From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers.



Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others.



The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference.



The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures.


Table of Contents

1 Introduction 
2 Modelling I; Synchronous Network Model 
3 Leader Election in a Synchronous Ring 
4 Algorithms in General Synchronous Networks 
5 Distributed Consensus with Link Failures 
6 Distributed Consensus with Process Failures 
7 More Consensus Problems 
8 Modelling II: Asynchronous System Model 
9 Modelling III: Asynchronous Shared Memory Model 
10 Mutual Exclusion 
11 Resource Allocation 
12 Consensus 
13 Atomic Objects 
14 Modelling IV: Asynchronous Network Model 
15 Basic Asynchronous Network Algorithms 
16 Synchronizers 
17 Shared Memory versus Networks 
18 Logical Time 
19 Global Snapshots and Stable Properties 
20 Network Resource Allocation 
21 Asynchronous Networks with Process Failures 
22 Data Link Protocols 
23 Partially Synchronous System Models 
24 Mutual Exclusion with Partial Synchrony 
25 Consensus with Partial Synchrony

Distributed algorithms

The Temporal Logic of Reactive and Concurrent Systems: Specification

This paper presents an algorithm by which a process in a distributed system determines a global state of the system during a computation. Many problems in distributed systems can be cast in terms of the problem of detecting global states. For instance, the global state detection algorithm helps to solve an important class of problems: stable property detection. A stable property is one that persists: once a stable property becomes true it remains true thereafter. Examples of stable properties are “computation has terminated,” “ the system is deadlocked” and “all tokens in a token ring have disappeared.” The stable property detection problem is that of devising algorithms to detect a given stable property. Global state detection can also be used for checkpointing.

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Distributed snapshots: determining global states of distributed systems

Virtual time is a new paradigm for organizing and synchronizing distributed systems which can be applied to such problems as distributed discrete event simulation and distributed database concurrency control. Virtual time provides a flexible abstraction of real time in much the same way that virtual memory provides an abstraction of real memory. It is implemented using the Time Warp mechanism, a synchronization protocol distinguished by its reliance on lookahead-rollback, and by its implementation of rollback via antimessages.

Virtual time

The problem of system simulation is typically solved in a sequential manner due to the wide and intensive sharing of variables by all parts of the system. We propose a distributed solution where processes communicate only through messages with their neighbors; there are no shared variables and there is no central process for message routing or process scheduling. Deadlock is avoided in this system despite the absence of global control. Each process in the solution requires only a limited amount of memory. The correctness of a distributed system is proven by proving the correctness of each of its component processes and then using inductive arguments. The proposed solution has been empirically found to be efficient in preliminary studies. The paper presents formal, detailed proofs of correctness.

Distributed Simulation: A Case Study in Design and Verification of Distributed Programs

An approach to carrying out asynchronous, distributed simulation on multiprocessor messagepassing architectures is presented. This scheme differs from other distributed simulation schemes because (1) the amount of memory required by all processors together is bounded and is no more than the amount required in sequential simulation and (2) the multiprocessor network is allowed to deadlock, the deadlock is detected, and then the deadlock is broken. Proofs for the correctness of this approach are outlined.

Asynchronous distributed simulation via a sequence of parallel computations

The problem of scheduling two or more processors to minimize the execution time of a program which consists of a set of partially ordered tasks is studied. Cases where task execution times are deterministic and others in which execution times are random variables are analyzed. It is shown that different algorithms suggested in the literature vary significantly in execution time and that the B-schedule of Coffman and Graham is near-optimal. A dynamic programming solution for the case in which execution times are random variables is presented.

A comparison of list schedules for parallel processing systems

We present a proof method for networks of processes in which component processes communicate exclusively through messages We show how to construct proofs of invariant properties which hold at all times during network computation, and terminal properties which hold upon termination of network computation, if network computation terminates The proof method is based upon specifying a process by a pair of assertions, analogous to pre-and post-conditions in sequential program proving The correctness of network specification is proven by applying inference rules to the specifications of component processes Several examples are proved using this technique

Proofs of Networks of Processes

The problem of resolving conflicts between processes in distributed systems is of practical importance. A conflict between a set of processes must be resolved in favor of some (usually one) process and against the others: a favored process must have some property that distinguishes it from others. To guarantee fairness, the distinguishing property must be such that the process selected for favorable treatment is not always the same. A distributed implementation of an acyclic precedence graph, in which the depth of a process (the longest chain of predecessors) is a distinguishing property, is presented. A simple conflict resolution rule coupled with the acyclic graph ensures fair resolution of all conflicts. To make the problem concrete, two paradigms are presented: the well-known distributed dining philosophers problem and a generalization of it, the distributed drinking philosophers problem.

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K. M. Chandy

Papers

Distributed Simulation: A Case Study in Design and Verification of Distributed Programs

Asynchronous distributed simulation via a sequence of parallel computations

A comparison of list schedules for parallel processing systems

Proofs of Networks of Processes

The drinking philosophers problem