Neural-Symbolic Learning and Reasoning: Contributions and Challenges

The goal of neural-symbolic computation is to integrate robust connectionist learning and sound symbolic reasoning. With the recent advances in connectionist learning, in particular deep neural networks, forms of representation learning have emerged. However, such representations have not become useful for reasoning. Results from neural-symbolic computation have shown to offer powerful alternatives for knowledge representation, learning and reasoning in neural computation. This paper recalls the main contributions and discusses key challenges for neural-symbolic integration which have been identified at a recent Dagstuhl seminar

Call subsumption mechanisms for tabled logic programs

Estágio realizado na ... e orientado pelo Eng.ºDocumento confidencial. Não pode ser disponibilizado para consultaTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

A unifying approach for queries and updates in deductive databases

This dissertation presents a unifying approach to process (recursive) queries and updates in a deductive database. To improve query performance, a combined top-down and bottom-up evaluation method is used to compile rules into iterative programs that contain relational algebra operators. This method is based on the lemma resolution that retains previous results to guarantee termination.Due to locality in database processing, it is desirable to materialize frequently used queries against views of the database. Unfortunately, if updates are allowed, maintaining materialized view tables becomes a major problem. We propose to materialize views incrementally, as queries are being answered. Hence views in our approach are only partially materialized. For such views, we design algorithms to perform updates only when the underlying view tables are actually affected.We compare our approach to two conventional methods for dealing with views: total materialization and query-modification. The first method materializes the entire view when it is defined while the second recomputes the view on the fly without maintaining any physical view tables. We demonstrate that our approach is a compromise between these two methods and performs better than either one in many situations.It is also desirable to be able to update views just like updating base tables. However, view updates are inherently ambiguous and the semantics of update propagation on recursively defined views were not well understood in the past. Using dynamic logic programming and lemma resolution, we are able to define the semantics of recursive view updates. These are expressed in the form of update translators specified by the database administrator when the view is defined. To guarantee completeness, we identify a subset of safe update translators. We prove that this subset of translators always terminate and are complete

Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming

Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform representation language for the problem specification and induced theories. ILP has been successfully applied to many real-world problems, especially in the biological domain (e.g. drug design, protein structure prediction), where relational information is of particular importance. The expressiveness of logic programs grants flexibility in specifying the learning task and understandability to the induced theories. However, this flexibility comes at a high computational cost, constraining the applicability of ILP systems. Constructing and evaluating complex concepts remain two of the main issues that prevent ILP systems from tackling many learning problems. These learning problems are interesting both from a research perspective, as they raise the standards for ILP systems, and from an application perspective, where these target concepts naturally occur in many real-world applications. Such complex concepts cannot be constructed or evaluated by parallelizing existing top-down ILP systems or improving the underlying Prolog engine. Novel search strategies and cover algorithms are needed. The main focus of this thesis is on how to efficiently construct and evaluate complex hypotheses in an ILP setting. In order to construct such hypotheses we investigate two approaches. The first, the Top Directed Hypothesis Derivation framework, implemented in the ILP system TopLog, involves the use of a top theory to constrain the hypothesis space. In the second approach we revisit the bottom-up search strategy of Golem, lifting its restriction on determinate clauses which had rendered Golem inapplicable to many key areas. These developments led to the bottom-up ILP system ProGolem. A challenge that arises with a bottom-up approach is the coverage computation of long, non-determinate, clauses. Prolog’s SLD-resolution is no longer adequate. We developed a new, Prolog-based, theta-subsumption engine which is significantly more efficient than SLD-resolution in computing the coverage of such complex clauses. We provide evidence that ProGolem achieves the goal of learning complex concepts by presenting a protein-hexose binding prediction application. The theory ProGolem induced has a statistically significant better predictive accuracy than that of other learners. More importantly, the biological insights ProGolem’s theory provided were judged by domain experts to be relevant and, in some cases, novel

Game engines and MAS: tuplespace-based interaction in Unity3D

I Game Engines stanno acquisendo sempre più importanza sia in ambito industriale, dove permettono lo sviluppo di applicazioni moderne e videogiochi, sia in ambito di ricerca, in particolare nel contesto dei sistemi multi-agente (MAS). La loro capacità espressiva, unita al supporto di tecnologie e funzionalità innovative, permette la creazione di sistemi moderni e complessi in maniera più efficiente: il loro continuo avanzamento tecnologico li ha portati ad essere una realtà su cui fare affidamento nella produzione di vari applicativi diversi, come applicazioni di realtà aumentata/virtuale/mista, simulazioni immersive, costruzione di mondi virtuali e 3D, ecc. Ciononostante, soffrono la mancanza di proprie astrazioni e meccanismi che possano essere affidabili e utilizzati per aggredire la complessità durante il design di sistemi complessi. Il tentativo di sfruttare le caratteristiche della teoria dei MAS all'interno degli ambienti di sviluppo dei Game Engines procede secondo questa direzione: integrando le astrazioni costituenti i MAS all'interno dei Game Engines, con particolare riferimento ai modelli di coordinazione tra agenti, può portare a nuove soluzioni, riuscendo a risolvere problemi tecnologici grazie all'aiuto degli engine grafici. Questa tesi utilizza il Game Engine Unity3D proponendo due librerie C#, le quali sfruttano una precedente integrazione dello stesso framework con il Prolog per l'abilitazione di un modello di interazione e coordinazione basato su spazi di tuple, utilizzabile tramite l'implementazione di primitive LINDA. Le librerie offrono interfacce di programmazione (API) sfruttabili dai programmatori C# Unity3D per integrare nelle loro creazioni il supporto a tale modello, con una nuova modalità per la gestione della coordinazione tra oggetti in Unity3D e fornisce importanti proprietà, essendo fondamentale nel contesto dei MAS dal punto di vista dell'ingegnerizzazione di sistemi complessi e della gestione delle interazioni tra agenti

Towards an aspect weaving BPEL engine

This position paper proposes the use of dynamic aspects and the visitor design pattern to obtain a highly configurable and extensible BPEL engine. Using these two techniques, the core of this infrastructural software can be customised to meet new requirements and add features such as debugging, execution monitoring, or changing to another Web Service selection policy. Additionally, it can easily be extended to cope with customer-specific BPEL extensions. We propose the use of dynamic aspects not only on the engine itself but also on the workflow in order to tackle the problems of Web Service hot deployment and hot fixes to long running processes. In this way, composing aWeb Service "on-the-fly" means weaving its choreography interface into the workflow