Vitor Nogueira

Using a logic programming language with persistence and contexts

This article merges two approaches: one dealing with persistence for logic programs, as provided by a relational database back-end and another which addresses the issues of logic program structuring, by way of the parametric context. We claim that these two can be effectively combined to obtain a language which offers significant gains in expressiveness over previous work. This claim is experimentally backed by the applications that have been developed using these tools.

Work out the semantic web search: the cooperative way

We propose a Cooperative Question Answering System that takes as input natural language queries and is able to return a cooperative answer based on semantic web resources, more specifically DBpedia represented in OWL/RDF as knowledge base and WordNet to build similar questions. Our system resorts to ontologies not only for reasoning but also to find answers and is independent of prior knowledge of the semantic resources by the user. The natural language question is translated into its semantic representation and then answered by consulting the semantics sources of information. The system is able to clarify the problems of ambiguity and helps finding the path to the correct answer. If there are multiple answers to the question posed (or to the similar questions for which DBpedia contains answers), they will be grouped according to their semantic meaning, providing a more cooperative and clarified answer to the user.

Temporal Contextual Logic Programming

The importance of temporal representation and reasoning is well known not only in the database community but also in the artificial intelligence one. Contextual Logic Programming [Antonio Porto and Luis Monteiro. Contextual logic programming. In Giorgio Levi and Maurizio Martelli, editors, Proceedings 6th Intl. Conference on Logic Programming, Lisbon, Portugal, 19-23 June 1989, pages 284-299. The MIT Press, Cambridge, MA, 1989] (CxLP) is a simple and powerful language that extends logic programming with mechanisms for modularity. Recent work not only presented a revised specification of CxLP together with a new implementation for it but also explained how this language could be seen as a shift into the Object-Oriented Programming paradigm [Salvador Abreu and Daniel Diaz. Objective: In minimum context. In Catuscia Palamidessi, editor, ICLP, volume 2916 of Lecture Notes in Computer Science, pages 128-147. Springer, 2003]. In this paper we propose a temporal extension of such language called Temporal Contextual Logic Programming. Such extension follows a reified approach to the temporal qualification, that besides the acknowledge increased expressiveness of reification allows us to capture the notion of time of the context. Together with the syntax of this language we also present its operational semantics and an application to the management of workflows.

Towards structured contexts and modules

Contextual Logic Programming was proposed by Monteiro and Porto [4] as a means of bringing modularity to the Prolog language. It was improved upon as a practical extension in a high performance Prolog system by Abreu and Diaz [1], providing a program structuring mechanism as well as fulfilling some of Prolog’s shortcomings when used for programming in-the-large, namely by enabling an object-oriented programming style without relinquishing the expressiveness and semantic robustness of Logic Programs.

A temporal programming language for heterogeneous information systems

In the last decades the number of information centers that receive data from different origins or technologies has increased enormously. Representing and reasoning with temporal data is an important issue in the majority of those centers. Nevertheless, most of the solutions to solve such issue are rather limited and specific to a certain domain.

Probabilistic Perception Revision in AgentSpeak(L)

Agent programming is mostly a symbolic discipline and, as such, draws little benefits from probabilistic areas as machine learning and graphical models. However, the greatest objective of agent research is the achievement of autonomy in dynamical and complex environments — a goal that implies embracing uncertainty and therefore the entailed representations, algorithms and techniques. This paper proposes an innovative and conflict free two layer approach to agent programming that uses already established methods and tools from both symbolic and probabilistic artificial intelligence. Moreover, this method is illustrated by means of a widely used agent programming example, GoldMiners.

ISTO: a language for temporal organizational information systems

In this paper we propose to extend the logical framework ISCO (Information System COnstruction language) with an expressive means of representing and implicitly using temporal information. Moreover, we also provide a compilation scheme that targets a logic language with modularity and temporal reasoning.

Integrating Temporal Annotations in a Modular Logic Language

Albeit temporal reasoning and modularity are very prolific fields of research in Logic Programming (LP) we find few examples of their integration. Moreover, in those examples, time and modularity are considered orthogonal to each other. In this paper we propose the addition of temporal annotations to a modular extension of LP such that the usage of a module is influenced by temporal conditions. Besides illustrative examples we also provide an operational semantics together with a compiler, allowing this way for the development of applications based on such language.

Modularity and Temporal Reasoning: A Logic Programming Approach

Albeit temporal reasoning and modularity are very prolific fields of research in logic programming (LP), we find few examples of their integration. In this paper we propose the addition of temporal annotations to a modular extension of LP. Moreover, we also provide a sketch for a compiler, allowing this way for the development of applications based on such language.