This academic text focuses on adversarial search and game theory within artificial intelligence, exploring how AI agents navigate environments where others actively work against them. It primarily discusses game-playing algorithms like minimax and alpha-beta pruning for deterministic, perfect-information games, detailing their mechanics and limitations. The document also addresses more complex scenarios, including stochastic games (involving chance elements like dice) and partially observable games (where information is hidden), introducing expectiminimax and Monte Carlo Tree Search (MCTS) as alternative strategies. Finally, it touches upon the integration of machine learning to enhance game AI, citing examples of AI surpassing human performance in various games like chess and Go.

The provided text explores Constraint Satisfaction Problems (CSPs), a framework for solving problems by representing them as variables that need values while adhering to specified constraints. It details various inference techniques like node, arc, and path consistency, which prune the search space by eliminating inconsistent values. The document also describes backtracking search algorithms, including intelligent methods like conflict-directed backjumping and constraint learning, and introduces local search algorithms such as min-conflicts for finding solutions. Finally, the text examines how the structure of a CSP's graph, particularly its tree width and cycle cutsets, impacts the efficiency of solution methods, alongside the concept of value symmetry.

This chapter expands upon search algorithms by addressing more complex, real-world environments that relax simplifying assumptions. It introduces local search and optimization problems, where the focus is on finding a good final state rather than the path, and discusses techniques like hill climbing and simulated annealing. The text then progresses to search with nondeterministic actions, where agents need to formulate conditional plans due to unpredictable outcomes, utilizing AND-OR search trees. Finally, the chapter explores search in partially observable and unknown environments, introducing the concept of belief states and the challenges of online search agents that learn about the environment as they interact with it, including methods like LRTA* for efficient exploration and adaptation.

The provided texts offer a comprehensive overview of enteral nutrition (EN) in hospitalized adults, synthesizing 15 years of research and clinical practice leading up to April 2025. They define EN as a method to prevent or treat disease-related malnutrition (DRM) in patients unable to eat orally, emphasizing its low utilization despite high malnutrition prevalence. The sources discuss evolving guidelines, the pathophysiology of malnutrition and inflammation, and practical considerations for EN administration, including access methods and safety advancements. Finally, a forthcoming review article by Leah Gramlich and Peggi Guenter, both prominent figures in the field, is highlighted as a summary of these advancements and their implications for future practice.

The provided texts detail the American Statistical Association's (ASA) landmark 2016 statement on p-values and statistical significance, driven by widespread misuse and misinterpretation within the scientific community. They outline a historical timeline of concerns regarding p-values, highlighting a "reproducibility crisis" and "circular logic" in their application. The sources explain the six key principles of the ASA statement, clarifying what p-values do and do not measure, and advocating for full transparency and contextual interpretation rather than rigid thresholds. Finally, they introduce alternative statistical approaches while emphasizing that no single index should replace scientific reasoning.

The provided texts comprehensively outline problem-solving agents and search algorithms in Artificial Intelligence. They explain how these agents formulate problems by defining states, initial states, goal states, actions, and action costs to create an abstract model of the environment. The sources detail various uninformed search strategies like Breadth-First Search, Uniform-Cost Search, Depth-First Search, Iterative Deepening Search, and Bidirectional Search, evaluating them based on completeness, optimal cost, time complexity, and space complexity. Furthermore, the texts explore informed (heuristic) search strategies such as Greedy Best-First Search and A* Search, emphasizing the critical role of heuristic functions derived through methods like problem relaxation, pattern databases, and landmark points, or even learned using machine learning.

The provided texts comprehensively introduce the concept of intelligent agents in artificial intelligence, defining them as entities that perceive environments through sensors and act via actuators. They explain how an agent function abstractly maps percept sequences to actions, while an agent program concretely implements this, contrasting it with the impractical table-driven approach. A central theme is rationality, which dictates agents should choose actions to maximize a performance measure, emphasizing the critical importance of its correct formulation. The sources categorize task environments using the PEAS framework (Performance, Environment, Actuators, Sensors) and classify them by properties like observability, determinism, and episodic nature. Finally, they detail different agent architectures—simple reflex, model-based reflex, goal-based, and utility-based agents—progressing in complexity, and highlight the crucial role of learning agents with their performance element, learning element, critic, and problem generator in achieving autonomy and adapting to unknown environments.

The provided sources offer a comprehensive overview of Artificial Intelligence (AI), detailing its core concepts, historical evolution, and multidisciplinary foundations. They explain how AI aims to build machines that act effectively and safely, exploring different definitions of intelligence such as acting humanly (Turing test) and acting rationally (rational agents), with the latter becoming the predominant approach. The texts trace AI's intellectual roots through contributions from philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory, and linguistics, highlighting key figures and breakthroughs from ancient times to the present. Finally, they discuss the benefits and risks of AI, emphasizing the critical need for systems to pursue human objectives and the challenges of ensuring beneficial AI to avoid unintended negative consequences like the "King Midas problem."