Building and Databases: the SEED Experience

We describe the database requirements of SEED (Software Environment to Support the Early Phases in Building Design). The requirements are typical for a database that intends to support a heterogeneous design support environment consisting of independent software modules with diverse internal design models, requirements not met by any commercial database system. The design and implementation of this database is an integral part of the overall software engineering effort. We describe the SEED approach that integrates external and in-house software based on a shared information model specified in the modeling language SPROUT, which allows for the specification of domains, and classes, relationship types and their behavior, and multiple classifications. The SPROUT run-time system organizes and coordinates the communication between the software modules and the databas

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Constraint solving over multi-valued logics - application to digital circuits

Due to usage conditions, hazardous environments or intentional causes, physical and virtual systems are subject to faults in their components, which may affect their overall behaviour. In a ‘black-box’ agent modelled by a set of propositional logic rules, in which just a subset of components is externally visible, such faults may only be recognised by examining some output function of the agent. A (fault-free) model of the agent’s system provides the expected output given some input. If the real output differs from that predicted output, then the system is faulty. However, some faults may only become apparent in the system output when appropriate inputs are given. A number of problems regarding both testing and diagnosis thus arise, such as testing a fault, testing the whole system, finding possible faults and differentiating them to locate the correct one. The corresponding optimisation problems of finding solutions that require minimum resources are also very relevant in industry, as is minimal diagnosis. In this dissertation we use a well established set of benchmark circuits to address such diagnostic related problems and propose and develop models with different logics that we formalise and generalise as much as possible. We also prove that all techniques generalise to agents and to multiple faults. The developed multi-valued logics extend the usual Boolean logic (suitable for faultfree models) by encoding values with some dependency (usually on faults). Such logics thus allow modelling an arbitrary number of diagnostic theories. Each problem is subsequently solved with CLP solvers that we implement and discuss, together with a new efficient search technique that we present. We compare our results with other approaches such as SAT (that require substantial duplication of circuits), showing the effectiveness of constraints over multi-valued logics, and also the adequacy of a general set constraint solver (with special inferences over set functions such as cardinality) on other problems. In addition, for an optimisation problem, we integrate local search with a constructive approach (branch-and-bound) using a variety of logics to improve an existing efficient tool based on SAT and ILP

Semantic Feature Construction

An effective set of features is integral to the success of machine learning algorithms. Semantic feature construction is the knowledge-driven manipulation of the propositional descriptor space of a set of examples for use in a learning algorithm. Two important sources of semanticsfor feature construction are the semantic type (and associated semantic properties) and the semantic class of features. These semantics canbe captured in a knowledge base and utilized to constrain search through the space of constructed features. This dissertation presents a systemthat captures semantic feature construction knowledge and implements a search algorithm that respects that knowledge. Results are presentedfor different combinations of features generated from different successor functions used in search. These results are compiled over many learning problems and several learning algorithms. Other results are also presentedfor different levels of detail in semantic knowledge. Generally, semantics are an effective guide in the space of constructed features

Transforming OCL to PVS: Using Theorem Proving Support for Analysing Model Constraints

The Unified Modelling Language (UML) is a de facto standard language for describing software systems. UML models are often supplemented with Object Constraint Language (OCL) constraints, to capture detailed properties of components and systems. Sophisticated tools exist for analysing UML models, e.g., to check that well-formedness rules have been satisfied. As well, tools are becoming available to analyse and reason about OCL constraints. Previous work has been done on analysing OCL constraints by translating them to formal languages and then analysing the translated constraints with tools such as theorem provers. This project contributes a transformation from OCL to the specification language of the Prototype Verification System (PVS). PVS can be used to analyse and reason about translated OCL constraints. A particular novelty of this project is that it carries out the transformation of OCL to PVS by using model transformation, as exemplified by the OMG's Model-Driven Architecture. The project implements and automates model transformations from OCL to PVS using the Epsilon Transformation Language (ETL) and tests the results using the Epsilon Comparison Language (ECL )

Refactoring for parameterizing Java classes

Type safety and expressiveness of many existing Java libraries and theirclient applications would improve, if the libraries were upgraded to definegeneric classes. Efficient and accurate tools exist to assist clientapplications to use generics libraries, but so far the libraries themselvesmust be parameterized manually, which is a tedious, time-consuming, anderror-prone task. We present a type-constraint-based algorithm forconverting non-generic libraries to add type parameters. The algorithmhandles the full Java language and preserves backward compatibility, thusmaking it safe for existing clients. Among other features, it is capableof inferring wildcard types and introducing type parameters formutually-dependent classes. We have implemented the algorithm as a fullyautomatic refactoring in Eclipse.We evaluated our work in two ways. First, our tool parameterized code thatwas lacking type parameters. We contacted the developers of several ofthese applications, and in all cases where we received a response, theyconfirmed that the resulting parameterizations were correct and useful.Second, to better quantify its effectiveness, our tool parameterizedclasses from already-generic libraries, and we compared the results tothose that were created by the libraries' authors. Our tool performed therefactoring accurately -- in 87% of cases the results were as good as thosecreated manually by a human expert, in 9% of cases the tool results werebetter, and in 4% of cases the tool results were worse