Agostino Dovier

An efficient algorithm for computing bisimulation equivalence

We propose an efficient algorithmic solution to the problem of determining a Bisimulation Relation on a finite structure working both on the explicit and on the implicit (symbolic) representation. As far as the explicit case is concerned, starting from a set-theoretic point of view we propose an algorithm that optimizes the solution to the Relational Coarsest Partition Problem given by Paige and Tarjan (SIAM J. Comput. 16(6) (1987) 973); its use in model-checking packages is also discussed and tested. For well-structured graphs our algorithm reaches a linear worst-case behaviour. The proposed algorithm is then re-elaborated to produce a symbolic version.

{log}: A language for programming in logic with finite sets

Abstract An extended logic programming language is presented, that embodies the fundamental form of set designation based on the (nesting) element insertion operator. The kind of sets to be handled is characterized both by adaptation of a suitable Herbrand universe and via axioms. Predicates ϵ and = designating set membership and equality are included in the base language, along with their negative counterparts ∉ and ≠. A unification algorithm that can cope with set terms is developed and proved correct and terminating. It is proved that by incorporating this new algorithm into SLD resolution and providing suitable treatment of ϵ, ≠, and ∉ as constraints, one obtains a correct management of the distinguished set predicates. Restricted universal quantifiers are shown to be programmable directly in the extended language and thus are added to the language as a convenient syntactic extension. A similar solution is shown to be applicable to intensional set-formers, provided either a built-in set collection mechanism or some form of negation in goals and clause bodies is made available.

Constraint Logic Programming approach to protein structure prediction

BackgroundThe protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems.ResultsConstraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics.ConclusionsThe results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

A Fast Bisimulation Algorithm

In this paper we propose an efficient algorithmic solution to the problem of determining a Bisimulation Relation on a finite structure. Starting from a set-theoretic point of view we propose an algorithm that optimizes the solution to the Relational coarsest Partition problem given by Paige and Tarjan in 1987 and its use in model-checking packages is briefly discussed and tested. Our algorithm reaches, in particular cases, a linear solution.

A comparison of CLP(FD) and ASP solutions to NP-Complete problems

This paper presents experimental comparisons between declarative encodings of various computationally hard problems in both Answer Set Programming (ASP) and Constraint Logic Programming (CLP) over finite domains. The objective is to identify how the solvers in the two domains respond to different problems, highlighting strengths and weaknesses of their implementations and suggesting criteria for choosing one approach versus the other. Ultimately, the work in this paper is expected to lay the ground for transfer of concepts between the two domains (e.g., suggesting ways to use CLP in the execution of ASP).

An empirical study of constraint logic programming and answer set programming solutions of combinatorial problems

This paper presents experimental comparisons between the declarative encodings of various computationally hard problems in Answer Set Programming (ASP) and Constraint Logic Programming over Finite Domains (CLP(FD)). The objective is to investigate how solvers in the two domains respond to different problems, highlighting the strengths and weaknesses of their implementations, and suggesting criteria for choosing one approach over the other. Ultimately, the work in this paper is expected to lay the foundations for a transfer of technology between the two domains, for example by suggesting ways to use CLP(FD) in the execution of ASP. †A preliminary version of this paper appeared in the Proceedings of the International Conference on Logic Programming, 2005, pp. 67–82.

GASP: Answer Set Programming with Lazy Grounding

In recent years, Answer Set Programming has gained popularity as a viable paradigm for applications in knowledge representation and reasoning. This paper presents a novel methodology to compute answer sets of an answer set program. The proposed methodology maintains a bottom-up approach to the computation of answer sets (as in existing systems), but it makes use of a novel structuring of the computation, that originates from the non-ground version of the program. Grounding is lazily performed during the computation of the answer sets. The implementation has been realized using Constraint Logic Programming over finite domains.

Answer Set Programming with Constraints Using Lazy Grounding

The paper describes a novel methodology to compute stable models in Answer Set Programming. The proposed approach relies on a bottom-up computation that does not require a preliminary grounding phase. The implementation of the framework can be completely realized within the framework of Constraint Logic Programming over finite domains. The use of a high level language for the implementation and the clean structure of the computation offer an ideal framework for the implementation of extensions of Answer Set Programming. In this work, we demonstrate how non-ground arithmetic constraints can be easily introduced in the computation model. The paper provides preliminary experimental results which confirm the potential for this approach.

A uniform approach to constraint-solving for lists, multisets, compact lists, and sets

Lists, multisets, and sets are well-known data structures whose usefulness is widely recognized in various areas of computer science. They have been analyzed from an axiomatic point of view with a parametric approach in Dovier et al. [1998], where the relevant unification algorithms have been developed. In this article, we extend these results considering more general constraints, namely, equality and membership constraints and their negative counterparts.

The Subgraph Bisimulation Problem

We study the complexity of the Subgraph Bisimulation Problem, which relates to Graph Bisimulation as Subgraph Isomorphism relates to Graph Isomorphism, and we prove its NP-Completeness. Our analysis is motivated by its applications to semistructured databases.