Abstract not found

A novel logic program like language, weight constraint rules, is developed for answer set programming purposes. It generalizes normal logic programs by allowing weight constraints in place of literals to represent, e.g., cardinality and resource constraints and by providing optimization capabilities. A declarative semantics is developed which extends the stable model semantics of normal programs. The computational complexity of the language is shown to be similar to that of normal programs under the stable model semantics. A simple embedding of general weight constraint rules to a small subclass of the language called basic constraint rules is devised. An implementation of the language, the smodels system, is developed based on this embedding. It uses a two level architecture consisting of a front-end and a kernel language implementation. The front-end allows restricted use of variables and functions and compiles general weight constraint rules to basic constraint rules. A major part of the work is the development of an ecient search procedure for computing stable models for this kernel language. The procedure is compared with and empirically tested against satis ability checkers and an implementation of the stable model semantics. It offers a competitive implementation of the stable model semantics for normal programs and attractive performance for problems where the new types of rules provide a compact representation.

The idea of answer set programming is to represent a given computational problem by a logic program whose answer sets correspond to solutions, and then use an answer set solver, such as smodels or dlv, to find an answer set for this program. Applications of this method to planning are related to the line of research on the frame problem that started with the invention of formal nonmonotonic reasoning in 1980.

The paper studies an implementation methodology for partial and disjunctive stable models where partiality and disjunctions are unfolded from a logic program so that an implementation of stable models for normal (disjunction-free) programs can be used as the core inference engine. The unfolding is done in two separate steps. Firstly, it is shown that partial stable models can be captured by total stable models using a simple linear and modular program transformation. Hence, reasoning tasks concerning partial models can be solved using an implementation of total models. Disjunctive partial stable models have been lacking implementations which now become available as the translation handles also the disjunctive case. Secondly, it is shown how total stable models of disjunctive programs can be determined by computing stable models for normal programs. Hence, an implementation of stable models of normal programs can be used as a core engine for implementing disjunctiv...

We present an extension of language A-Prolog by consistency-restoring rules with preferences, give the semantics of the new language, CR-Prolog, and show how the language can be used to formalize various types of commonsense knowledge and reasoning.

The Smodels system implements the stable model semantics for normal logic programs. It handles a subclass of programs which contain no function symbols and are domain-restricted but supports extensions including built-in functions as well as cardinality and weight constraints. On top of this core engine more involved systems can be built. As an example, we have implemented total and partial stable model computation for disjunctive logic programs. An interesting application method is based on answer set programming, i.e., encoding an application problem as a set of rules so that its solutions are captured by the stable models of the rules. Smodels has been applied to a number of areas including planning, model checking, reachability analysis, product configuration, dynamic constraint satisfaction, and feature interaction. General Information The Smodels system is written in C++ and the source code, test cases and documentation are available at

In this paper bounded model checking of asynchronous concurrent systems is introduced as a promising application area for answer set programming. As the model of asynchronous systems a generalisation of communicating automata, 1-safe Petri nets, are used. It is shown how a 1-safe Petri net and a requirement on the behaviour of the net can be translated into a logic program such that the bounded model checking problem for the net can be solved by computing stable models of the corresponding program. The use of the stable model semantics leads to compact encodings of bounded reachability and deadlock detection tasks as well as the more general problem of bounded model checking of linear temporal logic. Correctness proofs of the devised translations are given, and some experimental results using the translation and the Smodels system are presented.

The rules associated with propositional logic programs and the stable model semantics are not expressive enough to let one write concise programs. This problem is alleviated by introducing some new types of propositional rules. Together with a decision procedure that has been used as a base for an ecient implementation, the new rules supplant the standard ones in practical applications of the stable model semantics. 1 Introduction Logic programming with the stable model semantics has emerged as a viable method for solving constraint satisfaction problems [4, 5]. The state-ofthe -art system smodels [6] can often handle non-stratied programs with tens of thousands of rules. However, propositional logic programs can not compactly encode several types of constraints. For example, expressing the subsets of size k of an n-sized set as stable models requires on the order of nk rules. In order to remedy this problem, we improve upon the techniques of smodels, by extending the semantics with...

In this paper, we develop a general technique for truncating Petri net unfoldings, parameterized according to the level of information about the original unfolding one wants to preserve. Moreover, we propose a new notion of completeness of a truncated unfolding, which with the standard parameters is strictly stronger than that given in [5, 6], and is more appropriate for the existing model checking algorithms.

A new approach is presented for detecting whether a computation of an asynchronous distributed system satisfies Poss (read "possibly"), meaning the system could have passed through a global state satisfying property. Previous general-purpose algorithms for this problem explicitly enumerate the set of global states through which the system could have passed during the computation. The new approach is to represent this set symbolically, in particular, using ordered binary decision diagrams. We describe an implementation of this approach, suitable for off-line detection of properties, and compare its performance to the enumeration-based algorithm of Alagar & Venkatesan. In typical cases, the new algorithm is signi cantly faster. We have measured over 400-fold speedup in some cases.