Artificial Intelligence¶
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
Cited by¶
6 citations across 6 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
- 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¶
- 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
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