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Identifying Meaningful Citations
TLDR
We introduce the novel task of identifying important citations in scholarly literature. Expand
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Construction of the Literature Graph in Semantic Scholar
TLDR
We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. Expand
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Theoretical Foundations for Abstraction-Based Probabilistic Planning
TLDR
The affine-operator serves as a tool for constructing (convex) sets of probability distributions, and which can be considered as a generalization of belief functions and interval mass assignments. Expand
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Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures
TLDR
We propose a case-based approach to alleviating the preference elicitation bottleneck, using the closest existing preference structures as potential defaults. Expand
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Problem-Focused Incremental Elicitation of Multi-Attribute Utility Models
TLDR
We present an approach to planning and decision making that performs the utility elicitation incrementally and in a way that is informed by the domain model. Expand
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Similarity of personal preferences: Theoretical foundations and empirical analysis
TLDR
We propose a similarity measure, called probabilistic distance, that originates from the Kendall's tau function, a well-known concept in statistical literature. Expand
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A Hybrid Approach to Reasoning with Partially Elicited Preference Models
TLDR
We present a hybrid approach to preference elicitation and decision making that is grounded in classical multi-attribute utility theory, but can make effective use of the expressive power of qualitative approaches. Expand
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Feature-based decomposition of inductive proofs applied to real-time avionics software: an experience report
TLDR
The hardware and software in modern aircraft control systems are good candidates for verification using formal methods: they are complex, safety-critical, and challenge the capabilities of test-based verification strategies. Expand
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Modeling user preferences via theory refinement
TLDR
We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process in a Knowledge-Based Artificial Neural Network. Expand
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Preference Elicitation via Theory Refinement
TLDR
We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. Expand
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