Joxan Jaffar

Bio: Joxan Jaffar is an academic researcher from National University of Singapore. The author has contributed to research in topics: Constraint logic programming & Symbolic execution. The author has an hindex of 31, co-authored 106 publications receiving 6719 citations. Previous affiliations of Joxan Jaffar include IBM & Monash University, Clayton campus.

Constraint logic programming

Abstract: We address the problem of designing programming systems to reason with and about constraints. Taking a logic programming approach, we define a class of programming languages, the CLP languages, all of which share the same essential semantic properties. From a conceptual point of view, CLP programs are highly declarative and are soundly based within a unified framework of formal semantics. This framework not only subsumes that of logic programming, but satisfies the core properties of logic programs more naturally. From a user's point of view, CLP programs have great expressive power due to the constraints which they naturally manipulate. Intuition in the reasoning about programs is enhanced as a result of working directly in the intended domain of discourse. This contrasts with working in the Herbrand Universe wherein every semantic object has to be explicitly coded into a Herbrand term; this enforces reasoning at a primitive level. Finally, from an implementor's point of view, CLP systems can be efficient because of the exploitation of constraint solving techniques over specific domains.

Constraint logic programming : A survey

Abstract: Constraint Logic Programming (CLP) is a merger of two declarative paradigms: constraint solving and logic programming. Although a relatively new field, CLP has progressed in several quite different directions. In particular, the early fundamental concepts have been adapted to better serve in different areas of applications. In this survey of CLP, a primary goal is to give a systematic description of the major trends in terms of common fundamental concepts. The three main parts cover the theory, implementation issues, and programming for applications.

The CLP( ℛ ) language and system

Abstract: The CLP( ℛ ) programming language is defined, its underlying philosophy and programming methodology are discussed, important implementation issues are explored in detail, and finally, a prototype interpreter is described.CLP( ℛ ) is designed to be an instance of the Constraint Logic Programming Scheme, a family of rule-based constraint programming languages defined by Jaffar and Lassez. The domain of computation ℛ of this particular instance is the algebraic structure consisting of uninterpreted functors over real numbers. An important property of CLP( ℛ )is that the constraints are treated uniformly in the sense that they are used to specify the input parameters to a program, they are the only primitives used in the execution of a program, and they are used to describe the output of a program.Implementation of a CLP language, and of CLP( ℛ ) in particular, raises new problems in the design of a constraint-solver. For example, the constraint solver must be incremental in the sense that solving additional constraints must not entail the resolving of old constraints. In our system, constraints are filtered through an inference engine, an engine/solver interface, an equation solver and an inequality solver. This sequence of modules reflects a classification and prioritization of the classes of constraints. Modules solving higher priority constraints are isolated from the complexities of modules solving lower priority constraints. This multiple-phase solving of constraints, together with a set of associated algorithms, gives rise to a practical system.

The Semantics of Constraint Logic Programs

Abstract: The Constraint Logic Programming (CLP) Scheme was introduced by Jaffar and Lassez. The scheme gave a formal framework, based on constraints, for the basic operational, logical and algebraic semantics of an extended class of logic programs. This paper presents for the first time the semantic foundations of CLP in a self-contained and complete package. The main contributions are threefold. First, we extend the original conference paper by presenting definitions and basic semantic constructs from first principles, giving new and complete proofs for the main lemmas. Importantly, we clarify which theorems depend on conditions such as solution compactness, satisfaction completeness and independence of constraints. Second, we generalize the original results to allow for incompleteness of the constraint solver. This is important since almost all CLP systems use an incomplete solver. Third, we give conditions on the (possibly incomplete) solver which ensure that the operational semantics is confluent, that is, has independence of literal scheduling.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Foundations of logic programming

Abstract: This is the second edition of an account of the mathematical foundations of logic programming. Its purpose is to collect, in a unified and comprehensive manner, the basic theoretical results of the field, which have previously only been available in widely scattered research papers. In addition to presenting the technical results, the book also contains many illustrative examples and problems. The text is intended to be self-contained, the only prerequisites being some familiarity with PROLOG and knowledge of some basic undergraduate mathematics. The material is suitable either as a reference book for researchers or as a textbook for a graduate course on the theoretical aspects of logic programming and deductive database systems.

Markov logic networks

Abstract: We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

Chaff: engineering an efficient SAT solver

Abstract: 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.

Principles of program analysis

Abstract: Program analysis utilizes static techniques for computing reliable information about the dynamic behavior of programs. Applications include compilers (for code improvement), software validation (for detecting errors) and transformations between data representation (for solving problems such as Y2K). This book is unique in providing an overview of the four major approaches to program analysis: data flow analysis, constraint-based analysis, abstract interpretation, and type and effect systems. The presentation illustrates the extensive similarities between the approaches, helping readers to choose the best one to utilize.