First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Automating abstraction functions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-119).Data abstraction has been the dominant structuring paradigm for programs for decades. The essence of a data abstraction is the abstraction function, which relates the concrete program representation to its abstract meaning. However, abstraction functions are not generally considered to be a part of the executing program. We propose that making abstraction functions an executable part of the program can enable programmers to write clearer and more concise programs with fewer errors. In particular, we show that the object equality and hashing operations (which programmers are required to write), can often be expressed more clearly and more concisely in terms of the abstract state of the object. Getting these methods right has proven to be difficult for programmers at all skill levels, from novice through expert. In a case study of the standard Java libraries we show that rewriting the code with explicit declarative abstraction functions (and generating equality and hashing methods automatically) removed object-contract compliance faults previously found by Pacheco et al. To make abstraction functions part of the executing program we develop four techniques for the dynamic evaluation of abstraction functions written in a declarative first-order logic with relations and transitive closure. We observe that the abstraction functions programmers write in practice may often be viewed as navigation queries on the heap, and two of our techniques exploit this insight to synthesize executable code from declarative abstraction functions. The performance of our research prototype is within striking distance of hand-written code.by Derek F. Rayside.Ph.D

Proceedings of the Workshop on the lambda-Prolog Programming Language

The expressiveness of logic programs can be greatly increased over first-order Horn clauses through a stronger emphasis on logical connectives and by admitting various forms of higher-order quantification. The logic of hereditary Harrop formulas and the notion of uniform proof have been developed to provide a foundation for more expressive logic programming languages. The λ-Prolog language is actively being developed on top of these foundational considerations. The rich logical foundations of λ-Prolog provides it with declarative approaches to modular programming, hypothetical reasoning, higher-order programming, polymorphic typing, and meta-programming. These aspects of λ-Prolog have made it valuable as a higher-level language for the specification and implementation of programs in numerous areas, including natural language, automated reasoning, program transformation, and databases