Polynomial-time inference of all valid implications for Horn and related formulae

This paper investigates the complexity of a general inference problem: given a propositional formula in conjunctive normal form, find all prime implications of the formula. Here, a prime implication means a minimal clause whose validity is implied by the validity of the formula. We show that, under some reasonable assumptions, this problem can be solved in time polynomially bounded in the size of the input and in the number of prime implications. In the case of Horn formulae, the result specializes to yield an algorithm whose complexity grows only linearly with the number of prime implications. The result also applies to a class of formulae generalizing both Horn and quadratic formulae.To the memory of Robert G. Jeroslow     

Horn equational theories and paramodulation

A language of equational programs together with an inference system, based on paramodulation is defined. The semantics of the language is given with respect to least models, least fixpoints and success sets and its soundness and completeness is proven using fixpoint theory. The necessity of the functional reflexive axioms is investigated in detail. Finally, the application of these ideas to term rewriting systems is outlined by discussing directed paramodulation and narrowing.     

Recognition of Simple Enlarged Horn Formulas and Simple Extended Horn Formulas

The enlarged Horn formulas generalize the extended Horn formulas introduced by Chandru and Hooker (1991). Their satisfying truth assignments can be generated with polynomial delay. Unfortunately no polynomial algorithm is known for recognizing enlarged Horn formulas or extended Horn formulas. In this paper we define the class of simple enlarged Horn formulas, a subclass of the enlarged Horn formulas, that contains the simple extended Horn formulas introduced by Swaminathan and Wagner (1995). We present recognition algorithms for the simple enlarged Horn formulas and the simple extended Horn formulas whose complexity is bounded by the complexity of the arborescence-realization problem.     

Recognition and dualization of disguised bidual Horn functions

We consider the problem of dualizing a Boolean function f given by CNF, i.e., computing a CNF for its dual f^d. While this problem is not solvable in quasi-polynomial total time in general (unless SAT is solvable in quasi-polynomial time), it is so in case the input belongs to special classes, e.g., the class of bidual Horn CNF ? [Discrete Appl. Math. 96-97 (1999) 55-88] (i.e., both ? and its dual ?^d represent Horn functions). In this paper, we show that a disguised bidual Horn CNF ? (i.e., ? becomes a bidual Horn CNF after renaming of variables) can be recognized in polynomial time, and its dualization can be done in quasi-polynomial total time. We also establish a similar result for dualization of prime CNFs.     

Learning Function-Free Horn Expressions

The problem of learning universally quantified function free first order Horn expressions is studied. Several models of learning from equivalence and membership queries are considered, including the model where interpretations are examples (Learning from Interpretations), the model where clauses are examples (Learning from Entailment), models where extensional or intentional background knowledge is given to the learner (as done in Inductive Logic Programming), and the model where the reasoning performance of the learner rather than identification is of interest (Learning to Reason). We present learning algorithms for all these tasks for the class of universally quantified function free Horn expressions. The algorithms are polynomial in the number of predicate symbols in the language and the number of clauses in the target Horn expression but exponential in the arity of predicates and the number of universally quantified variables. We also provide lower bounds for these tasks by way of characterising the VC-dimension of this class of expressions. The exponential dependence on the number of variables is the main gap between the lower and upper bounds.     

Learning Horn definitions: Theory and an application to planning

A Horn definition is a set of Horn clauses with the same predicate in all head literals. In this paper, we consider learning non-recursive, first-order Horn definitions from entailment. We show that this class is exactly learnable from equivalence and membership queries. It follows then that this class is PAC learnable using examples and membership queries. Finally, we apply our results to learning control knowledge for efficient planning in the form of goal-decomposition rules. Chandra Reddy, Ph.D.: He is currently a doctoral student in the Department of Computer Science at Oregon State University. He is completing his Ph.D. on June 30, 1998. His dissertation is entitled “Learning Hierarchical Decomposition Rules for Planning: An Inductive Logic Programming Approach.” Earlier, he had an M. Tech in Artificial Intelligence and Robotics from University of Hyderabad, India, and an M.Sc.(tech) in Computer Science from Birla Institute of Technology and Science, India. His current research interests broadly fall under machine learning and planning/scheduling—more specifically, inductive logic programming, speedup learning, data mining, and hierarchical planning and optimization. Prasad Tadepalli, Ph.D.: He has an M.Tech in Computer Science from Indian Institute of Technology, Madras, India and a Ph.D. from Rutgers University, New Brunswick, USA. He joined Oregon State University, Corvallis, as an assistant professor in 1989. He is now an associate professor in the Department of Computer Science of Oregon State University. His main area of research is machine learning, including reinforcement learning, inductive logic programming, and computational learning theory, with applications to classification, planning, scheduling, manufacturing, and information retrieval.     

On renaming a set of clauses as a Horn set

Let S = {C₁, …, C_m} be a set of clauses in the propositional calculus and let n denote the number of variables appearing these clauses. We present and O(mn) time algorithm to test whether S can be renamed as a Horn set.     

On the size of (generalized) OBDDs for threshold functions

Ordered binary decision diagrams (OBDDs) are one of the most common dynamic data structures for Boolean functions. Among the many areas of application are hardware verification, model checking, and symbolic graph algorithms. Threshold functions are the basic functions for discrete neural networks and are used as building blocks in the design of some symbolic graph algorithms. In this paper the first exponential lower bound on the size of a more general model than OBDDs and the first nontrivial asymptotically optimal bound on the OBDD size for a threshold function are presented.     

Theory revision with queries: Horn, read-once, and parity formulas

A theory, in this context, is a Boolean formula; it is used to classify instances, or truth assignments. Theories can model real-world phenomena, and can do so more or less correctly. The theory revision, or concept revision, problem is to correct a given, roughly correct concept. This problem is considered here in the model of learning with equivalence and membership queries. A revision algorithm is considered efficient if the number of queries it makes is polynomial in the revision distance between the initial theory and the target theory, and polylogarithmic in the number of variables and the size of the initial theory. The revision distance is the minimal number of syntactic revision operations, such as the deletion or addition of literals, needed to obtain the target theory from the initial theory. Efficient revision algorithms are given for Horn formulas and read-once formulas, where revision operators are restricted to deletions of variables or clauses, and for parity formulas, where revision operators include both deletions and additions of variables. We also show that the query complexity of the read-once revision algorithm is near-optimal.     

The novel generating algorithm and properties of hybrid-P-ary generalized bridge functions

1 Introduction Copy theory is a kind of signal generation technology, built on the research of Walsh function. The main content of the copy theory includes generating non-sine orthogonal functions, utilizing shift and symmetric copy methods and control in…     

An efficient algorithm for Horn description

We give a new algorithm for computing a prepositional Horn CNF formula given the set of its models. Its running time is O(|R|n(|R|+n)), where |R| is the number of models and n that of variables, and the computed CNF contains at most |R|n clauses. This algorithm also uses the well-known closure property of Horn relations in a new manner.     

Inference flexibility in Horn clause knowledge bases and the simplex method

This paper shows that the basis matrix inverse of the linear program associated with a propositional Horn clause knowledge base provides a proof structure of inference by forward chaining. The basis matrix inverse indicates how each assertion determines the others and is itself determined by the others. This tabulated proof structure provides a convenient way of making inference transparent and flexible.     

Horn axiomatizations for sequential data

We propose a notion of deterministic association rules for ordered data. We prove that our proposed rules can be formally justified by a purely logical characterization, namely, a natural notion of empirical Horn approximation for ordered data which involves background Horn conditions; these ensure the consistency of the propositional theory obtained with the ordered context. The whole framework resorts to concept lattice models from Formal Concept Analysis, but adapted to ordered contexts. We also discuss a general method to mine these rules that can be easily incorporated into any algorithm for mining closed sequences, of which there are already some in the literature.     

格值命题逻辑系统中广义文字的正规性

基于格蕴涵代数的格值命题逻辑系统能定性地刻画不可比较性和不精确性。广义文字是该系统中α-归结自动推理的核心概念,是α-归结中的最基本单元。公式的正规性是α-归结原理中保持完备性的重要条件,其语义性质是公式形式的重要反映。从语义角度研究了广义文字的正规性,给出了两种典型正规公式F1→F2和(F1→F2)'的真值情况。为讨论广义文字的形式及其α-可归结性提供了理论基础。     

Learning Conjunctions of Horn Clauses

An algorithm is presented for learning the class of Boolean formulas that are expressible as conjunctions of Horn clauses. (A Horn clause is a disjunction of literals, all but at most one of which is a negated variable.) The algorithm uses equivalence queries and membership queries to produce a formula that is logically equivalent to the unknown formula to be learned. The amount of time used by the algorithm is polynomial in the number of variables and the number of clauses in the unknown formula.     

Algorithmic Debugging and Hypothetical Reasoning

This paper presents a generalization of Shapiro style algorithmic debugging for generalized Horn clause intuitionistic logic. This logic offers hypothetical reasoning and negation is defined not by failure but by inconsistency. We extend Shapiro's notion of intended interpretation, symptoms and errors and give formal results paralleling those known for definite clauses. We also show how a corresponding diagnosis module for RISC- a logic programming system for generalized Horn clause intuitionistic logic-can be defined by meta interpretation. In contrast to Shapiro's PROLOG modules ours work independently of the specific computation rule that in RISC may be specified by the user.     

Correlations between Horn fractions, satisfiability and solver performance for fixed density random 3-CNF instances

An enhanced concept of sub-optimal reverse Horn fraction of a CNF-formula was introduced in [18]. It was shown that this fraction is very useful in effectively (almost) separating 3-colorable random graphs with fixed node-edge density from the non-3-colorable ones. A correlation between this enhanced sub-optimal reverse Horn fraction and satisfiability of random 3-SAT instances with a fixed density was observed. In this paper, we present experimental evidence that this correlation scales to larger-sized instances and that it extends to solver performances as well, both of complete and incomplete solvers. Furthermore, we give a motivation for various phases in the algorithm aHS, establishing the enhanced sub-optimal reverse Horn fraction, and we present clear evidence for the fact that the observed correlations are stronger than correlations between satisfiability and sub-optimal MAXSAT-fractions established similarly to the enhanced sub-optimal reverse Horn fraction. The latter observation is noteworthy because the correlation between satisfiability and the optimal MAXSAT-fraction is obviously 100%. AMS subject classification 90C05, 03B99, 68Q01, 68W01