Artificial Intelligence: A Modern Approach

@inproceedings{Russell1995ArtificialIA,
  title={Artificial Intelligence: A Modern Approach},
  author={Stuart J. Russell and Peter Norvig},
  year={1995}
}
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning… 
Ten Project Proposals in Artificial Intelligence
TLDR
Ten proposals for projects in the latter branch of artificial intelligence, which includes game playing, expert systems, natural language, and robotics, are presented.
Artificial intelligence
TLDR
A general overview of this broad interdisciplinary field is presented, organized around the main modules of the notional architecture of an intelligent agent (knowledge representation; problem solving and planning; knowledge acquisition and learning; natural language, speech, and vision; action processing and robotics) which highlights both the main areas of artificial intelligence research, development and application.
Structured Approach to the Intelligent System Design
TLDR
The goal of this paper is to find active, productive may be not the best way to determine the starting position and some directions of intelligent system design.
The state of artificial intelligence
Epistemic Logic and Planning
TLDR
This project combines the inferencing power of epistemic logic in the adaptation phase of CBR with the performance of case-based planning, which is proved to be more efficient then using planning algorithms alone.
The logic of adaptive behavior : knowledge representation and algorithms for the Markov decision process framework in first-order domains
TLDR
This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting, and a methodological translation is constructed from the propositional to the relational setting.
Epistemic Logic Planning
TLDR
This project combines the inferencing power of epistemic logic in the adaptation of CBR with the performance of case-based planning, which is proved to be more efficient then using planning algorithms alone.
Selecting Actions and Making Decisions : Lessons from AI Planning
TLDR
This paper presents the ideas underlying the development of well-founded and empirically tested techniques for recognizing and exploiting structure, and argues for their relevance to models of natural intelligent behavior as well.
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