We address the problem of propositional logic-based abduction, i.e., the problem of searching for a best explanation for a given propositional observation according to a given propositional knowledge base. We give a general algorithm, based on the notion of projection; then we study restrictions over the representations of the knowledge base and of the query, and find new polynomial classes of abduction problems.
This thesis presents new methods for unsupervised learning of distributed representations of words and entities from text and knowledge bases. The first algorithm presented in the thesis is a multi-view algorithm for learning representations of words called Multiview Latent Semantic Analysis (MVLSA). By incorporating up to 46 different types of co-occurrence statistics for the same vocabulary of english words, I show that MVLSA outperforms other state-of-the-art word embedding models. Next, I focus on learning entity representations for search and recommendation and present the second method of this thesis, Neural Variational Set Expansion (NVSE). NVSE is also an unsupervised learning method, but it is based on the Variational Autoencoder framework. Evaluations with human annotators show that NVSE can facilitate better search and recommendation of information gathered from noisy, automatic annotation of unstructured natural language corpora. Finally, I move from unstructured data and focus on structured knowledge graphs. I present novel approaches for learning embeddings of vertices and edges in a knowledge graph that obey logical constraints.
Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.
Compressing Pattern Databases / 638 Ariel Felner, Ram Meshulam, Robert C. Holte, and Richard E. Korf A General Solution to the Graph History Interaction Problem / 644 Akihiro Kishimoto and Martin Müller Best-First Frontier Search with Delayed Duplicate Detection / 650 Richard E. Korf Temperature Discovery Search / 658 Martin Müller, Markus Enzenberger, and Jonathan Schaeffer Simple Search Methods for Finding a Nash Equilibrium / 664 Ryan Porter, Eugene Nudelman, and Yoav Shoham Towards Efficient Sampling: Exploiting Random Walk Strategies / 670 Wei Wei, Jordan Erenrich, and Bart Selman Space-Efficient Memory-Based Heuristics / 677 Rong Zhou and Eric A. Hansen Structured Duplicate Detection in External-Memory Graph Search / 683 Rong Zhou and Eric A. Hansen