A Time Leap Challenge for SAT Solving

We compare the impact of hardware advancement and algorithm advancement for SAT solving over the last two decades. In particular, we compare 20-year-old SAT-solvers on new computer hardware with modern SAT-solvers on 20-year-old hardware. Our findings show that the progress on the algorithmic side has at least as much impact as the progress on the hardware side.Comment: Authors' version of a paper which is to appear in the proceedings of CP'202

Semantic Management of Location-Based Services in Wireless Environments

En los últimos años el interés por la computación móvil ha crecido debido al incesante uso de dispositivos móviles (por ejemplo, smartphones y tablets) y su ubicuidad. El bajo coste de dichos dispositivos unido al gran número de sensores y mecanismos de comunicación que equipan, hace posible el desarrollo de sistemas de información útiles para sus usuarios. Utilizando un cierto tipo especial de sensores, los mecanismos de posicionamiento, es posible desarrollar Servicios Basados en la Localización (Location-Based Services o LBS en inglés) que ofrecen un valor añadido al considerar la localización de los usuarios de dispositivos móviles para ofrecerles información personalizada. Por ejemplo, se han presentado numerosos LBS entre los que se encuentran servicios para encontrar taxis, detectar amigos en las cercanías, ayudar a la extinción de incendios, obtener fotos e información de los alrededores, etc. Sin embargo, los LBS actuales están diseñados para escenarios y objetivos específicos y, por lo tanto, están basados en esquemas predefinidos para el modelado de los elementos involucrados en estos escenarios. Además, el conocimiento del contexto que manejan es implícito; razón por la cual solamente funcionan para un objetivo específico. Por ejemplo, en la actualidad un usuario que llega a una ciudad tiene que conocer (y comprender) qué LBS podrían darle información acerca de medios de transporte específicos en dicha ciudad y estos servicios no son generalmente reutilizables en otras ciudades. Se han propuesto en la literatura algunas soluciones ad hoc para ofrecer LBS a usuarios pero no existe una solución general y flexible que pueda ser aplicada a muchos escenarios diferentes. Desarrollar tal sistema general simplemente uniendo LBS existentes no es sencillo ya que es un desafío diseñar un framework común que permita manejar conocimiento obtenido de datos enviados por objetos heterogéneos (incluyendo datos textuales, multimedia, sensoriales, etc.) y considerar situaciones en las que el sistema tiene que adaptarse a contextos donde el conocimiento cambia dinámicamente y en los que los dispositivos pueden usar diferentes tecnologías de comunicación (red fija, inalámbrica, etc.). Nuestra propuesta en la presente tesis es el sistema SHERLOCK (System for Heterogeneous mobilE Requests by Leveraging Ontological and Contextual Knowledge) que presenta una arquitectura general y flexible para ofrecer a los usuarios LBS que puedan serles interesantes. SHERLOCK se basa en tecnologías semánticas y de agentes: 1) utiliza ontologías para modelar la información de usuarios, dispositivos, servicios, y el entorno, y un razonador para manejar estas ontologías e inferir conocimiento que no ha sido explicitado; 2) utiliza una arquitectura basada en agentes (tanto estáticos como móviles) que permite a los distintos dispositivos SHERLOCK intercambiar conocimiento y así mantener sus ontologías locales actualizadas, y procesar peticiones de información de sus usuarios encontrando lo que necesitan, allá donde esté. El uso de estas dos tecnologías permite a SHERLOCK ser flexible en términos de los servicios que ofrece al usuario (que son aprendidos mediante la interacción entre los dispositivos), y de los mecanismos para encontrar la información que el usuario quiere (que se adaptan a la infraestructura de comunicación subyacente)

Towards generalizable neuro-symbolic reasoners

Doctor of PhilosophyDepartment of Computer ScienceMajor Professor Not ListedSymbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of --not necessarily easily obtained-- data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of trainable artificial neural networks. Over the course of this dissertation, we provide a brief summary of our recent efforts to bridge the neural and symbolic divide in the context of deep deductive reasoners. More specifically, We designed a novel way of conducting neuro-symbolic through pointing to the input elements. More importantly we showed that the proposed approach is generalizable across new domain and vocabulary demonstrating symbol-invariant zero-shot reasoning capability. Furthermore, We have demonstrated that a deep learning architecture based on memory networks and pre-embedding normalization is capable of learning how to perform deductive reason over previously unseen RDF KGs with high accuracy. We are applying these models on Resource Description Framework (RDF), first-order logic, and the description logic EL+ respectively. Throughout this dissertation we will discuss strengths and limitations of these models particularly in term of accuracy, scalability, transferability, and generalizabiliy. Based on our experimental results, pointer networks perform remarkably well across multiple reasoning tasks while outperforming the previously reported state of the art by a significant margin. We observe that the Pointer Networks preserve their performance even when challenged with knowledge graphs of the domain/vocabulary it has never encountered before. To our knowledge, this work is the first attempt to reveal the impressive power of pointer networks for conducting deductive reasoning. Similarly, we show that memory networks can be trained to perform deductive RDFS reasoning with high precision and recall. The trained memory network's capabilities in fact transfer to previously unseen knowledge bases. Finally will talk about possible modifications to enhance desirable capabilities. Altogether, these research topics, resulted in a methodology for symbol-invariant neuro-symbolic reasoning

Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery

To manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around

Scalable Reasoning for Knowledge Bases Subject to Changes

ScienceWeb is a semantic web system that collects information about a research community and allows users to ask qualitative and quantitative questions related to that information using a reasoning engine. The more complete the knowledge base is, the more helpful answers the system will provide. As the size of knowledge base increases, scalability becomes a challenge for the reasoning system. As users make changes to the knowledge base and/or new information is collected, providing fast enough response time (ranging from seconds to a few minutes) is one of the core challenges for the reasoning system. There are two basic inference methods commonly used in first order logic: forward chaining and backward chaining. As a general rule, forward chaining is a good method for a static knowledge base and backward chaining is good for the more dynamic cases. The goal of this thesis was to design a hybrid reasoning architecture and develop a scalable reasoning system whose efficiency is able to meet the interaction requirements in a ScienceWeb system when facing a large and evolving knowledge base. Interposing a backward chaining reasoner between an evolving knowledge base and a query manager with support of trust yields an architecture that can support reasoning in the face of frequent changes. An optimized query-answering algorithm, an optimized backward chaining algorithm and a trust-based hybrid reasoning algorithm are three key algorithms in such an architecture. Collectively, these three algorithms are significant contributions to the field of backward chaining reasoners over ontologies. I explored the idea of trust in the trust-based hybrid reasoning algorithm, where each change to the knowledge base is analyzed as to what subset of the knowledge base is impacted by the change and could therefore contribute to incorrect inferences. I adopted greedy ordering and deferring joins in optimized query-answering algorithm. I introduced four optimizations in the algorithm for backward chaining. These optimizations are: 1) the implementation of the selection function, 2) the upgraded substitute function, 3) the application of OLDT and 4) solving of the owl: sameAs problem. I evaluated our optimization techniques by comparing the results with and without optimization techniques. I evaluated our optimized query answering algorithm by comparing to a traditional backward-chaining reasoner. I evaluated our trust-based hybrid reasoning algorithm by comparing the performance of a forward chaining algorithm to that of a pure backward chaining algorithm. The evaluation results have shown that the hybrid reasoning architecture with the scalable reasoning system is able to support scalable reasoning of ScienceWeb to answer qualitative questions effectively when facing both a fixed knowledge base and an evolving knowledge base