Implementing the Davis–Putnam Method

The method proposed by Davis, Putnam, Logemann, and Loveland for propositional reasoning, often referred to as the Davis–Putnam method, is one of the major practical methods for the satisfiability (SAT) problem of propositional logic. We show how to implement the Davis–Putnam method efficiently using the trie data structure for propositional clauses. A new technique of indexing only the first and last literals of clauses yields a unit propagation procedure whose complexity is sublinear to the number of occurrences of the variable in the input. We also show that the Davis–Putnam method can work better when unit subsumption is not used. We illustrate the performance of our programs on some quasigroup problems. The efficiency of our programs has enabled us to solve some open quasigroup problems.     

由一阶逻辑公式得到命题逻辑可满足性问题实例

命题逻辑可满足性(SAT)问题是计算机科学中的一个重要问题.近年来许多学者在这方面进行了大量的研究,提出了不少有效的算法.但是,很多实际问题如果用一组一阶逻辑公式来描述,往往更为自然.当解释的论域是一个固定大小的有限集合时,一阶逻辑公式的可满足性问题可以等价地归约为SAT问题.为了利用现有的高效SAT工具,提出了一种从一阶逻辑公式生成SAT问题实例的算法,并描述了一个自动的转换工具,给出了相应的实验结果.还讨论了通过增加公式来消除同构从而减小搜索空间的一些方法.实验表明,这一算法是有效的,可以用来解决数学研究和实际应用中的许多问题.     

Towards a Logic of Perishable Propositions

Reasoning with uncertain information is a central issue for Artificial Intelligence and Information Systems. Consequently, a vast amount of studies and results devoted to this issue can be found in the literature.Curiously, reasoning with information whose uncertaintyvaries along time cannot be found so much in the literature. Dynamic reliability is a feature found rather frequently in many problems, e.g. in financial markets, meteorology and demographics.In this article we consider propositions whose information about truth evaluation is gradually lost with time. Our motivation has come from the information system under development within the project SIDAM—Distributed Information Systems for Mobile Agents (supported by FAPESP, grant 98/06138-2)—to provide citizens with information about urban automotive traffic in a large city.We propose functions of loss of information, and show how they can be used to characterize the obsolescence of propositional information in a logical system.     

Parallelization of deduction strategies: An analytical study

In this paper we present a general analysis of the parallelization of deduction strategies. We classify strategies assubgoal-reduction strategies, expansion-oriented strategies, andcontraction-based strategies. For each class we analyze how and what types of parallelism can be utilized. Since the operational semantics of deduction-based programming languages can be construed as subgoal-reduction strategies, our analysis encompasses, at the abstract level, both strategies for deduction-based programming and those for theorem proving. We distinguish different types of parallel deduction based on the granularity of parallelism. These two criteria — the classification of strategies and of types of parallelism — provide us with a framework to treat problems and with a grid to classify approaches to parallel deduction. Within this framework, we analyze many issues, including the dynamicity and size of the database of clauses during the derivation, the possibility of conflicts between parallel inferences, and duplication versus sharing of clauses. We also suggest the type of architectures that may be suitable for each class of strategies. We substantiate our analysis by describing existing methods, emphasizing parallel expansion-oriented strategies and parallel contraction-based strategies for theorem proving. The most interesting and least explored by existing approaches are the contraction-based strategies. The presence of contraction rules — rules that delete clauses — and especially the application ofbackward contraction, emerges as a key issue for parallelization of these strategies. Backward contraction is the main reason for the impressive experimental success of contraction-based strategies. Our analysis shows that backward contraction makes efficient parallelization much more difficult. In our analysis, coarse-grain parallelism appears to be the best choice for parallelizing contraction-based reasoning. Accordingly, we propose a notion ofparallelism at the search level as coarse-grain parallelism for deduction.Supported by the GE Foundation Faculty Fellowship to the University of Iowa and by the National Science Foundation with grant CCR-94-08667.Supported by grant NSC 83-0408-E-002-012T of the National Science Council of the Republic of China.     

Mining Propositional Simplification Proofs for Small Validating Clauses

The problem of obtaining small conflict clauses in SMT systems has received a great deal of attention recently. We report work in progress to find small subsets of the current partial assignment that imply the goal formula when it has been propositionally simplified to a boolean value. The approach used is algebraic proof mining. Proofs from a propositional reasoner that the goal is equivalent to a boolean value (in the current assignment) are viewed as first-order terms. An equational theory between proofs is then defined, which is sound with respect to the quasi-order “proves a more general set theorems.” The theory is completed to obtain a convergent rewrite system that puts proofs into a canonical form. While our canonical form does not use the smallest subset of the current assignment, it does drop many unnecessary parts of the proof. The paper concludes with discussion of the complexity of the problem and effectiveness of the approach.     

Solving satisfiability problems with preferences

Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider.     

A simplifier for propositional formulas with many binary clauses

Deciding whether a propositional formula in conjunctive normal form is satisfiable (SAT) is an NP-complete problem. The problem becomes linear when the formula contains binary clauses only. Interestingly, the reduction to SAT of a number of well-known and important problems--such as classical AI planning and automatic test pattern generation for circuits--yields formulas containing many binary clauses. In this paper we introduce and experiment with 2-SIMPLIFY, a formula simplifier targeted at such problems. 2-SIMPLIFY constructs the transitive closure of the implication graph corresponding to the binary clauses in the formula and uses this graph to deduce new unit literals. The deduced literals are used to simplify the formula and update the graph, and so on, until stabilization. Finally, we use the graph to construct an equivalent, simpler set of binary clauses. Experimental evaluation of this simplifier on a number of bench-mark formulas produced by encoding AI planning problems prove 2-SIMPLIFY to be a useful tool in many circumstances.     

Excluding Symmetries in Constraint-Based Search

We introduce a new method, called symmetry excluding search (SES), for excluding symmetries in constraint based search. To our knowledge, it is the first declarative method that can be applied to arbitrary symmetries. The SES-method is based on the notion of symmetric constraints, which are used in our modification of a general constraint based search algorithm. The method does not influence the search strategy. Furthermore, it can be used with either the full set of symmetries, or a subset of all symmetries.We proof correctness, completeness and symmetry exclusion properties of our method. Then, we show how to apply the SES-method in the special case of geometric symmetries (rotations and reflections) and permutation symmetries. Furthermore, we give results from practical applications.     

Constraint-based probabilistic modeling for statistical abduction

We introduce a new framework for logic-based probabilistic modeling called constraint-based probabilistic modeling which defines CBPMs (constraint-based probabilistic models) , i.e. conditional joint distributions P(⋅∣KB) over independent propositional variables constrained by a knowledge base KB consisting of clauses. We first prove that generative models such as PCFGs and discriminative models such as CRFs have equivalent CBPMs as long as they are discrete. We then prove that CBPMs in infinite domains exist which give existentially closed logical consequences of KB probability one. Finally we derive an EM algorithm for the parameter learning of CBPMs and apply it to statistical abduction.     

The problem of finding a semantic strategy for focusing inference rules

This article is the fifteenth of a series of articles discussing various open research problems in automated reasoning. Here we focus on finding a strategy — other than a simple syntactic one — that can be used for focusing the attention of the inference rule(s) in use. The problem proposed for research asks one to find semantic criteria for selecting the clauses needed to complete the application of an inference rule. Suggestions for test problems are included. We note that Yuan Yu has solved two of these problems using a rather different approach with the Boyer-Moore theorem prover; his work is reported elsewhere in this issue of the Journal of Automated Reasoning.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under Contract W-31-109-Eng-38.     

Reducing Higher-Order Theorem Proving to a Sequence of SAT Problems

We describe a complete theorem proving procedure for higher-order logic that uses SAT-solving to do much of the heavy lifting. The theoretical basis for the procedure is a complete, cut-free, ground refutation calculus that incorporates a restriction on instantiations. The refined nature of the calculus makes it conceivable that one can search in the ground calculus itself, obtaining a complete procedure without resorting to meta-variables and a higher-order lifting lemma. Once one commits to searching in a ground calculus, a natural next step is to consider ground formulas as propositional literals and the rules of the calculus as propositional clauses relating the literals. With this view in mind, we describe a theorem proving procedure that primarily generates relevant formulas along with their corresponding propositional clauses. The procedure terminates when the set of propositional clauses is unsatisfiable. We prove soundness and completeness of the procedure. The procedure has been implemented in a new higher-order theorem prover, Satallax, which makes use of the SAT-solver MiniSat. We also describe the implementation and give several examples. Finally, we include experimental results of Satallax on the higher-order part of the TPTP library.     

Dealing with missing values in neural network-based diagnostic systems

Backpropagation neural networks have been applied to prediction and classification problems in many real world situations. However, a drawback of this type of neural network is that it requires a full set of input data, and real world data is seldom complete. We have investigated two ways of dealing with incomplete data — network reduction using multiple neural network classifiers, and value substitution using estimated values from predictor networks — and compared their performance with an induction method. On a thyroid disease database collected in a clinical situation, we found that the network reduction method was superior. We conclude that network reduction can be a useful method for dealing with missing values in diagnostic systems based on backpropagation neural networks.     

命题逻辑的子句集中文字的分类

检测和消除命题逻辑公式中的冗余文字,是人工智能领域广泛研究的基本问题。针对命题逻辑的子句集中子句的划分,结合冗余子句和冗余文字的概念,将命题逻辑的子句集中的文字分为必需文字、有用文字和无用文字3类,并分别给出其定义。讨论3种文字与无冗余等价子集的性质,给出其等价子集的等价描述方法。得到题逻辑的子句集中必需文字、有用文字和无用文字的判定方法,借助子句集的可满足性得到3种文字与子句集的可满足性的等价条件。上述结果对命题逻辑中文字属性的判断提供了多种可选择方法,同时为命题逻辑公式的化简奠定了理论基础。     

Propositional semantics for disjunctive logic programs

In this paper we study the properties of the class of head-cycle-free extended disjunctive logic programs (HEDLPs), which includes, as a special case, all nondisjunctive extended logic programs. We show that any propositional HEDLP can be mapped in polynomial time into a propositional theory such that each model of the latter corresponds to an answer set, as defined by stable model semantics, of the former. Using this mapping, we show that many queries over HEDLPs can be determined by solving propositional satisfiability problems. Our mapping has several important implications: It establishes the NP-completeness of this class of disjunctive logic programs; it allows existing algorithms and tractable subsets for the satisfiability problem to be used in logic programming; it facilitates evaluation of the expressive power of disjunctive logic programs; and it leads to the discovery of useful similarities between stable model semantics and Clark's predicate completion.     

Configuration structures, event structures and Petri nets

In this paper the correspondence between safe Petri nets and event structures, due to Nielsen, Plotkin and Winskel, is extended to arbitrary nets without self-loops, under the collective token interpretation. To this end we propose a more general form of event structure, matching the expressive power of such nets. These new event structures and nets are connected by relating both notions with configuration structures, which can be regarded as representations of either event structures or nets that capture their behaviour in terms of action occurrences and the causal relationships between them, but abstract from any auxiliary structure.A configuration structure can also be considered logically, as a class of propositional models, or—equivalently—as a propositional theory in disjunctive normal from. Converting this theory to conjunctive normal form is the key idea in the translation of such a structure into a net.For a variety of classes of event structures we characterise the associated classes of configuration structures in terms of their closure properties, as well as in terms of the axiomatisability of the associated propositional theories by formulae of simple prescribed forms, and in terms of structural properties of the associated Petri nets.     

Verifying Programs with Unreliable Channels

We consider the verification of a particular class of infinite-state systems, namely systems consisting of finite-state processes that communicate via unbounded lossy FIFO channels. This class is able to model, e.g., link protocols such as the Alternating Bit Protocol and HDLC. For this class of systems, we show that several interesting verification problems are decidable by giving algorithms for verifying (1) thereachability problem—is a finite set of global states reachable from some other global state of the system ? (2)safety properties over tracesformulated as regular sets of allowed finite traces, and (3)eventuality properties—do all computations of a system eventually reach a given set of states? We have used the algorithms to verify some idealized sliding-window protocols with reasonable time and space resources. Our results should be contrasted with the well-known fact that these problems are undecidable for systems with unboundedperfectFIFO channels.     

The equivalence of Horn and network complexity for Boolean functions

Summary We propose in this paper a logical complexity measure — Horn complexity — for Boolean functions which measures the minimal length of quasi-Horn definitions of such functions by propositional formulae. The interest for this complexity measure comes on the one hand from the observation that the satisfiability problem for Horn formulae is in P, on the other hand from a strong connection to Cook's problem. We show the proposed Horn complexity to be polynomially equivalent to network complexity and therefore to Turing complexity for Boolean functions.A preliminary version of this work has appeared in the Proceedings of the 5^th Scandinavian Logic Symposium, edited by B. Mayoh and F. Jensen, Aalborg University Press, Aalborg 1979     

N-Prolog and equivalence of logic programs

The aim of this work is to develop a declarative semantics for N-Prolog with negation as failure. N-Prolog is an extension of Prolog proposed by Gabbay and Reyle (1984, 1985), which allows for occurrences of nested implications in both goals and clauses. Our starting point is an operational semantics of the language defined by means of top-down derivation trees. Negation as finite failure can be naturally introduced in this context. A goal-G may be inferred from a database if every top-down derivation of G from the database finitely fails, i.e., contains a failure node at finite height.Our purpose is to give a logical interpretation of the underlying operational semantics. In the present work (Part 1) we take into consideration only the basic problems of determining such an interpretation, so that our analysis will concentrate on the propositional case. Nevertheless we give an intuitive account of how to extend our results to a first order language. A full treatment of N-Prolog with quantifiers will be deferred to the second part of this work.Our main contribution to the logical understanding of N-Prolog is the development of a notion of modal completion for programs, or databases. N-Prolog deductions turn out to be sound and complete with respect to such completions. More exactly, we introduce a natural modal three-valued logic PK and we prove that a goal is derivable from a propositional program if and only if it is implied by the completion of the program in the logic PK. This result holds for arbitrary programs. We assume no syntactic restriction, such as stratification (Apt et al. 1988; Bonner and McCarty 1990). In particular, we allow for arbitrary recursion through negation.Our semantical analysis heavily relies on a notion of intensional equivalence for programs and goals. This notion is naturally induced by the operational semantics, and is preserved under substitution of equivalent subexpressions. Basing on this substitution property we develop a theory of normal forms of programs and goals. Every program can be effectively transformed into an equivalent program in normal form. From the simple and uniform structure of programs in normal form one may directly define the completion.     

A Temporal Logic for Proving Properties of Topologically General Executions

We present a generalization of the temporal propositional logic of linear time which is useful for stating and proving properties of the generic execution sequence of a parallel program or a non-deterministic program. The formal system we present is exactly that same as the third of three logics presented by Lehmann and Shelah (Information and Control53, 165–198 (1982)), but we give it a different semantics. The models are tree models of arbitrary size similar to those used in branching time temporal logic. The formulation we use allows us to state properties of the “co-meagre” family of paths, where the term “co-meagre” refers to a set whose complement is of the first category in Baire's classification looking at the set of paths in the model as a metric space. Our system is decidable, sound, and, complete for models of arbitrary size, but it has the finite model property; namely, every sentence having a model has a finite model.