Definition and Complexity of Some Basic Metareasoning Problems

In most real-world settings, due to limited time or other resources, an agent cannot perform all potentially useful deliberation and information gathering actions. This leads to the metareasoning problem of selecting such actions. Decision-theoretic methods for metareasoning have been studied in AI, but there are few theoretical results on the complexity of metareasoning. We derive hardness results for three settings which most real metareasoning systems would have to encompass as special cases. In the first, the agent has to decide how to allocate its deliberation time across anytime algorithms running on different problem instances. We show this to be $\mathcal{NP}$-complete. In the second, the agent has to (dynamically) allocate its deliberation or information gathering resources across multiple actions that it has to choose among. We show this to be $\mathcal{NP}$-hard even when evaluating each individual action is extremely simple. In the third, the agent has to (dynamically) choose a limited number of deliberation or information gathering actions to disambiguate the state of the world. We show that this is $\mathcal{NP}$-hard under a natural restriction, and $\mathcal{PSPACE}$-hard in general

Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings

Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types. We present a method for characterizing the usage patterns of clinical concepts among different document types, in order to capture semantic differences beyond the lexical level. By training concept embeddings on clinical documents of different types and measuring the differences in their nearest neighborhood structures, we are able to measure divergences in concept usage while correcting for noise in embedding learning. Experiments on the MIMIC-III corpus demonstrate that our approach captures clinically-relevant differences in concept usage and provides an intuitive way to explore semantic characteristics of clinical document collections.Comment: LOUHI 2019 (co-located with EMNLP

Distributed Reasoning in a Peer-to-Peer Setting: Application to the Semantic Web

In a peer-to-peer inference system, each peer can reason locally but can also solicit some of its acquaintances, which are peers sharing part of its vocabulary. In this paper, we consider peer-to-peer inference systems in which the local theory of each peer is a set of propositional clauses defined upon a local vocabulary. An important characteristic of peer-to-peer inference systems is that the global theory (the union of all peer theories) is not known (as opposed to partition-based reasoning systems). The main contribution of this paper is to provide the first consequence finding algorithm in a peer-to-peer setting: DeCA. It is anytime and computes consequences gradually from the solicited peer to peers that are more and more distant. We exhibit a sufficient condition on the acquaintance graph of the peer-to-peer inference system for guaranteeing the completeness of this algorithm. Another important contribution is to apply this general distributed reasoning setting to the setting of the Semantic Web through the Somewhere semantic peer-to-peer data management system. The last contribution of this paper is to provide an experimental analysis of the scalability of the peer-to-peer infrastructure that we propose, on large networks of 1000 peers

A multi-matching technique for combining similarity measures in ontology integration

Ontology matching is a challenging problem in many applications, and is a major issue for interoperability in information systems. It aims to find semantic correspondences between a pair of input ontologies, which remains a labor intensive and expensive task. This thesis investigates the problem of ontology matching in both theoretical and practical aspects and proposes a solution methodology, called multi-matching . The methodology is validated using standard benchmark data and its performance is compared with available matching tools. The proposed methodology provides a framework for users to apply different individual matching techniques. It then proceeds with searching and combining the match results to provide a desired match result in reasonable time. In addition to existing applications for ontology matching such as ontology engineering, ontology integration, and exploiting the semantic web, the thesis proposes a new approach for ontology integration as a backbone application for the proposed matching techniques. In terms of theoretical contributions, we introduce new search strategies and propose a structure similarity measure to match structures of ontologies. In terms of practical contribution, we developed a research prototype, called MLMAR - Multi-Level Matching Algorithm with Recommendation analysis technique, which implements the proposed multi-level matching technique, and applies heuristics as optimization techniques. Experimental results show practical merits and usefulness of MLMA

Combining Representation Learning with Logic for Language Processing

The current state-of-the-art in many natural language processing and automated knowledge base completion tasks is held by representation learning methods which learn distributed vector representations of symbols via gradient-based optimization. They require little or no hand-crafted features, thus avoiding the need for most preprocessing steps and task-specific assumptions. However, in many cases representation learning requires a large amount of annotated training data to generalize well to unseen data. Such labeled training data is provided by human annotators who often use formal logic as the language for specifying annotations. This thesis investigates different combinations of representation learning methods with logic for reducing the need for annotated training data, and for improving generalization.Comment: PhD Thesis, University College London, Submitted and accepted in 201

Air Force Institute of Technology Research Report 2017

This Research Report presents the FY18 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

Knowledge Representation and Reasoning with Deep Neural Networks

Knowledge representation and reasoning is one of the central challenges of artificial intelligence, and has important implications in many fields including natural language understanding and robotics. Representing knowledge with symbols, and reasoning via search and logic has been the dominant paradigm for many decades. In this work, we use deep neural networks to learn to both represent symbols and perform reasoning end-to-end from data. By learning powerful non-linear models, our approach generalizes to massive amounts of knowledge and works well with messy real-world data using minimal human effort. First, we show that recurrent neural networks with an attention mechanism achieve state-of-the-art reasoning on a large structured knowledge graph. Next, we develop Neural Programmer, a neural network augmented with discrete operations that can be learned to induce latent programs with backpropagation. We apply Neural Programmer to induce short programs on two datasets: a synthetic dataset requiring arithmetic and logic reasoning, and a natural language question answering dataset that requires reasoning on semi-structured Wikipedia tables. We present what is to our awareness the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset. Unlike previous learning approaches to program induction, the model does not require domain-specific grammars, rules, or annotations. Finally, we discuss methods to scale Neural Programmer training to large databases