توضیحات
27-4: What if AI does succeed? — A: Mathematical background — A-1: Complexity analysis and O() notation — A-2: Vectors, matrices, and linear algebra — A-3: Probability distributions — B: Notes on languages and algorithms — B-1: Defining languages with Backus-Naur form (BNF) — B-2: Describing algorithms with pseudocode — B-3: Online help — Bibliography — Index.;20-4: Summary, bibliographical and historical notes, exercises — 21: Reinforcement learning — 21-1: Introduction — 21-2: Passive reinforcement learning — 21-3: Active reinforcement learning — 21-4: Generalization in reinforcement learning — 21-5: Policy search — 21-6: Applications of reinforcement learning — 21-7: Summary, bibliographical and historical notes, exercises — 6: Communicating, Perceiving, And Acting — 22: Natural language processing — 22-1: Language models — 22-2: Text classification — 22-3: Information retrieval — 22-4: Information extraction — 22-5: Summary, bibliographical and historical notes, exercises — 23: Natural language for communication — 23-1: Phrase structure grammars — 23-2: Syntactic analysis (parsing) — 23-3: Augmented grammars and semantic interpretation — 23-4: Machine translation — 23-5: Speech recognition — 23-6: Summary, bibliographical and historical notes, exercises — 24: Perception — 24-1: Image formation.;11: Planning and acting in the real world — 11-1: Time, schedules, and resources — 11-2: Hierarchical planning — 11-3: Planning and acting in nondeterministic domains — 11-4: Multiagent planning — 11-5: Summary, bibliographical and historical notes, exercises — 12: Knowledge representation — 12-1: Ontological engineering — 12-2: Categories and objects — 12-3: Events — 12-4: Mental events and mental objects — 12-5: Reasoning systems for categories — 12-6: Reasoning with default information — 12-7: Internet shopping world — 12-8: Summary, bibliographical and historical notes, exercises — 4: Uncertain Knowledge And Reasoning — 13: Quantifying uncertainty — 13-1: Acting under uncertainty — 13-2: Basic probability notation — 13-3: Inference using full joint distributions — 13-4: Independence — 13-5: Bayes’ rule and its use — 13-6: Wumpus world revisited — 13-7: Summary, bibliographical and historical notes, exercises — 14: Probabilistic reasoning.;7-5: Propositional theorem proving — 7-6: Effective propositional model checking — 7-7: Agents based on propositional logic — 7-8: Summary, bibliographical and historical notes, exercises — 8: First-order logic — 8-1: Representation revisited — 8-2: Syntax and semantics of first-order logic — 8-3: Using first-order logic — 8-4: Knowledge engineering in first-order logic — 8-5: Summary, bibliographical and historical notes, exercises — 9: Inference in first-order logic — 9-1: Propositional vs first-order inference — 9-2: Unification and lifting — 9-3: Forward chaining — 9-4: Backward chaining — 9-5: Resolution — 9-6: Summary, bibliographical and historical notes, exercises — 10: Classical planning — 10-1: Definition of classical planning — 10-2: Algorithms for planning as state-space search — 10-3: Planning graphs — 10-4: Other classical planning approaches — 10-5: Analysis of planning approaches — 10-6: Summary, bibliographical and historical notes, exercises.;16-7: Decision-theoretic expert systems — 16-8: Summary, bibliographical and historical notes, exercises — 17: Making complex decisions — 17-1: Sequential decision problems — 17-2: Value iteration — 17-3: Policy iteration — 17-4: Partially observable MDPs — 17-5: Decisions with multiple agents: game theory — 17-6: Mechanism design — 17-7: Summary, bibliographical and historical notes, exercises.;4-4: Searching with partial observations — 4-5: Online search agents and unknown environments — 4-6: Summary, bibliographical and historical notes, exercises — 5: Adversarial search — 5-1: Games — 5-2: Optimal decisions in games — 5-3: Alpha-beta pruning — 5-4: Imperfect real-time decisions — 5-5: Stochastic games — 5-6: Partially observable games — 5-7: State-of-the-art game programs — 5-8: Alternative approaches — 5-9: Summary, bibliographical and historical notes, exercises — 6: Constraint satisfaction problems — 6-1: Defining constraint satisfaction problems — 6-2: Constraint propagation: inference in CSPs — 6-3: Backtracking search for CSPs — 6-4: Local search for CSPs — 6-5: Structure of problems — 6-6: Summary, bibliographical and historical notes, exercises — 3: Knowledge. Reasoning And Planning — 7: Logical agents — 7-1: Knowledge-based agents — 7-2: Wumpus world — 7-3: Logic — 7-4: Propositional logic: a very simple logic.;24-2: Early image-processing operations — 24-3: Object recognition by appearance — 24-4: Reconstructing the 3D world — 24-5: Object recognition form structural information — 24-6: Using vision — 24-7: Summary, bibliographical and historical notes, exercises — 25: Robotics — 25-1: Introduction — 25-2: Robot hardware — 25-3: Robotic perception — 25-4: Planning to move — 25-5: Planning uncertain movements — 25-6: Moving — 25-7: Robotic software architectures — 25-8: Application domains — 25-9: Summary, bibliographical and historical notes, exercises — 7: Conclusions — 26: Philosophical foundations — 26-1: Weak AI: can machines act intelligently? — 26-2: Strong AI: can machines really think? — 26-3: Ethics and risks of developing artificial intelligence — 26-4: Summary, bibliographical and historical notes, exercises — 27: AI: Present and future — 27-1: Agent components — 27-2: Agent architectures — 27-3: Are we going in the right direction?;1: Artificial Intelligence — 1: Introduction — 1-1: What is AI? — 1-2: Foundations of artificial intelligence — 1-3: History of artificial intelligence — 1-4: State of the art — 1-5: Summary, bibliographical and historical notes, exercises — 2: Intelligent agents — 2-1: Agents and environments — 2-2: Good behavior: the concepts of rationality — 2-3: Nature of environments — 2-4: Structure of agents — 2-5: Summary, bibliographical and historical notes, exercises — 2: Problem-Solving — 3: Solving problems by searching — 3-1: Problem-solving agents — 3-2: Example problems — 3-3: Searching for solutions — 3-4: Uninformed search strategies — 3-5: Informed (heuristic) search strategies — 3-6: Heuristic functions — 3-7: Summary, bibliographical and historical notes, exercises — 4: Beyond classical search — 4-1: Local search algorithms and optimization problems — 4-2: Local search in continuous spaces — 4-3: Searching with nondeterministic actions.;14-1: Representing knowledge in an uncertain domain — 14-2: Semantics of Bayesian networks — 14-3: Efficient representation of conditional distributions — 14-4: Exact inference in Bayesian networks — 14-5: Approximate inference in Bayesian networks — 14-6: Relational and first-order probability models — 14-7: Other approaches to uncertain reasoning — 14-8: Summary, bibliographical and historical notes, exercises — 15: Probabilistic reasoning over time — 15-1: Time and uncertainty — 15-2: Inference in temporal models — 15-3: Hidden Markov models — 15-4: Kalman filters — 15-5: Dynamic Bayesian Networks — 15-6: Keeping track of many objects — 15-7: Summary, bibliographical and historical notes, exercises — 16: Making simple decisions — 16-1: Combining beliefs and desires under uncertainty — 16-2: Basis of utility theory — 16-3: Utility functions — 16-4: Multiattribute utility functions — 16-5: Decision networks — 16-6: Value of information.;Learning — 18: Learning from examples — 18-1: Forms of learning — 18-2: Supervised learning — 18-3: Learning decision trees — 18-4: Evaluating and choosing the best hypothesis — 18-5: Theory of learning — 18-6: Regression and classification with linear models — 18-7: Artificial neural networks — 18-8: Nonparametric models — 18-9: Support vector machines — 18-10: Ensemble learning — 18-11: Practical machine learning — 18-12: Summary, bibliographical and historical notes, exercises — 19: Knowledge in learning — 19-1: Logical formulation of learning — 19-2: Knowledge in learning — 19-3: Explanation-based learning — 19-4: Learning using relevance information — 19-5: Inductive logic programming — 19-6: Summary, bibliographical and historical notes, exercises — 20: Learning probabilistic models — 20-1: Statistical learning — 20-2: Learning with complete data — 20-3: Learning with hidden variables: the EM algorithm.
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ترجمه ماشینی :
27-4: اگر هوش مصنوعی موفق شود چه؟ — الف: پیشینه ریاضی — الف-1: تحلیل پیچیدگی و نماد O() — الف-2: بردارها، ماتریس ها و جبر خطی — الف-3: توزیع احتمال — ب: نکاتی در مورد زبان ها و الگوریتم ها – – B-1: تعریف زبان با فرم Backus-Naur (BNF) — B-2: توصیف الگوریتم ها با شبه کد — B-3: راهنمای آنلاین — کتابشناسی — فهرست؛ 20-4: خلاصه، کتابشناختی و تاریخی یادداشت ها، تمرین ها — 21: یادگیری تقویتی — 21-1: مقدمه — 21-2: یادگیری تقویتی غیرفعال — 21-3: یادگیری تقویتی فعال — 21-4: تعمیم در یادگیری تقویتی — 21-5: جستجوی خط مشی — 21-6: کاربردهای یادگیری تقویتی — 21-7: خلاصه، یادداشت های کتابشناختی و تاریخی، تمرین ها — 6: برقراری ارتباط، درک و عمل — 22: پردازش زبان طبیعی — 22-1: زبان مدل ها — 22-2: طبقه بندی متن — 22-3: بازیابی اطلاعات — 22-4: استخراج اطلاعات — 22-5: خلاصه، یادداشت های کتابشناختی و تاریخی، تمرین ها — 23: زبان طبیعی برای ارتباط — 23 -1: دستور زبان ساختار عبارت — 23-2: تجزیه و تحلیل نحوی (تجزیه) — 23-3: گرامرهای تقویت شده و تفسیر معنایی — 23-4: ترجمه ماشینی — 23-5: تشخیص گفتار — 23-6: خلاصه، یادداشت های کتابشناختی و تاریخی، تمرین ها — 24: ادراک — 24-1: شکل گیری تصویر. 11: برنامه ریزی و عمل در دنیای واقعی — 11-1: زمان، برنامه ها و منابع — 11-2: برنامه ریزی سلسله مراتبی — 11-3: برنامه ریزی و اقدام در حوزه های غیر قطعی — 11-4: برنامه ریزی چند عاملی — 11-5: خلاصه، یادداشت های کتابشناختی و تاریخی، تمرین ها — 12: بازنمایی دانش — 12-1: مهندسی هستی شناسی — 12-2: دسته ها و اشیاء — 12-3: رویدادها — 12-4: رویدادهای ذهنی و اشیاء ذهنی — 12-5: سیستم های استدلال برای دسته ها — 12-6: استدلال با اطلاعات پیش فرض — 12 -7: دنیای خرید اینترنتی — 12-8: خلاصه، یادداشت های کتابشناختی و تاریخی، تمرین ها — 4: دانش و استدلال نامطمئن — 13: کمی سازی عدم قطعیت — 13-1: عمل در شرایط عدم قطعیت — 13-2: پایه نماد احتمال — 13-3: استنتاج با استفاده از توزیع های مشترک کامل — 13-4: استقلال — 13-5: قاعده بیز و استفاده از آن — 13-6: جهان Wumpus بازبینی شده — 13-7: خلاصه، کتابشناختی و یادداشت های تاریخی، تمرین ها — 14: استدلال احتمالی.؛ 7-5: اثبات قضیه گزاره — 7-6: بررسی مدل گزاره ای مؤثر — 7-7: عوامل مبتنی بر منطق گزاره ای — 7-8: خلاصه، کتابشناختی و یادداشت های تاریخی، تمرین ها — 8: منطق مرتبه اول — 8-1: بازبینی مجدد — 8-2: نحو و معناشناسی منطق مرتبه اول — 8-3: ا
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