain of a variable in a CSP?? The set of the variables it can take on. What are the constraints in a CSP?? Expressions that limit what values a variable can take on. What are inference techniques in CSPs?? Using constraints to rule out certain variable assignments. What is the minimum-remaining-values heuristic?

In which we relax the simplifying assumptions of the previous chapter, to get closer to the goal. [[AIMA: Solving Problems by Searching]]N was all about fully observable, deterministic, static, known environments where the solution is a sequence of actions. But the real world is rarely like this, and different algorithms need to be used for partially observable, non deterministic, dynamic and unknown environments (not neccessarily all at once). Flashcards What is local search?

“AI: A Modern Approach” is a textbook about Artificial Intelligence, written by Stuart Russell and Peter Norvig. I’m currently studying it outside of school with a few other people on a private Discord server. For an introduction to what the textbook is about, see [[AIMA: Introduction]]N. For an explanation of the note taking format, see [[Albatross, how I take notes]]B Table of Contents I: Introduction Chapter 01: [[AIMA: Introduction]]N Chapter 02: [[AIMA: Intelligent Agents]]N II: Problem Solving Chapter 03: [[AIMA: Solving Problems by Searching]]N Chapter 04: [[AIMA: Constraint Satisfaction Problems]]N Chapter 05: [[AIMA: Adversarial Search and Games]]N Chapter 06: [[AIMA: Search in Complex Environments]]N III: Knowledge, Reasoning, and Planning Chapter 07: [[AIMA: Logical Agents]]N Chapter 08: [[AIMA: First-Order Logic]]N Chapter 09: [[AIMA: Inference in First-Order Logic]]N Chapter 10: [[AIMA: Knowledge Representation]]N Chapter 11: [[AIMA: Automated Planning]]N IV: Uncertain Knowledge and Reasoning Chapter 12: [[AIMA: Quantifying Uncertainty]]N Chapter 13: [[AIMA: Probabilistic Reasoning]]N Chapter 14: [[AIMA: Probabilistic Reasoning Over Time]]N Chapter 15: [[AIMA: Probabilistic Programming]]?