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AIMA4e Annotations
A companion to the great white brick.
As of November 23, 2022
(Start date: November 21, 2022.)
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PREFACE
The discipline: artificial intelligence "the study of agents that receive percepts from the environment and perform actions." (vii)
Aspects of the phenomenon:
I INTELLIGENCE --"Artificial Intelligence"
1 Intro:
definitions, foundations, history, philosophy, state of the art, risks-benefits
2 Agents:
environments, `good' behavior, agent structure and types
II SOLVING--"Problem-solving"
3 Searching:
Looking ahead to find a sequence.
Algorithms, strategies, informed/heuristic strategies.
4 Complex Environments:
More realistic environments.
Local search, optimization, continuous spaces, nondeterministic actions, partially observable env.s, online search and unknown env.s.
5 Adversarial Games:
Other agents competing against us.
Theory, optimal decisions, alpha-beta tree search, Monte Carlo tree search, stochastic g.s, partially observable g.s, limitations.
6 Constraint Satisfaction Problems:
States as domains, solutions as allowable combinations of states.
Constraint propagation, inference, backtracking search, local search, structure of problems
III THINKING--"Knowledge, reasoning, and planning"
7 Logical Agents:
Forming representations and reasoning before acting.
Knowledge-based agents; representing worlds; logic, world models and `possible worlds'; logic without objects.
8 First-Order Logic:
A formal language for objects and their relations.
`Ontological commitment' (what is assumed about reality); syntax, semantics; knowledge engineering (building formal representations of important objects and relations in a domain).
9 First-Order Inference:
Reasoning about objects and their relations.
Algorithms to answer any 1st-order logic question.
10 Knowledge Representation:
Representing the real world for problem solving.
What content to put into a knowledge base.
Knowledge representation languages and their uses (315):
* First-order logic: reasoning about a world of objects and relations;
* Hierarchical task networks: for reasoning about plans (chpt. 11);
* Bayesian networks: for reasoning with uncertainty (chpt. 13);
* Markov models: for reasoning over time (chpt. 17);
* Deep neural networks: for reasoning about images, sounds, other data (chpt. 21).
11 Automated Planning:
Hierarchical task networks.
Planning for spacecraft, factories, military campaigns; representing actions and states; efficient algorithms and heuristics.
IV UNCERTAINTY--"Uncertain knowledge and reasoning"
12 Quantifying Uncertainty:
An answer to the laziness and ignorance that kill formal logic.
Causes of uncertainty are environment types (partially observable, nondeterministic, adversarial); belief state grows big and unlikely fast (384); agents still need a way to act; absolute certainty is impossible; it comes down to importance, likelihood and degree of success (385-386).
Logic fails because laziness and ignorance; probability theory solves the qualification problem by summarizing the uncertainty.
* Laziness: too much work to list everything, or use such a list;
* Ignorance: (theoretical) there are no complete theories; (practical) we can never run all the tests.
13 Probabilistic Reasoning [big]:
Bayesian networks.
For reasoning with uncertainty by representing causal independence (398) and conditional independence (401) relationships to simplify probabilistic representations of the world.
14 Probabilistic Reasoning Over Time:
Comprehending the uncertain past, present and future.
Belief state plus transition model yields prediction (chpt 4, 7, 11); percepts and sensor model yield updated belief state; add probability theory to switch from possible states to probable states.
15 Probabilistic Programming:
Universal formal languages to represent any computable probability model, and they come with algorithms.
Using formal logic and traditional programming languages to represent probabilistic information.
16 Making Simple Decisions:
Agents getting what they want in an uncertain world--as much as possible, on average.
Beliefs, desires; utility theory; utility functions; decision networks; the value of information (547); this chapter is concerned with one-shot or episodic decsions problems (as opposed to sequential) (cf. 562).
17 Making Complex Decisions:
What to do today given decisions to be made tomorrow.
Sequential decision problems (as opposed to one-shot episodic): the agent's utility depends on a sequence of decisions in stochastic (explicitly probabilistic (45)) and partially observable environments. Markov models (563; cf. 463) for reasoning over time (chpt. 17).
18 Multiagent Decision Making [big]:
When there's more than one agent in the environment.
The nature of such environments and the strategies for problem-solving depend on the relationships between agents: non-cooperative and cooperative game theory; collective decision-making.
V LEARNING--"Machine learning"
19: 20: 21: deep neural networks: for reasoning about images, sounds, other data (chpt. 21). 22:
VI INTERACTING--"Communicating, perceiving, and acting"
23: 24: 25: 26:
VII CONCLUSIONS--"Conclusions"
27: 28:
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References