(The below text version of the notes is for search purposes and convenience. See the PDF version for proper formatting such as bold, italics, etc., and graphics where applicable. Copyright: 2022 Retraice, Inc.)
AIMA4e Annotations
A companion to the great white brick.
As of November 25, 2022
(Start date: November 21, 2022.)
[1]retraice.com
Version notes: [2]Retraice ([3]2022/11/21) (Re57), first draft, covered Preface, Sections I, II; [4]Retraice ([5]2022/11/22) (Re58), no footnotes, covered Sections III, IV; [6]Retraice ([7]2022/11/22) (Re58) again, moved some notes from Re57 and Re58 notes to footnotes here;
[8]Retraice ([9]2022/11/23) (Re59), covered Sections V, VI, VII; [10]Retraice ([11]2022/11/24) (Re60), covered Appendix A.
PREFACE
* The phenomenon: intelligent agents[12]^1
* The discipline: artificial intelligence,[13]^2 "the study of agents that receive percepts from the environment and perform actions." (vii)
* Aspects of the phenomenon:
+ Agent function: "Each ...agent implements a function that maps percept sequences to actions" (vii)
o Ways to represent agent functions include: "reactive agents, real-time planners, decision-theoretic systems, and deep learning systems." (vii)
+ Learning
o "a construction method for competent systems" (viii)
o "a way of extending the reach of the designer into unknown environments." (viii)
+ Goals
o Robotics and vision:
# "not ...independently defined problems"
# "[things] in the service of achieving goals."
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[14]^3 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[15]^4 worlds; logic, world models and `possible worlds';[16]^5 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[17]^6 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,[18]^7 nondeterministic, adversarial[19]^8 ); belief state grows big and unlikely fast (384); agents still need a way to act; absolute certainty is impossible;[20]^9 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.[21]^10
* 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.
[22]^11
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.[23]^12
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);[24]^13 this chapter is concerned with one-shot or episodic decisions problems (as opposed to sequential) (cf. 562, below).
17 Making Complex Decisions:
What to do today given decisions to be made tomorrow.
Sequential decision problems (as opposed to one-shot episodic, cf. above): 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 Learning From Examples [big]:
Improving behavior by observing the present (past?) and predicting the future.
Learning is improving performance (behavior) after making observations.[25]^14
If the agent is a computer: Machine learning: "a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems." (651)
Subsections:
* supervised learning;
* learning decision trees;
* model selection and optimization;
* theory of learning;
* linear regression (finding the best-fit line, i.e. predicting `future' [dependent] values based on plotting `past' [independent] values), classification;[26]^15
* nonparametric models (which retain all the examples, aka `instance-based' or `memory-based' learning, which is more true to large datasets [scalable?] than parametric, which summarize, and then discard, training data in fixed numbers of parameters),
* ensemble learning (using multiple hypotheses instead of one, and averaging or voting--`base' models are combined into an `ensemble' model);
* ML system development, the practice (software engineering and design patters in ML ops).
20 Learning Probabilistic Models:
View `learning' as "uncertain reasoning from observations" and model the world accordingly.
Agents can't use probability and decision theories until they learn them from experience: treat learning itself as an inference process in a probabilistic world. Use Bayesian networks. Key concepts: data and hypotheses. "Here, the data are evidence ...instantiations of some or all of the random variables describing the domain."[27]^16
21 Deep Learning:
represent hypotheses as "complex algebraic circuits with tunable connection strengths."
The circuits are orginzed into layers, a multi-step computation path. Ideal for recognizing, translating and generating images (including objects in images) and speech; `neural networks'.
From chpt. 10 on knowledge rep. languages, above notes: "deep neural networks: for reasoning about images, sounds, other data."
Think: gradient descent, back-propagation, convolutional neural networks.
22 Reinforcement Learning:
Learning from experiences of reward and punishment instead of correct examples from a supervisor
Passive and active RL., Q-learning, apprenticeships and inverse RL.
Cf. Reward is Enough, May 2021: [28]https://www.deepmind.com/publications/reward-is-enough
VI INTERACTING--"Communicating, perceiving, and acting"
23 Natural Language Processing:
Communicating with humans and learning from what they've written.
Language model: "a probability distribution describing the likelihood of any string." (824)
N-grams, grammar, syntax, semantics, parsing, vagueness, ambiguity, quantification.
24 Deep Learning for Natural Language Processing:
Using neural nets on natural language to effectively handle the complexity.
"[R]epresenting words as points in a high-dimensional space." RNNs for "long-distance context."
Cf. Attention Is All You Need, 2017: [29]https://arxiv.org/abs/1706.03762 and AIMA4e p. 868, transformer architecture, self-attention.
25 Computer Vision:
Connecting AI to cameras.
Photons provide a lot of valuable information to agents--too much information.
Surveillance cameras--good and bad; cars. Lots of machines do better if they can see.
From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."
26 Robotics:
Connecting AI to sensors, effectors and actuators
To enable movement in-and of--the physical world. Cars, spacecraft, surgeons, submarines, delivery bots.
From the Preface: Robotics and vision: "not ...independently defined problems"..."[things] in the service of achieving goals."
VII CONCLUSIONS--"Conclusions"
27 Philosophy, Ethics, and Safety of AI:
What is AI? What should we do with it? What might it do with us?
Trust--of systems, humans, ourselves, each other.
The human use of human beings. Usefulness of human beings at all?
Medicine. War.
28 The Future of AI:
Our tools will improve dramatically; our ends might remain the same.
Our preferences, our tools, our architectures. They're ours, for now.
Minimize the negative impacts, don't maximize the positive?
A: MATH--"Appendix A: Mathematical Background"
A.1 [SOLVING per §II]: Complexity Analysis and O() Notation:
problem and algorithm analysis (computer science math)
Asymptotic and worst-case analysis of algorithms:
Approximately predicting the performance (and efficiency) of algorithms based on their steps in worst-case (or best or average) and infinite-case (asymptotic) input scenarios, in order to avoid actually implementing them, and to enable comparison of algorithms.[30]^17
Abstract over the input, and then the implementation, to find the key factors (string length; lines of code) that make the space/time difference. Ignore constants, usually; focus on the key variables.
Complexity analysis of problems:
Polynomial time O(n^k) problems, class P.
Non-polynomial time problems.
Nondeterministic polynomial problems: class NP. A problem with some algorithm that can guess and check a solution in polynomial time.
A.2 [THINKING per §III]: Vectors, Matrices, and Linear Algebra:
line equation probing (`unknowns' math)
A vector is a pile of numbers (or unknowns or variables), a matrix is a pile of piles of numbers; some of the questions we can ask are `linear problems'[31]^18 (think prediction, interpolation, extrapolation), and algebra (finding unknowns by repairing [or completion] and balancing) on these things is `linear algebra'.
Vectors: Ordered sequences of values--represent something in the real world as just a set of values measuring specific aspects of that thing.[32]^19
Linear algebra: Doing algebra (finding unknowns by repairing [or completion] and balancing) on systems of equations of lines in planes instead of single equations and equations of points on lines (algebra). Think: finding line or plane intersections or bounded regions (based on inequalities instead of equations),[33]^20 and changing lines without affecting intersections[34]^21 --that sort of thing.
Thinking about higher dimensional objects: left-right x, up-down y, forward-backward z, wrist-watch value (time) t, color spectrum p, texture q, weight r, etc.
A.3 [UNCERTAINTY per §IV] Probability Distributions:
quantifying `probably' (uncertainty math)
Probability is a controversial concept.[35]^22
Experiments yield outcomes; a set of outcomes is an event; the set of all possible outcomes is the sample space.[36]^23
"A `probability' is a measure over a set of events...." A probability model: sample space plus the probability measure for each outcome.
Cf. `random variable' note above.
B: CODE--"Appendix B: Notes on Languages and Algorithms"
__
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