... quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1.3 U1, POSIT can solve hard random 400-variable 3-SAT problems in about 2 hours on the average. In general, it can solve hard n-variable random 3-SAT problems with search trees of size O(2 n=18:7 ). In addition to justifying these claims, this dissertation describes the most significant achievements of other researchers in this area, and discusses all of the widely known general techniques for speeding up SAT search algorithms. It should be useful to anyone interested in NP-complete problems or combinatorial optimization in general, and it should be particularly useful to researchers in either Artificial Intelligence or Operations Research.

General purpose truth maintenance systems have received considerable attention in the past few years. This paper discusses the functionality of truth maintenance systems and compares various existing algorithms. Applications and directions for future research are also discussed. Introduction In 1978 Jon Doyle wrote a masters thesis at the MIT AI Laboratory entitled "Truth Maintenance Systems for Problem Solving" [ Doyle, 1979 ] . In this thesis Doyle described an independent module called a truth maintenance system, or TMS, which maintained beliefs for general problem solving systems. In the twelve years since the appearance of Doyle's TMS a large body of literature has accumulated on truth maintenance. The seminal idea appears not to have been any particular technical mechanism but rather the general concept of an independent module for truth (or belief) maintenance. All truth maintenance systems manipulate proposition symbols and relationships between proposition symbols. I will use...

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This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part the National Science Foundation contract IRI-8819624 and in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-86-K-0124

Decision procedures are increasingly being employed for deciding or simplifying propositional combinations of ground equalities involving uninterpreted function symbols, linear arithmetic, arrays, and other theories. Two approaches for constructing decision procedures for combinations of ground theories were pioneered in the late seventies. In the approach of Nelson and Oppen, decision procedures for two disjoint theories are combined by introducing variables to name subterms and iteratively propagating any deduced equalities between variables from one theory to another. Shostak employs a different approach that works far more efficiently in practice. He uses an optimized implementation of the congruence closure procedure for ground equality over uninterpreted function symbols to combine theories that are canonizable and algebraically solvable. Many useful theories have these properties. Shostak's algorithm is subtle and complex and his description of this procedure is lacking in ri...

Most knowledge representation languages are based on classes and taxonomic relationships between classes. Taxonomic hierarchies without defaults or exceptions are semantically equivalent to a collection of formulas in first or- der predicate calculus. Although designers of knowledge representation lan- guages often express an intuitive feeling that there must be some advantage to representing facts as taxonomic relationships rather than first order for- mulas, there are few,, if any, technical results supporting this intuition. We attempt to remedy this situation by presenting a taxonomic syntax for first order predicate calculus and a series of theorems that support the claim that taxonomic syntax is superior to classical syntax.

Our approach to both hardware and software research is to create and use real systems sothat we can investigate their properties fully. Complex systems cannot be evaluated solely in the abstract. Based on this belief, our strategy is to demonstrate the technical and practicalfeasibility of our ideas by building prototypes and using them as daily tools. The experience we gain is useful in the short term in enabling us to refine our designs, and invaluable in the longterm in helping us to advance the state of knowledge about those systems. Most of the major advances in information systems have come through this strategy, including time-sharing, theArpaNet, and distributed personal computing. SRC also performs work of a more mathematical flavor which complements our systemsresearch. Some of this work is in established fields of theoretical computer science, such as the analysis of algorithms, computational geometry, and logics of programming. The rest ofthis work explores new ground motivated by problems that arise in our systems research. DEC has a strong commitment to communicating the results and experience gained throughpursuing these activities. The Company values the improved understanding that comes with exposing and testing our ideas within the research community. SRC will therefore reportresults in conferences, in professional journals, and in our research report series. We will seek users for our prototype systems among those with whom we have common research interests,and we will encourage collaboration with university researchers.

We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structured terms consisting of quantifier, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more "natural" formalism whose use and representations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenomena such as pronouns and ellipsis. Second, the formalism allows the specification of description subsumption as a partial ordering on related concepts (variable nodes in a semantic network) that relates more general concepts to more specific instances of that concept, as is done in language. Finally, this structured variable representation simplifies the resolution of some representational difficulties with certain classes of natural language sentences...

We have previously argued that the syntactic structure of natural language can be exploited to construct powerful polynomial time inference procedures. This paper supports the earlier arguments by demonstrating that a natural language based polynomial time procedure can solve Schubert's steamroller in a single step. This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the work described in this paper was provided in part by Misubishi Electric Research Laboratories, Inc. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-85-K-0124. This paper appeared in AAAI-91. A postscript electronic source for this paper can be found in ftp.ai.mit.edu:/pub/dam/aaaib.ps. A bibtex reference can be found in ftp.ai.mit.edu:/pub/dam/dam.bib. 1 Introduction Schubert's steamro...

We consider the concept of a local set of inference rules. A local rule set can be automatically transformed into a rule set for which bottom up evaluation terminates in polynomial time. The local rule set transformation gives polynomial time evaluation strategies for a large variety of rule sets that can not be given terminating evaluation strategies by any other known automatic technique. This paper discusses three new results. First, it is shown that every polynomial time predicate can be defined by an (unstratified) local rule set. Second, a new machine recognizable subclass of the local rule sets is identified. Finally we show that locality, as a property of rule sets, is undecidable in general. This paper appeared in KR-92. A postscript electronic source for this paper can be found in ftp.ai.mit.edu:/pub/dam/kr92.ps. A bibtex reference can be found in internet file ftp.ai.mit.edu:/pub/dam/dam.bib. 1 INTRODUCTION Under what conditions does a given set of inference rules define ...