Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI.

* An evolutionary approach provides a unifying theme 
* Thorough coverage of important AI ideas, old and new
* Frequent use of examples and illustrative diagrams
* Extensive coverage of machine learning methods throughout the text
* Citations to over 500 references
* Comprehensive index

Table of Contents

1 Introduction
2 Stimulus-Response Agents
3 Neural Networks 
4 Machine Evolution 
5 State Machines 
6 Robot Vision 
7 Agents that Plan 
8 Uninformed Search 
9 Heuristic Search 
10 Planning, Acting, and Learning 
11 Alternative Search Formulations and Applications 
12 Adversarial Search 
13 The Propositional Calculus 
14 Resolution in The Propositional Calculus 
15 The Predicate Calculus 
16 Resolution in the Predicate Calculus 
17 Knowledge-Based Systems 
18 Representing Commonsense Knowledge 
19 Reasoning with Uncertain Information 
20 Learning and Acting with Bayes Nets 
21 The Situation Calculus 
22 Planning 
23 Multiple Agents 
24 Communication Among Agents 
25 Agent Architectures

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Artificial Intelligence: A New Synthesis

In this paper we consider the problem of the logical characterization of the notion of consistent answer in a relational database that may violate given integrity constraints. This notion is captured in terms of the possible repaired versions of the database. A method for computing consistent answers is given and its soundness and completeness (for some classes of constraints and queries) proved. The method is based on an iterative procedure whose termination for several classes of constraints is proved as well.

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Consistent query answers in inconsistent databases

The purpose of this paper is to show that logic provides a convenient formalism for studying classical database problems. There are two main parts to the paper, devoted respectively to conventional databases and deductive databases. In the first part, we focus on query languages, integrity modeling and maintenance, query optimization, and data dependencies. The second part deals mainly with the representation and manipulation of deduced facts and incomplete information. Categories and Subject Descriptors: H.2.1 [Database Management]: Logical Design— data models; H.2.3 [Database Management]: Languages— query languages; H.2.4 [Database Management]: Systems— query processing General Terms: Deductive Databases, Indefinite Data, Logic and Databases, Null Values, Relational Databases

/pdf/logic-and-databases-a-deductive-approach-56l5k9wqo0.pdf

Logic and Databases: A Deductive Approach

Dependent type theories have a long history of being used for theorem proving One aspect of type theory which makes it very powerful as a proof language is that it mixes deduction with computation This also makes type theory a good candidate for programming---the strength of the type system allows properties of programs to be stated and established, and the computational properties provide semantics for the programs

This thesis is concerned with bridging the gap between the theoretical presentations of type theory and the requirements on a practical programming language Although there are many challenging research problems left to solve before we have an industrial scale programming language based on type theory, this thesis takes us a good step along the way

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Towards a practical programming language based on dependent type theory

OTTER (Organized Techniques for Theorem-proving and Effective Research) is a resolution-style theorem-proving program for first-order logic with equality. OTTER includes the inference rules binary resolution, hyperresolution, UR-resolution, and binary paramodulation. Some of its other abilities and features are conversion from first-order formulas to clauses, forward and back subsumption, factoring, weighting, answer literals, term ordering, forward and back demodulation, evaluable functions and predicates, and Knuth-Bendix completion. OTTER is coded in C, is free, and is portable to many different kinds of computer.

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OTTER 3.0 Reference Manual and Guide

In this article we show that the three equations known as commutativity, associativity, and the Robbins equation are a basis for the variety of Boolean algebras. The problem was posed by Herbert Robbins in the 1930s. The proof was found automatically by EQP, a theorem-proving program for equational logic. We present the proof and the search strategies that enabled the program to find the proof.

Solution of the Robbins Problem

Otter is Other Techniques for Theorem-proving and Effective Research), a revolution-style theorem-proving program for first-order logic with equality.

/pdf/otter-1-0-users-guide-3ibvzb45a5.pdf

OTTER 1.0 Users' Guide

OTTER is a resolution-style theorem-proving program for first-order logic with equality. OTTER includes the inference rules binary resolution, hyperresolution, UR-resolution, and binary paramodulation. Some of its other abilities and features are conversion from first-order formulas to clauses, forward and back subsumption, factoring, weighting, answer literals, term ordering, forward and back demodulation, evaluable functions and predicates, Knuth-Bendix completion, and the hints strategy. OTTER is coded in ANSI C, is free, and is portable to many different kinds of computer.

OTTER 3.3 Reference Manual

This article addresses the problem of indexing and retrieving first-order predicate calculus terms in the context of automated deduction programs. The four retrieval operations of concern are to find variants, generalizations, instances, and terms that unify with a given term. Discrimination-tree indexing is reviewed, and several variations are presented. The path-indexing method is also reviewed. Experiments were conducted on large sets of terms to determine how the properties of the terms affect the performance of the two indexing methods. Results of the experiments are presented.

William McCune

Papers

OTTER 3.0 Reference Manual and Guide

Solution of the Robbins Problem

OTTER 1.0 Users' Guide

OTTER 3.3 Reference Manual

Experiments with discrimination-tree indexing and path indexing for term retrieval