AIMA -- Nueva Edición (2002) de Russell y Norvig

Somewhat corrected and detailed by CvdB

Table of Contents

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00 Front Matter

Part I Artificial Intelligence
  • In Part I, we acknowledge the historical contributions of control theory, game theory, economics, and neuroscience. This helps set the tone for a more integrated coverage of these ideas in subsequent chapters.
    01 Introduction
    02 Intelligent Agents

    Part II Problem solving--
  • In Part II, online search algorithms are covered and a new chapter on constraint satisfaction has been added. The latter provides a natural connection to the material on logic.
    03 Solving Problems by Searching
    04 Informed Search and Exploration
    05 Constraint Satisfaction Problems
    06 Adversarial Search

    Part III Knowledge and reasoning
  • In Part III, propositional logic, which was presented as a stepping-stone to first-order logic in the first edition, is now presented as a useful representation language in its own right, with <>fast inference algorithms and circuit-based agent designs. The chapters on first-order logic have been reorganized to present the material more clearly and we have added the Internet shopping domain as an example.
    07 Logical Agents
    08 First-Order Logic
    09 Inference in First-Order Logic
    10 Knowledge Representation

    Part IV Planning
  • In Part IV, we include newer planning methods such as Graphplan and satisfiability-based planning, and we increase coverage of scheduling, conditional planning, hierarchical planning, and multiagent planning.     
    11 Planning Algorithms
    12 Planning and Acting

    Part V Uncertain knowledge and reasoning
  • In Part V, we have augmented the material on Bayesian networks with new algorithms, such as variable elimination and Markov Chain Monte Carlo, and we have created a new chapter on uncertain temporal reasoning, covering hidden Markov models, Kalman filters, and dynamic Bayesian networks. The coverage of Markov decision processes is deepened, and we add sections on game theory and mechanism design.
    13 Uncertainty
    14 Probabilistic Reasoning
    15 Probabilistic Reasoning Over Time
    16 Making Simple Decisions
    17 Making Complex Decisions

    Part VI Learning
  • In Part VI, we tie together work in statistical, symbolic, and neural learning and add sections on boosting algorithms, the EM algorithm, instance-based learning, and kernel methods (support vector machines).
    18 Learning from Observations
    19 Knowledge in Learning -- Learning Logical Representations
    20 Statistical Learning
    21 Reinforcement Learning

    Part VII Communicating, perceiving, and acting
  • In Part VII, coverage of language processing adds sections on discourse processing and grammar induction, as well as a chapter on probabilistic language models, with applications to information retrieval and machine translation. The coverage of robotics stresses the integration of uncertain sensor data, and the chapter on vision has updated material on object recognition.
    22 Communicatios -- Agents That Communicate
    23 Probabilistic Language Processing
    24 Perception
    25 Robotics

    Part VIII Conclusions
  • In Part VIII, we introduce a section on the ethical implications of AI.
    26 Philosophical Foundations
    27 AI: Present and Future


        Appendix A: Mathematical Background Appendix B: Notes on Languages and Algorithms Bibliography

  • AI: A Modern Approach by Stuart Russell and Peter Norvig Modified: Nov 12, 2002


    Curso de Introducción a la Inteligencia Artificial en ocho partes