Artificial Intelligence A Modern Approach 3rd Edition

2020

Citations in the corpus

• Relational inductive biases, deep learning, and graph networks (2018, neutral): “…approaches, including logic, grammars, classic planning, graphical models, causal reasoning, Bayesian nonparametrics, and probabilistic programming (Chomsky, 1957; Nilsson and Fikes, 1970; Pearl, 1986, 2009; Russell and Norvig, 2009; Hjort et al., 2010; Goodman et al., 2012; Ghahramani, 2015).”
• Deep Reinforcement Learning for Autonomous Driving: A Survey (2020, neutral): “and Norvig define an agent as “anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators” [19].”
• Simple And Efficient Architecture Search for Convolutional Neural Networks (2017, positive): “Our proposed algorithm is a simple hill climbing strategy (Russell & Norvig, 2009).”
• NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language (2019, neutral): “The worst case complexity vanilla logic programming is exponential in the depth of the proof (Russell and Norvig, 2010a).”
• Learning Plannable Representations with Causal InfoGAN (2018, neutral): “The one-hot and binary representations above can be directly plugged in to graph-planning algorithms such as Dijkstra’s shortest-path algorithm [34].”
• Artificial Intelligence and Management: The Automation–Augmentation Paradox (2020, unknown): “InthefieldofAI,utilityfunctions areusedtoconveyvariousoutcomes ’ relative value to machines, which in turn allows them to propose alternatives that optimize the utility function (Russell & Norvig, 2009).”
• Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations (2020, unknown): “A common manner with which to conceptualize machine learning is to consider the type of learning algorithm used to solve a problem: supervised learning, unsupervised learning, and reinforcement learning.(8) Each of these types of learning algorithms has a range of techniques that can be applied and ”
• Salesperson communication effectiveness in a digital sales interaction (2020, unknown): “Supervised learning is described as observing input-output pairs and then creating a function that maps input to output (Russell & Norvig, 2014).”
• Artificial intelligence and big data in entrepreneurship: a new era has begun (2019, unknown): “…AI under conditions of uncertainty is a vibrant research field (e.g., Ghahramani 2015; Tversky and Kahnemann 1974; for an overview see also see Russell and Norvig 2016) and we might see major developments in this particular field in the near future as well, including new implications for…”
• Causability and explainability of artificial intelligence in medicine (2019, unknown): “Therefore, it is often called machine intelligence (Poole, Mackworth, & Goebel, 1998) to contrast it to human intelligence (Russell & Norvig, 2010).”
• Quo vadis artificial intelligence? (2022, unknown): “As pointed out by Professor Stuart Russell, the field has operated for over 50 years on one simple assumption: the more intelligent, the better [142].”
• Transforming the communication between citizens and government through AI-guided chatbots (2019, unknown): “Chatbots are intelligent agents (defined as devices that perceive their environment and take actions that maximize their chance of success at some goal (Russell & Norvig, 2009), which have the ability to understand a spoken language and use speech communication as user interface.”
• Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review (2019, unknown): “Initially, we considered only the main learning AI technique or method [1 – 3] for this systematic review.”
• Excavating AI: the politics of images in machine learning training sets (2021, unknown): “For reasons that are rarely 4 Russell SJ (2010).”
• Aligning AI Optimization to Community Well-Being (2020, unknown): “There are also value functions, cost functions, fitness functions, energy functions and more, all of which operate on similar principles (Russell and Norvig 2010).”
• Artificial Intelligence and Journalism (2019, unknown): “The prevailing view adopted by the computer science field, which determined the goal of AI, is to create computer agents to act rationally (Russell & Norvig, 2010).”