AIMA -- Nueva Edición (2002) de Russell y Norvig
Somewhat corrected and detailed by CvdB
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00 Front Matter
Part I Artificial IntelligenceIn 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
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.
Reasoning 15 Probabilistic Reasoning
Over Time 16 Making Simple
Decisions 17 Making Complex
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
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
VIII Conclusions In Part VIII, we introduce a section on the ethical implications of AI.
Foundations 27 AI: Present and
Appendix A: Mathematical
Background Appendix B: Notes on
Languages and Algorithms Bibliography