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Ilkka Niemelä

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Utrecht University

Università degli Studi di Napoli "Federico II"

Universidad Politécnica de Madrid

Cornell University

Luís Moniz Pereira

Universidade Nova de Lisboa

Francesco Scarcello

University of Calabria

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Papers by Ilkka Niemelä

Evaluating an Algorithm for Default Reasoning

by Patrik Simons and Ilkka Niemelä

In this paper performance of a lately proposed decision procedure for Reiter's default logic ... more In this paper performance of a lately proposed decision procedure for Reiter's default logic is evaluated. Recent complexity results indicate that there are two orthogonal sources of complexity in default reasoning: classical (first-order) reasoning and conflict resolution (i.e. choosing an appropriate set of applicable non-conflicting default rules). For classical reasoning well-known techniques exist and conflict resolution presents the new computational

Smodels: A System for Answer Set Programming

by Patrik Simons and Ilkka Niemelä

Computing Research Repository, 2000

The Smodels system implements the stable model se- mantics for normal logic programs. It handles ... more The Smodels system implements the stable model se- mantics for normal logic programs. It handles a sub- class of programs which contain no function symbols and are domain-restricted but supports extensions in- cluding 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

Answer Set Optimization

International Joint Conference on Artificial Intelligence, 2003

We investigate the combination of answer set pro gramming and qualitative optimization technique... more We investigate the combination of answer set pro gramming and qualitative optimization techniques. Answer set optimization programs (ASO pro grams) have two parts. The generating program produces answer sets representing possible solutions. The preference program expresses user preferences. It induces a preference relation on the answer sets of based on the degree to which rules are satisfied. We discuss possible

Nonmonotonic Reasoning: Towards Efficient Calculi and Implementations

ABSTRACT An abstract is not available.

Smodels-an implementation of the stable model and well-founded semantics for normal logic programs

Logic Programming and Non-monotonic Reasoning, 1997

... It is also able to handle two basic query-answering tasks, ie, to decide whether a given lite... more

Smodels — an implementation of the stable model and well-founded semantics for normal logic programs

Lecture Notes in Computer Science, 1997

The Smodels system is a C++ implementation of the wellfounded and stable model semantics for rang... more The Smodels system is a C++ implementation of the wellfounded and stable model semantics for range-restricted function-free normal programs. The system includes two modules: (i) smodels which implements the two semantics for ground programs and (ii) parse which computes a grounded version of a range-restricted function-free normal program. The latter module does not produce the whole set of ground instances of the program but a subset that is su cient in the sense that no stable models are lost. The implementation of the stable model semantics for ground programs is based on bottom-up backtracking search where a powerful pruning method is employed. The pruning method exploits an approximation technique for stable models which is closely related to the well-founded semantics. One of the advantages of this novel technique is that it can be implemented to work in linear space. This makes it possible to apply the stable model semantics also in areas where resulting programs are highly non-strati ed and can possess a large number of stable models. The implementation has been tested extensively and compared with a state of the art implementation of the stable model semantics, the SLG system. In tests involving ground programs it clearly outperforms SLG.

Implementing Ordered Disjunction Using Answer Set Solvers for Normal Programs

Lecture Notes in Computer Science, 2002

Logic programs with ordered disjunction (LPODs) add a new connective to logic programming. This c... more Logic programs with ordered disjunction (LPODs) add a new connective to logic programming. This connective allows us to represent alternative, ranked options for problem solutions in the heads of rules: A×B intuitively means: if possible A, but if A is not possible, then at least B. The semantics of logic programs with ordered disjunction is based on a preference relation on answer sets. In this paper we show how LPODs can be implemented using answer set solvers for normal programs. The implementation is based on a generator which produces candidate answer sets and a tester which checks whether a given candidate is maximally preferred and produces a better candidate if it is not. We also discuss the complexity of reasoning tasks based on LPODs.

Bounded LTL Model Checking with Stable Models

Lecture Notes in Computer Science, 2001

In this paper bounded model checking of asynchronous concurrent systems is introduced as a promis... more 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. KEYWORDS: bounded model checking, stable models, LTL, step semantics * This is an extended version of a paper titled "Bounded LTL Model Checking with Stable Models" presented at the 6th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'2001), Vienna, Austria, September 2001. † The financial support of Academy of Finland (Projects 53695, 47754) and Foundation of Technology (Tekniikan Edistämissäätiö), Helsinki, Finland are gratefully acknowledged. ‡ The financial support of Academy of Finland (Projects 53695, 47754) is gratefully acknowledged.

Towards an Efficient Tableau Method for Boolean Circuit Satisfiability Checking

Lecture Notes in Computer Science, 2000

. Boolean circuits oer a natural, structured, and compactrepresentation of Boolean functions for ... more . Boolean circuits oer a natural, structured, and compactrepresentation of Boolean functions for many application domains. In thispaper a tableau method for solving satisability problems for Boolean circuitsis devised. The method employs a direct cut rule combined with deterministicdeduction rules. Simplication rules for circuits and a searchheuristic attempting to minimize the search space are developed. Experimentsin symbolic model checking domain

Developing a Declarative Rule Language for Applications in Product Configuration

Lecture Notes in Computer Science, 1998

... Abstract. A rule-based language is proposed for product configuration applications. It is equ... more

Logic Programs With Monotone Cardinality Atoms

Lecture Notes in Computer Science, 2003

We investigate mca-programs, that is, logic programs with clauses built of monotone cardinality a... more We investigate mca-programs, that is, logic programs with clauses built of monotone cardinality atoms of the form kX, where k is a non-negative integer and X is a finite set of propositional atoms. We develop a theory of mca-programs. We demonstrate that the operational concept of the one-step provability operator generalizes to mca-programs, but the generalization involves nondeterminism. Our main results show that the formalism of mca-programs is a common generalization of (1) normal logic programming with its semantics of models, supported models and stable models, (2) logic programming with cardinality atoms and with the semantics of stable models, as defined by Niemelä, Simons and Soininen, and (3) of disjunctive logic programming with the possiblemodel semantics of Sakama and Inoue.

Stable Model Semantics of Weight Constraint Rules

Lecture Notes in Computer Science, 1999

Answer Set Programming without Unstratified Negation

Lecture Notes in Computer Science, 2008

The paper argues that for answer set programming purposes it is not necessary to use unstratified... more The paper argues that for answer set programming purposes it is not necessary to use unstratified negation but it is more appropriate to employ a basic language based on simple choice constructs, integrity constraints, and stratified negation. This offers a framework that enables natural problem encodings and smooth extensions, for instance, with constraints and aggregates.

A Module-Based Framework for Multi-language Constraint Modeling

Lecture Notes in Computer Science, 2009

We develop a module-based framework for constraint modeling where it is possible to combine diffe... more We develop a module-based framework for constraint modeling where it is possible to combine different constraint modeling languages and exploit their strengths in a flexible way. In the framework a constraint model consists of modules with clear input/output interfaces. When combining modules, apart from the interface, a module is a black box whose internals are invisible to the outside world. Inside a module a chosen constraint language (approaches such as CP, ASP, SAT, and MIP) can be used. This leads to a clear modular semantics where the overall semantics of the whole constraint model is obtained from the semantics of individual modules. The framework supports multi-language modeling without the need to develop a complicated joint semantics and enables the use of alternative semantical underpinnings such as default negation and classical negation in the same model. Furthermore, computational aspects of the framework are considered and, in particular, possibilities of benefiting from the known module structure in solving constraint models are studied.

Extending the Smodels System with Cardinality and Weight Constraints

Logics in Artificial Intelligence, 2000

The Smodels system is one of the state-of-the-art implementations of stable model computation for... more The Smodels system is one of the state-of-the-art implementations of stable model computation for normal logic programs. In order to enable more realistic applications, the basic modeling language of normal programs has been extended with new constructs including cardinality and weight constraints and corresponding implementation techniques have been developed. This paper summarizes the extensions that have been included in the

Efficient Implementation of the Well-founded and Stable Model Semantics

by Patrik Simons and Ilkka Niemelä

International Conference on Logic Programming/Joint International Conference and Symposium on Logic Programming, 1996

An implementation of the well-founded and stable model semantics for range-restrictedfunction-fre... more An implementation of the well-founded and stable model semantics for range-restrictedfunction-free normal programs is presented. It includes two modules: an algorithm for implementingthe two semantics for ground programs and an algorithm for computing a groundedversion of a range-restricted function-free normal program. The latter algorithm does notproduce the whole set of ground instances of the program but a subset which is

Empirical Testing of a Weight Constraint Rule Based Configurator

In this paper we first describe a configurator imple- mentation based on a practically important ... more In this paper we first describe a configurator imple- mentation based on a practically important subset of a synthesized ontology of configuration knowledge. The underlying configuration modeling language has been provided with a declarative semantics by mapping it to weight constraint rules, a form of logic programs. Three issues important for efficiency of the implementation are addressed: off-line compilation of

A Fixpoint Definition of Dynamic Constraint Satisfaction

Lecture Notes in Computer Science, 1999

Lecture Notes in Computer Science, 1996

Unfolding Partiality and Disjunctions in Stable Model Semantics

by Patrik Simons, Ilkka Niemelä, and Jia-huai You

Principles of Knowledge Representation and Reasoning, 2000

The paper studies an implementation methodology for partial and disjunctive stable models where p... more 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

Evaluating an Algorithm for Default Reasoning

by Patrik Simons and Ilkka Niemelä

In this paper performance of a lately proposed decision procedure for Reiter's default logic ... more In this paper performance of a lately proposed decision procedure for Reiter's default logic is evaluated. Recent complexity results indicate that there are two orthogonal sources of complexity in default reasoning: classical (first-order) reasoning and conflict resolution (i.e. choosing an appropriate set of applicable non-conflicting default rules). For classical reasoning well-known techniques exist and conflict resolution presents the new computational

Smodels: A System for Answer Set Programming

by Patrik Simons and Ilkka Niemelä

Computing Research Repository, 2000

The Smodels system implements the stable model se- mantics for normal logic programs. It handles ... more The Smodels system implements the stable model se- mantics for normal logic programs. It handles a sub- class of programs which contain no function symbols and are domain-restricted but supports extensions in- cluding 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

Answer Set Optimization

International Joint Conference on Artificial Intelligence, 2003

We investigate the combination of answer set pro gramming and qualitative optimization technique... more We investigate the combination of answer set pro gramming and qualitative optimization techniques. Answer set optimization programs (ASO pro grams) have two parts. The generating program produces answer sets representing possible solutions. The preference program expresses user preferences. It induces a preference relation on the answer sets of based on the degree to which rules are satisfied. We discuss possible

Nonmonotonic Reasoning: Towards Efficient Calculi and Implementations

ABSTRACT An abstract is not available.

Smodels-an implementation of the stable model and well-founded semantics for normal logic programs

Logic Programming and Non-monotonic Reasoning, 1997

... It is also able to handle two basic query-answering tasks, ie, to decide whether a given lite... more

Smodels — an implementation of the stable model and well-founded semantics for normal logic programs

Lecture Notes in Computer Science, 1997

The Smodels system is a C++ implementation of the wellfounded and stable model semantics for rang... more The Smodels system is a C++ implementation of the wellfounded and stable model semantics for range-restricted function-free normal programs. The system includes two modules: (i) smodels which implements the two semantics for ground programs and (ii) parse which computes a grounded version of a range-restricted function-free normal program. The latter module does not produce the whole set of ground instances of the program but a subset that is su cient in the sense that no stable models are lost. The implementation of the stable model semantics for ground programs is based on bottom-up backtracking search where a powerful pruning method is employed. The pruning method exploits an approximation technique for stable models which is closely related to the well-founded semantics. One of the advantages of this novel technique is that it can be implemented to work in linear space. This makes it possible to apply the stable model semantics also in areas where resulting programs are highly non-strati ed and can possess a large number of stable models. The implementation has been tested extensively and compared with a state of the art implementation of the stable model semantics, the SLG system. In tests involving ground programs it clearly outperforms SLG.

Implementing Ordered Disjunction Using Answer Set Solvers for Normal Programs

Lecture Notes in Computer Science, 2002

Logic programs with ordered disjunction (LPODs) add a new connective to logic programming. This c... more Logic programs with ordered disjunction (LPODs) add a new connective to logic programming. This connective allows us to represent alternative, ranked options for problem solutions in the heads of rules: A×B intuitively means: if possible A, but if A is not possible, then at least B. The semantics of logic programs with ordered disjunction is based on a preference relation on answer sets. In this paper we show how LPODs can be implemented using answer set solvers for normal programs. The implementation is based on a generator which produces candidate answer sets and a tester which checks whether a given candidate is maximally preferred and produces a better candidate if it is not. We also discuss the complexity of reasoning tasks based on LPODs.

Bounded LTL Model Checking with Stable Models

Lecture Notes in Computer Science, 2001

In this paper bounded model checking of asynchronous concurrent systems is introduced as a promis... more 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. KEYWORDS: bounded model checking, stable models, LTL, step semantics * This is an extended version of a paper titled "Bounded LTL Model Checking with Stable Models" presented at the 6th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'2001), Vienna, Austria, September 2001. † The financial support of Academy of Finland (Projects 53695, 47754) and Foundation of Technology (Tekniikan Edistämissäätiö), Helsinki, Finland are gratefully acknowledged. ‡ The financial support of Academy of Finland (Projects 53695, 47754) is gratefully acknowledged.

Towards an Efficient Tableau Method for Boolean Circuit Satisfiability Checking

Lecture Notes in Computer Science, 2000

. Boolean circuits oer a natural, structured, and compactrepresentation of Boolean functions for ... more . Boolean circuits oer a natural, structured, and compactrepresentation of Boolean functions for many application domains. In thispaper a tableau method for solving satisability problems for Boolean circuitsis devised. The method employs a direct cut rule combined with deterministicdeduction rules. Simplication rules for circuits and a searchheuristic attempting to minimize the search space are developed. Experimentsin symbolic model checking domain

Developing a Declarative Rule Language for Applications in Product Configuration

Lecture Notes in Computer Science, 1998

... Abstract. A rule-based language is proposed for product configuration applications. It is equ... more

Logic Programs With Monotone Cardinality Atoms

Lecture Notes in Computer Science, 2003

We investigate mca-programs, that is, logic programs with clauses built of monotone cardinality a... more We investigate mca-programs, that is, logic programs with clauses built of monotone cardinality atoms of the form kX, where k is a non-negative integer and X is a finite set of propositional atoms. We develop a theory of mca-programs. We demonstrate that the operational concept of the one-step provability operator generalizes to mca-programs, but the generalization involves nondeterminism. Our main results show that the formalism of mca-programs is a common generalization of (1) normal logic programming with its semantics of models, supported models and stable models, (2) logic programming with cardinality atoms and with the semantics of stable models, as defined by Niemelä, Simons and Soininen, and (3) of disjunctive logic programming with the possiblemodel semantics of Sakama and Inoue.

Stable Model Semantics of Weight Constraint Rules

Lecture Notes in Computer Science, 1999

Answer Set Programming without Unstratified Negation

Lecture Notes in Computer Science, 2008

The paper argues that for answer set programming purposes it is not necessary to use unstratified... more The paper argues that for answer set programming purposes it is not necessary to use unstratified negation but it is more appropriate to employ a basic language based on simple choice constructs, integrity constraints, and stratified negation. This offers a framework that enables natural problem encodings and smooth extensions, for instance, with constraints and aggregates.

A Module-Based Framework for Multi-language Constraint Modeling

Lecture Notes in Computer Science, 2009

We develop a module-based framework for constraint modeling where it is possible to combine diffe... more We develop a module-based framework for constraint modeling where it is possible to combine different constraint modeling languages and exploit their strengths in a flexible way. In the framework a constraint model consists of modules with clear input/output interfaces. When combining modules, apart from the interface, a module is a black box whose internals are invisible to the outside world. Inside a module a chosen constraint language (approaches such as CP, ASP, SAT, and MIP) can be used. This leads to a clear modular semantics where the overall semantics of the whole constraint model is obtained from the semantics of individual modules. The framework supports multi-language modeling without the need to develop a complicated joint semantics and enables the use of alternative semantical underpinnings such as default negation and classical negation in the same model. Furthermore, computational aspects of the framework are considered and, in particular, possibilities of benefiting from the known module structure in solving constraint models are studied.

Extending the Smodels System with Cardinality and Weight Constraints

Logics in Artificial Intelligence, 2000

The Smodels system is one of the state-of-the-art implementations of stable model computation for... more The Smodels system is one of the state-of-the-art implementations of stable model computation for normal logic programs. In order to enable more realistic applications, the basic modeling language of normal programs has been extended with new constructs including cardinality and weight constraints and corresponding implementation techniques have been developed. This paper summarizes the extensions that have been included in the

Efficient Implementation of the Well-founded and Stable Model Semantics

by Patrik Simons and Ilkka Niemelä

International Conference on Logic Programming/Joint International Conference and Symposium on Logic Programming, 1996

An implementation of the well-founded and stable model semantics for range-restrictedfunction-fre... more An implementation of the well-founded and stable model semantics for range-restrictedfunction-free normal programs is presented. It includes two modules: an algorithm for implementingthe two semantics for ground programs and an algorithm for computing a groundedversion of a range-restricted function-free normal program. The latter algorithm does notproduce the whole set of ground instances of the program but a subset which is

Empirical Testing of a Weight Constraint Rule Based Configurator

In this paper we first describe a configurator imple- mentation based on a practically important ... more In this paper we first describe a configurator imple- mentation based on a practically important subset of a synthesized ontology of configuration knowledge. The underlying configuration modeling language has been provided with a declarative semantics by mapping it to weight constraint rules, a form of logic programs. Three issues important for efficiency of the implementation are addressed: off-line compilation of

A Fixpoint Definition of Dynamic Constraint Satisfaction

Lecture Notes in Computer Science, 1999

Lecture Notes in Computer Science, 1996

Unfolding Partiality and Disjunctions in Stable Model Semantics

by Patrik Simons, Ilkka Niemelä, and Jia-huai You

Principles of Knowledge Representation and Reasoning, 2000

The paper studies an implementation methodology for partial and disjunctive stable models where p... more 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