Abstract Diagnosis for Timed Concurrent Constraint programs

The Timed Concurrent Constraint Language (tccp in short) is a concurrent logic language based on the simple but powerful concurrent constraint paradigm of Saraswat. In this paradigm, the notion of store-as-value is replaced by the notion of store-as-constraint, which introduces some differences w.r.t. other approaches to concurrency. In this paper, we provide a general framework for the debugging of tccp programs. To this end, we first present a new compact, bottom-up semantics for the language that is well suited for debugging and verification purposes in the context of reactive systems. We also provide an abstract semantics that allows us to effectively implement debugging algorithms based on abstract interpretation. Given a tccp program and a behavior specification, our debugging approach automatically detects whether the program satisfies the specification. This differs from other semiautomatic approaches to debugging and avoids the need to provide symptoms in advance. We show the efficacy of our approach by introducing two illustrative examples. We choose a specific abstract domain and show how we can detect that a program is erroneous.Comment: 16 page

The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence

Intelligent systems based on first-order logic on the one hand, and on artificial neural networks (also called connectionist systems) on the other, differ substantially. It would be very desirable to combine the robust neural networking machinery with symbolic knowledge representation and reasoning paradigms like logic programming in such a way that the strengths of either paradigm will be retained. Current state-of-the-art research, however, fails by far to achieve this ultimate goal. As one of the main obstacles to be overcome we perceive the question how symbolic knowledge can be encoded by means of connectionist systems: Satisfactory answers to this will naturally lead the way to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page

Decidability and Synthesis of Abstract Inductive Invariants

Decidability and synthesis of inductive invariants ranging in a given domain play an important role in many software and hardware verification systems. We consider here inductive invariants belonging to an abstract domain $A$ as defined in abstract interpretation, namely, ensuring the existence of the best approximation in $A$ of any system property. In this setting, we study the decidability of the existence of abstract inductive invariants in $A$ of transition systems and their corresponding algorithmic synthesis. Our model relies on some general results which relate the existence of abstract inductive invariants with least fixed points of best correct approximations in $A$ of the transfer functions of transition systems and their completeness properties. This approach allows us to derive decidability and synthesis results for abstract inductive invariants which are applied to the well-known Kildall's constant propagation and Karr's affine equalities abstract domains. Moreover, we show that a recent general algorithm for synthesizing inductive invariants in domains of logical formulae can be systematically derived from our results and generalized to a range of algorithms for computing abstract inductive invariants

Reflective Relational Machines

AbstractWe propose a model of database programming withreflection(dynamic generation of queries within the host programming language), called thereflective relational machine, and characterize the power of this machine in terms of known complexity classes. In particular, the polynomial time restriction of the reflective relational machine is shown to express PSPACE, and to correspond precisely to uniform circuits of polynomial depth and exponential size. This provides an alternative, logic based formulation of the uniform circuit model, which may be more convenient for problems naturally formulated in logic terms, and establishes that reflection allows for more “intense” parallelism, which is not attainable otherwise (unless P=PSPACE). We also explore the power of the reflective relational machine subject to restrictions on the number of variables used, emphasizing the case of sublinear bounds

Combining parallel search and parallel consistency in constraint programming

Program parallelization becomes increasingly important when new multi-core architectures provide ways to improve performance. One of the greatest challenges of this development lies in programming parallel applications. Declarative languages, such as constraint programming, can make the transition to parallelism easier by hiding the parallelization details in a framework. Automatic parallelization in constraint programming has mostly focused on parallel search. While search and consistency are intrinsically linked, the consistency part of the solving process is often more time-consuming. We have previously looked at parallel consistency and found it to be quite promising. In this paper we investigate how to combine parallel search with parallel consistency. We evaluate which problems are suitable and which are not. Our results show that parallelizing the entire solving process in constraint programming is a major challenge as parallel search and parallel consistency typically suit different types of problems