The Allen Institute for Artificial Intelligence
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Identifying Meaningful Citations
- Marco Valenzuela, Vu A. Ha, Oren Etzioni
- Computer ScienceAAAI Workshop: Scholarly Big Data
- 1 April 2015
This work introduces the novel task of identifying important citations in scholarly literature, i.e., citations that indicate that the cited work is used or extended in the new effort, and proposes a supervised classification approach that addresses this task with a battery of features.
Construction of the Literature Graph in Semantic Scholar
This paper reduces literature graph construction into familiar NLP tasks, point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task.
Toward Case-Based Preference Elicitation: Similarity Measures on Preference Structures
This paper proposes eliciting the preferences of a new user interactively and incrementally, using the closest existing preference structures as potential defaults, and takes the first step of studying various distance measures over fully and partially specified preference structures.
Theoretical Foundations for Abstraction-Based Probabilistic Planning
The central notion in the framework proposed here is that of the affine-operator, which 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.
Problem-Focused Incremental Elicitation of Multi-Attribute Utility Models
This paper identifies patterns of problem instances where plans can be proved to be suboptimal if the (unknown) utility function satisfies certain conditions and presents an approach to planning and decision making that performs the utility elicitation incrementally and in a way that is informed by the domain model.
Similarity of personal preferences: Theoretical foundations and empirical analysis
A Hybrid Approach to Reasoning with Partially Elicited Preference Models
This work shows how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify supoptimal alternatives, and demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.
Feature-based decomposition of inductive proofs applied to real-time avionics software: an experience report
- Vu A. Ha, M. Rangarajan, D. Cofer, H. Ruess, B. Dutertre
- Computer ScienceProceedings. 26th International Conference on…
- 23 May 2004
A feature-based technique for modeling state-transition systems and formulating inductive invariants is presented, facilitating an incremental approach to theorem proving that scales well to models of increasing complexity, and has the potential to be applicable to a wide range of problems.
Modeling user preferences via theory refinement
It is shown how to encode assumptions concerning preferential independence and monotonicity in a Knowledge-Based Artificial Neural Network and the degree to which user preferences violate a set of assumptions is quantified.
Geometric foundations for interval-based probabilities
- Vu A. Ha, A. Doan, V. Vu, P. Haddawy
- Mathematics, Computer ScienceAnnals of Mathematics and Artificial Intelligence
- 10 November 1998
An analysis of some of the central issues in representing and reasoning with interval probabilities, including the probability cross-product operator and its interval generalization, the cc-operator is provided.