Principles of Artificial Intelligence

By Nils J. Nilsson. 1980

Subfields: Knowledge Graphs & Representation

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Citations in the corpus

Neural Logic Machines (2019, methodology): “4 BLOCKS WORLD We also test NLM’s capability of decision making in the classic blocks world domain (Nilsson, 1982; Gupta & Nau, 1992) by slightly extending the model to fit the formulation of Markov Decision Process (MDP) in reinforcement learning.”
Deep learning in neural networks: An overview (2014, methodology): “Learning hierarchical representations through deep SL, UL, RL Many methods of Good Old-Fashioned Artificial Intelligence (GOFAI) (Nilsson, 1980) as well as more recent approaches to AI (Russell, Norvig, Canny, Malik, & Edwards, 1995) and Machine Learning (Mitchell, 1997) learn hierarchies of more an”
Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018, methodology): “We can generate these conditional explanations by either performing post-processing on conformant explanations or by performing an AND-OR graph search with AO∗(Nilsson 1980).”
Cooperative Pathfinding (2005, background): “A well-known property of A* with a consistent heuristic is that the optimal distance from the start to a node is known once that node is expanded (Nilsson 1980).”
Explanation-Based Learning: An Alternative View (1986, methodology): “The generalization process used in EBG is a specialization of goal-regression (Waldinger, 1977; Nilsson, 1980).”
Experimental photonic quantum memristor (2021, background): “[23] Nils J Nilsson, Principles of artificial intelligence (Morgan Kaufmann, 2014).”
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016, background): “- Probabilistic logics have been also studied from a knowledge representational perspective [Nilsson, 1986, Halpern, 1990, Poole, 1993b]. e aim of this research was initially more a probabilistic characterization of logic than suitable representations for learning.”
SPROUT: A program for structure generation (1993, methodology): “generation is well known as a combinatiorial problem [ 17 ] where attempts at finding solutions rapidly produce a large number of intermediate structures.”
From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014, unknown): “In the AI community, related works are the classical A* heuristic search [Nilsson, 1980] and the AO* variant from [Hansen and Zilberstein, 1999].”
Efficient Learning and Planning Within the Dyna Framework (1993, background): “…initialized to 0, upon discovery of the high-reward goal state G the algorithmperforms value estimate updates in the same order as an A search (Nilsson, 1980) wouldproceed from G backwards toward S, with the d function serving as the heuristic estimateof remaining path length (backward) to…”
Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications (2000, methodology): “Doering et al. (1997) have proposed using the A*-Algorithm (Nilsson, 1980) for the same task.”
Scheduling Aircraft Landings - The Static Case (2000, methodology): “These systems include, in part, modules to schedule landings and takeoffs: OLCKERS 1986, 1987, 1990; P LATZ and B ROKOF , 1994), which takes an initial heuristic schedule and applies an enumeration procedure designed to eliminate separation-time conflicts ● MAESTRO (G ARCIA , 1990), where the soluti”
Planning with Incomplete Information as Heuristic Search in Belief Space (2000, methodology): “This is a standard model in AI (Nilsson 1980) that for convenience we call the deterministic control problem model (Bertsekas 1995) and is characterized by S1.”
A comprehensive survey of recent trends in deep learning for digital images augmentation (2021, background): “Deep learning (DL) is a sub-category of machine learning (Baştanlar and Özuysal 2014) which consequently a subset of artificial intelligence (Nilsson 1981).”