Boolean satisfiability is probably the most studied of the combinatorial optimization/search problems. Significant effort has been devoted to trying to provide practical solutions to this problem for problem instances encountered in a range of applications in electronic design automation (EDA), as well as in artificial intelligence (AI). This study has culminated in the development of several SAT packages, both proprietary and in the public domain (e.g. GRASP, SATO) which find significant use in both research and industry. Most existing complete solvers are variants of the Davis-Putnam (DP) search algorithm. In this paper we describe the development of a new complete solver, Chaff which achieves significant performance gains through careful engineering of all aspects of the search-especially a particularly efficient implementation of Boolean constraint propagation (BCP) and a novel low overhead decision strategy. Chaff has been able to obtain one to two orders of magnitude performance improvement on difficult SAT benchmarks in comparison with other solvers (DP or otherwise), including GRASP and SATO.

/pdf/chaff-engineering-an-efficient-sat-solver-2ima1ekzuu.pdf

Chaff: engineering an efficient SAT solver

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

/pdf/artificial-intelligence-a-new-synthesis-1mm7uhqtx9.pdf

Artificial Intelligence: A New Synthesis

1. What is Statistical Mechanics? 2. Random walks and emergent properties 3. Temperature and equilibrium 4. Phase-space dynamics and ergodicity 5. Entropy 6. Free Energies 7. Quantum statistical mechanics 8. Calculation and computation 9. Order parameters, broken symmetry, and topology 10. Correlations, response, and dissipation 11. Abrupt phase transitions 12. Continuous phase transitions APPENDIX: FOURIER METHODS

Statistical Mechanics: Entropy, Order Parameters and Complexity

We study the satisfiability of randomly generated formulas formed by $M$ clauses of exactly $K$ literals over $N$ Boolean variables. For a given value of $N$ the problem is known to be most difficult with $\alpha=M/N$ close to the experimental threshold $\alpha_c$ separating the region where almost all formulas are SAT from the region where all formulas are UNSAT. Recent results from a statistical physics analysis suggest that the difficulty is related to the existence of a clustering phenomenon of the solutions when $\alpha$ is close to (but smaller than) $\alpha_c$. We introduce a new type of message passing algorithm which allows to find efficiently a satisfiable assignment of the variables in the difficult region. This algorithm is iterative and composed of two main parts. The first is a message-passing procedure which generalizes the usual methods like Sum-Product or Belief Propagation: it passes messages that are surveys over clusters of the ordinary messages. The second part uses the detailed probabilistic information obtained from the surveys in order to fix variables and simplify the problem. Eventually, the simplified problem that remains is solved by a conventional heuristic.

pdf/survey-propagation-an-algorithm-for-satisfiability-2bajyapdf8.pdf

Survey propagation: an algorithm for satisfiability

We study the problem of satisfiability of randomly chosen clauses, each with K Boolean variables. Using the cavity method at zero temperature, we find the phase diagram for the K=3 case. We show the existence of an intermediate phase in the satisfiable region, where the proliferation of metastable states is at the origin of the slowdown of search algorithms. The fundamental order parameter introduced in the cavity method, which consists of surveys of local magnetic fields in the various possible states of the system, can be computed for one given sample. These surveys can be used to invent new types of algorithms for solving hard combinatorial optimizations problems. One such algorithm is shown here for the K=3 satisfiability problem, with very good performances.

Random K -satisfiability problem: From an analytic solution to an efficient algorithm

It has recently been shown that local search is surprisingly good at nding satisfying assignments for certain classes of CNF formulas 24]. In this paper we demonstrate that the power of local search for satissability testing can be further enhanced by employinga new strategy, called \mixed random walk", for escaping from local minima. We present experimental results showing how this strategy allows us to handle formulas that are substantially larger than those that can be solved with basic local search. We also present a detailed comparison of our random walk strategy with simulated annealing. Our results show that mixed random walk is the superior strategy on several classes of computationally diicult problem instances. Finally, we present results demonstrating the eeectiveness of local search with walk for solving circuit synthesis and diagnosis problems.

Local Search Strategies for Satisfiability Testing

Local search strategies for satisfiability testing

Noise Strategies for Local Search

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Local Search Strategies for Satisfiability Testing

Local search strategies for satisfiability testing

Noise Strategies for Local Search