Artificial intelligence - a modern approach, 2nd Edition

By Stuart Russell, Peter Norvig. Prentice Hall series in artificial intelligence, 2003

1,669 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/23729ae655799ed2baa40ad77a2d4344453754f6

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Citations in the corpus

Temporal-Logic-Based Reactive Mission and Motion Planning (2009, background): “On the other hand, top-down task-planning approaches are usually focused on finding coarse, which are typically discrete, robot actions in order to achieve more complex tasks [2], [3].”
A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction (2016, methodology): “However, if the dimensionality of the feature space increases, the classification problem becomes significantly more difficult [19] (curse of dimensionality).”
Adaptive routing for intermittently connected mobile ad hoc networks (2005, background): “The simplest is to allow application developers to define a static hierarchy among the predicted context attributes [8].”
Automating string processing in spreadsheets using input-output examples (2011, unknown): “There is a tradeoff between the expressiveness of a search space, and the complexity of finding simple consistent hypotheses within that space [6, 18].”
Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “Two key requirements for ML to function are: 1) data that are relevant and detailed enough to answer the question being asked; and 2) a computational ML technique appropriate for the type, amount, and complexity of the data available (1).”
Issues in data fusion for healthcare monitoring (2008, methodology): “types of decision-making techniques [36] are used in healthcare systems.”
Software model checking (2009, unknown): “One direction of research applies guided or directed search heuristics inspired by search heuristics from the artificial intelligence literature, such as iterative deepening [Korf 1985], best-first search [Russell and Norvig 2003], or A⇤ search [Hart et al. 1968; Russell and Norvig 2003].”
Optimal and Approximate Q-value Functions for Decentralized POMDPs (2008, methodology): “It performs a guided A*-like search over partially specified joint policies, pruning joint policies that are guaranteed to be worse than the best (fully specified) joint policy found so far by an admissible heuristic.”
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback (2018, background): “An action value function Qπ : S × A → R is a function that returns the expected total reward in a given state and for a given action when following the policy π [25].”
Imitation Learning (2017, background): “An agent is defined as an entity that autonomously interacts within an environment toward achieving or optimizing a goal [Russell and Norvig 2003].”
A Rational Analysis of Rule-Based Concept Learning (2008, background): “reached its apogee in the work of Shepard, Hovland, and Jenkins (1961) and Feldman (2000), who organized possible Boolean concepts (those that discriminate among objects representable by binary features) into syntactically equivalent families and studied how the syntax was reflected in learnability.”