Rosella Gennari | Free University of Bozen-Bolzano (original) (raw)
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Papers by Rosella Gennari
Abstract. We use a strong form of the tree model property to boost the performance of resolution-... more Abstract. We use a strong form of the tree model property to boost the performance of resolution-based first-order theorem provers on the so-called relational translations of modal formulas. We provide both the mathematical underpinnings and experimental results con- ...
We provide here an extension of a general framework introduced in [Apt99b,Apt99c] that allows to ... more We provide here an extension of a general framework introduced in [Apt99b,Apt99c] that allows to explain several local consistency algorithms in a systematic way. In this framework we proceed in two steps. First, we introduce a generic iteration algorithm on partial orderings and ...
ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 2014
Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter - CHItaly 2015, 2015
Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter - CHItaly 2015, 2015
Journal of Data and Information Quality, 2015
Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming, 2000
Soft constraints based on semirings are a generalization of classical constraints, where tuples o... more Soft constraints based on semirings are a generalization of classical constraints, where tuples of variables' values in each soft constraint are uniquely associated to elements from an algebraic structure called semiring. This framework is able to express, for example, fuzzy, classical, weighted, valued and over-constrained constraint problems. Classical constraint propagation has been extended and adapted to soft constraints by de ning a schema for soft local consistency BMR97]. On the other hand, in Apt99a,Apt99b] it has been proved that most of the well known constraint propagation algorithms for classical constraints can be cast within a single schema. In this paper we combine these two schema and we show how the framework of Apt99a,Apt99b] can be used for soft constraints. In doing so, we generalize the concept of soft local consistency, and we prove some convenient properties about its termination.
Lecture Notes in Computer Science, 2001
There are various formalizations of soft constraints in the literature; so far, we have analyzed ... more There are various formalizations of soft constraints in the literature; so far, we have analyzed the semiring-based approach of [BMR97], the fuzzy ones in [Rut94] and the Max-CSP's from [FW92]. If we abstract the common features from those frameworks, we can define soft ...
Lecture Notes in Computer Science, 2000
Abstract. We provide here an extension of a general framework in-troduced in [Apt99b,Apt99c] that... more Abstract. We provide here an extension of a general framework in-troduced in [Apt99b,Apt99c] that allows to explain several local consis-tency algorithms in a systematic way. In this framework we proceed in two steps. First, we introduce a generic iteration ...
Automated temporal planning requires coping with uncontrollable actions. This is particularly tru... more Automated temporal planning requires coping with uncontrollable actions. This is particularly true of the spacecraft application domain. Therein, there is an ongoing effort towards the definition of representation and reasoning frameworks for finding a plan robust to the uncontrollability and inherent uncertainty of the domain, based either on constraint satisfaction or game theoretical approaches. In this position paper, we first analyse and summarise key notions from the different approaches using a unifying notation. Then we pinpoint questions emerging from our analysis.
Contents1 Introduction 61.1 Temporal Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . ... more Contents1 Introduction 61.1 Temporal Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . 61.2 Constraint Programming . . . . . . . . . . . . . . . . . . . . . . . 71.2.1 Constraint problems and constraint satisfaction . . . . . . 71.2.2 Algorithms to solve constraints . . . . . . . . . . . . . . . 91.3 Temporal reasoning and ConstraintProgramming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.3.1 Temporal Reasoning with metric information . . . . . . . 141.3.2...
Abstract. We use a strong form of the tree model property to boost the performance of resolution-... more Abstract. We use a strong form of the tree model property to boost the performance of resolution-based first-order theorem provers on the so-called relational translations of modal formulas. We provide both the mathematical underpinnings and experimental results con- ...
We provide here an extension of a general framework introduced in [Apt99b,Apt99c] that allows to ... more We provide here an extension of a general framework introduced in [Apt99b,Apt99c] that allows to explain several local consistency algorithms in a systematic way. In this framework we proceed in two steps. First, we introduce a generic iteration algorithm on partial orderings and ...
ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 2014
Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter - CHItaly 2015, 2015
Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter - CHItaly 2015, 2015
Journal of Data and Information Quality, 2015
Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming, 2000
Soft constraints based on semirings are a generalization of classical constraints, where tuples o... more Soft constraints based on semirings are a generalization of classical constraints, where tuples of variables' values in each soft constraint are uniquely associated to elements from an algebraic structure called semiring. This framework is able to express, for example, fuzzy, classical, weighted, valued and over-constrained constraint problems. Classical constraint propagation has been extended and adapted to soft constraints by de ning a schema for soft local consistency BMR97]. On the other hand, in Apt99a,Apt99b] it has been proved that most of the well known constraint propagation algorithms for classical constraints can be cast within a single schema. In this paper we combine these two schema and we show how the framework of Apt99a,Apt99b] can be used for soft constraints. In doing so, we generalize the concept of soft local consistency, and we prove some convenient properties about its termination.
Lecture Notes in Computer Science, 2001
There are various formalizations of soft constraints in the literature; so far, we have analyzed ... more There are various formalizations of soft constraints in the literature; so far, we have analyzed the semiring-based approach of [BMR97], the fuzzy ones in [Rut94] and the Max-CSP's from [FW92]. If we abstract the common features from those frameworks, we can define soft ...
Lecture Notes in Computer Science, 2000
Abstract. We provide here an extension of a general framework in-troduced in [Apt99b,Apt99c] that... more Abstract. We provide here an extension of a general framework in-troduced in [Apt99b,Apt99c] that allows to explain several local consis-tency algorithms in a systematic way. In this framework we proceed in two steps. First, we introduce a generic iteration ...
Automated temporal planning requires coping with uncontrollable actions. This is particularly tru... more Automated temporal planning requires coping with uncontrollable actions. This is particularly true of the spacecraft application domain. Therein, there is an ongoing effort towards the definition of representation and reasoning frameworks for finding a plan robust to the uncontrollability and inherent uncertainty of the domain, based either on constraint satisfaction or game theoretical approaches. In this position paper, we first analyse and summarise key notions from the different approaches using a unifying notation. Then we pinpoint questions emerging from our analysis.
Contents1 Introduction 61.1 Temporal Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . ... more Contents1 Introduction 61.1 Temporal Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . 61.2 Constraint Programming . . . . . . . . . . . . . . . . . . . . . . . 71.2.1 Constraint problems and constraint satisfaction . . . . . . 71.2.2 Algorithms to solve constraints . . . . . . . . . . . . . . . 91.3 Temporal reasoning and ConstraintProgramming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.3.1 Temporal Reasoning with metric information . . . . . . . 141.3.2...