Artificial Intelligence¶
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
Cited by¶
8 citations across 8 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Belief Formation
- AI and machine learning: Bayesian inference engines, knowledge-base updating, multi-agent belief sharing, formal epistemic logic, probabilistic graphical models, and the construction of belief states as posterior distributions in reinforcement-learning and partially observable Markov-decision-process agents, a framework Russell and Norvig (2020) treat as the unifying spine of probabilistic AI.
This sourceTreats probabilistic inference and belief-state representation (POMDPs, Bayesian networks) as the unifying spine of probabilistic AI; (Annotation corrected: the prior def text described 'well-defined problem / search tractability' content from another prime.)
- AI and machine learning: Bayesian inference engines, knowledge-base updating, multi-agent belief sharing, formal epistemic logic, probabilistic graphical models, and the construction of belief states as posterior distributions in reinforcement-learning and partially observable Markov-decision-process agents, a framework Russell and Norvig (2020) treat as the unifying spine of probabilistic AI.
- Incomplete Contract
- In AI alignment it is reward functions and prompts as incomplete specifications whose gap-handlers are learned priors and fallback rules — the specification problem being fundamentally contract incompleteness.
This sourceTreats reward-function and goal specification as inherently incomplete (the value-alignment problem), with learned priors and fallback behavior as gap-handlers.
- In AI alignment it is reward functions and prompts as incomplete specifications whose gap-handlers are learned priors and fallback rules — the specification problem being fundamentally contract incompleteness.
- Path
- In search and reasoning solution-finding in a state space — game tree, proof search, planner — is the construction of a path from initial state to goal, and the vocabulary of frontier, expansion, and backtracking is path vocabulary.
This sourceStandard treatment of state-space search as constructing a path from initial state to goal, with frontier, expansion, and backtracking.
- In search and reasoning solution-finding in a state space — game tree, proof search, planner — is the construction of a path from initial state to goal, and the vocabulary of frontier, expansion, and backtracking is path vocabulary.
- Problem Space
- Precision requires these contrasts, which Russell and Norvig (2020) formalize in their treatment of problem-solving agents and well-defined problem formulations.
This sourceStandard AI reference: develops the formal definition of a well-defined problem (initial state, actions, transition model, goal test, path cost), distinguishes problem from problem-solving agent, and treats abstraction-level choice as a determinant of search tractability.
- Precision requires these contrasts, which Russell and Norvig (2020) formalize in their treatment of problem-solving agents and well-defined problem formulations.
- Satisficing
- - Operations research: Satisficing appears formally in constraint-satisfaction algorithms, integer programming solvers, and combinatorial optimization tools where the goal is any feasible solution (not the best) within a finite time budget
This sourceChapter on anytime algorithms and satisficing search. AI satisficing instantiation; anytime planners that can terminate with any available solution and improve over time as computation permits. Shows satisficing in modern AI systems.*
- - Operations research: Satisficing appears formally in constraint-satisfaction algorithms, integer programming solvers, and combinatorial optimization tools where the goal is any feasible solution (not the best) within a finite time budget
Mechanisms¶
- Decision-Tree Search Diagram
- A diagram maintained by hand silently diverges from the actual search state until it depicts a fiction; and a genuinely large search has a tree too enormous to draw, so a faithful diagram of a million nodes shows nothing at all — the combinatorial explosion that game-tree analysis has always fought.
This sourceRussell and Norvig show how game-tree branching produces enormous search spaces, motivating cutoff and pruning rather than exhaustive expansion.
- A diagram maintained by hand silently diverges from the actual search state until it depicts a fiction; and a genuinely large search has a tree too enormous to draw, so a faithful diagram of a million nodes shows nothing at all — the combinatorial explosion that game-tree analysis has always fought.
- Finite Horizon Assumption
- Its failure mode is the horizon effect
This sourceDefines the horizon effect as a fixed-depth search failure that pushes unavoidable damage beyond the search horizon.
- Its failure mode is the horizon effect
- Search Tree Pruning with Refinement
- Its signature failure is the horizon effect: a shallow evaluation of a branch misses a decisive event lying just beyond the search depth, so a branch is pruned (or a survivor trusted) on a value that a slightly deeper look would overturn.
This sourceDocuments a shallow evaluation of a branch misses a decisive event lying just beyond the search depth; a branch is pruned (or a survivor trusted) on a value that a slightly deeper look would overturn.
- Its signature failure is the horizon effect: a shallow evaluation of a branch misses a decisive event lying just beyond the search depth, so a branch is pruned (or a survivor trusted) on a value that a slightly deeper look would overturn.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. Neither this nor any other of the 8 citations of this work carries a recorded support check.
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Links previously used in the corpus¶
Before the registry existed this work was also linked 2 other ways.
- https://aima.cs.berkeley.edu/ ×3
- https://www.pearson.com/en-us/subject-catalog/p/artificial-intelligence-a-modern-approach/P200000003500/9780134610993 ×1
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