DI_PhD http://hdl.handle.net/10362/1045 2013-05-20T08:51:11Z 2013-05-20T08:51:11Z Salinas, Edward Mauricio Alférez http://hdl.handle.net/10362/9370 2013-05-08T13:27:49Z 2012-01-01T00:00:00Z

Title: Derivation and consistency checking of models in early software product line engineering Authors: Salinas, Edward Mauricio Alférez Abstract: Software Product Line Engineering (SPLE) should offer the ability to express the derivation of product-specific assets, while checking for their consistency. The derivation of product-specific assets is possible using general-purpose programming languages in combination with techniques such as conditional compilation and code generation. On the other hand, consistency checking can be achieved through consistency rules in the form of architectural and design guidelines, programming conventions and well-formedness rules. Current approaches present four shortcomings: (1) focus on code derivation only, (2) ignore consistency problems between the variability model and other complementary specification models used in early SPLE, (3) force developers to learn new, difficult to master, languages to encode the derivation of assets, and (4) offer no tool support. This dissertation presents solutions that contribute to tackle these four shortcomings. These solutions are integrated in the approach Derivation and Consistency Checking of models in early SPLE (DCC4SPL) and its corresponding tool support. The two main components of our approach are the Variability Modelling Language for Requirements(VML4RE), a domain-specific language and derivation infrastructure, and the Variability Consistency Checker (VCC), a verification technique and tool. We validate DCC4SPL demonstrating that it is appropriate to find inconsistencies in early SPL model-based specifications and to specify the derivation of product-specific models. Description: Dissertação para obtenção do Grau de Doutor em Engenharia Informática

2012-01-01T00:00:00Z Han, The Anh http://hdl.handle.net/10362/8784 2013-02-11T15:07:51Z 2012-01-01T00:00:00Z

Title: Intention recognition, commitment and their roles in the evolution of cooperation Authors: Han, The Anh Abstract: The goal of this thesis is twofold. First, intention recognition is studied from an Arti cial Intelligence (AI) modeling perspective. We present a novel and e cient intention recognition method that possesses several important features: (i) The method is context-dependent and incremental, enabled by incrementally constructing a three-layer Bayesian network model as more actions are observed, and in a context-dependent manner, relying on a logic programming knowledge base concerning the context; (ii) The Bayesian network is composed from a knowledge base of readily speci ed and readily maintained Bayesian network fragments with simple structures, enabling an e cient acquisition of the corresponding knowledge base (either from domain experts or else automatically from a plan corpus); and, (iii) The method addresses the issue of intention change and abandonment, and can appropriately resolve the issue of multiple intentions recognition. Several aspects of the method are evaluated experimentally, achieving some de nite success. Furthermore, on top of the intention recognition method, a novel framework for intention-based decision making is provided, illustrating several ways in which an ability to recognize intentions of others can enhance a decision making process. A second subgoal of the thesis concerns that, whereas intention recognition has been extensively studied in small scale interactive settings, there is a major shortage of modeling research with respect to large scale social contexts, namely evolutionary roles and aspects of intention recognition. Employing our intention recognition method and the tools of evolutionary game theory, this thesis explicitly addresses the roles played by intention recognition in the nal outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity for assessing intentions of others in the course of social dilemmas, we show how intention recognition is selected by natural selection, opening a window of opportunity for cooperation to thrive, even in hard cooperation prone games like the Prisoner's Dilemma. In addition, there are cases where it is di cult, if not impossible, to recognize the intentions of another agent. In such cases, the strategy of proposing commitment, or of intention manifestation, can help to impose or clarify the intentions of others. Again using the tools of evolutionary game theory, we show that a simple form of commitment can lead to the emergence of cooperation; furthermore, the combination of commitment with intention recognition leads to a strategy better than either one by itself. How the thesis should be read? We recommend that the thesis be read sequentially, chapter by chapter [1-2-3-4-5-6-7-8]. However, for those more interested in intention recognition from the AI modeling perspective, i.e. the rst subgoal of the thesis, Chapters 6 and 7 can be omitted and Chapters 4 and 5 are optional [1-2-3-(4)-(5)-8]. In addition, for those more keen on the problem of the evolution of cooperation, i.e. the second subgoal of thesis, Chapter 3 and even Chapter 2, can be omitted [1-(2)-4-5-6-7-8]. Description: Dissertação para obtenção do Grau de Doutor em Informática

2012-01-01T00:00:00Z Carvalho, Elsa Cristina Batista Bento http://hdl.handle.net/10362/8603 2013-01-25T11:03:53Z 2012-01-01T00:00:00Z

Title: Probabilistic constraint reasoning Authors: Carvalho, Elsa Cristina Batista Bento Abstract: The continuous constraint paradigm has been often used to model safe reasoning in applications where uncertainty arises. Constraint propagation propagates intervals of uncertainty among the variables of the problem, eliminating values that do not belong to any solution. However, constraint programming is very conservative: if initial intervals are wide (reflecting large uncertainty), the obtained safe enclosure of all consistent scenarios may be inadequately wide for decision support. Since all scenarios are considered equally likely, insufficient pruning leads to great inefficiency if some costly decisions may be justified by very unlikely scenarios. Even when probabilistic information is available for the variables of the problem, the continuous constraint paradigm is unable to incorporate and reason with such information. Therefore, it is incapable of distinguishing between different scenarios, based on their likelihoods. This thesis presents a probabilistic continuous constraint paradigm that associates a probabilistic space to the variables of the problem, enabling probabilistic reasoning to complement the underlying constraint reasoning. Such reasoning is used to address probabilistic queries and requires the computation of multi-dimensional integrals on possibly non linear integration regions. Suitable algorithms for such queries are developed, using safe or approximate integration techniques and relying on methods from continuous constraint programming in order to compute safe covers of the integration region. The thesis illustrates the adequacy of the probabilistic continuous constraint framework for decision support in nonlinear continuous problems with uncertain information, namely on inverse and reliability problems, two different types of engineering problems where the developed framework is particularly adequate to support decision makers. Description: Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

2012-01-01T00:00:00Z Knorr, Matthias http://hdl.handle.net/10362/6702 2012-01-12T10:40:30Z 2011-01-01T00:00:00Z

Title: Combining open and closed world reasoning for the semantic web Authors: Knorr, Matthias Abstract: One important problem in the ongoing standardization of knowledge representation languages for the Semantic Web is combining open world ontology languages, such as the OWL-based ones, and closed world rule-based languages. The main difficulty of such a combination is that both formalisms are quite orthogonal w.r.t. expressiveness and how decidability is achieved. Combining non-monotonic rules and ontologies is thus a challenging task that requires careful balancing between expressiveness of the knowledge representation language and the computational complexity of reasoning. In this thesis, we will argue in favor of a combination of ontologies and nonmonotonic rules that tightly integrates the two formalisms involved, that has a computational complexity that is as low as possible, and that allows us to query for information instead of calculating the whole model. As our starting point we choose the mature approach of hybrid MKNF knowledge bases, which is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. We extend the two-valued framework of MKNF logics to a three-valued logics, and we propose a well-founded semantics for non-disjunctive hybrid MKNF knowledge bases. This new semantics promises to provide better efficiency of reasoning,and it is faithful w.r.t. the original two-valued MKNF semantics and compatible with both the OWL-based semantics and the traditional Well- Founded Semantics for logic programs. We provide an algorithm based on operators to compute the unique model, and we extend SLG resolution with tabling to a general framework that allows us to query a combination of non-monotonic rules and any given ontology language. Finally, we investigate concrete instances of that procedure w.r.t. three tractable ontology languages, namely the three description logics underlying the OWL 2 pro les. Description: Dissertação para obtenção do Grau de Doutor em Informática

2011-01-01T00:00:00Z