• Publications
  • Influence
Answering Queries from Context-Sensitive Probabilistic Knowledge Bases
TLDR
This work presents a query answering procedure that takes a query Q and a set of evidence E and constructs a Bayesian network to compute P(Q¦E) . Expand
Anytime Deduction for Probabilistic Logic
TLDR
The deduction method presented here contrasts with other methods whose ability to perform logical reasoning is either limited or requires finding all truth assignments consistent with the given sentences. Expand
A comparative analysis of techniques for predicting academic performance
TLDR
In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network for predicting student performance, and the maturity of open source tools was demonstrated. Expand
Generating Bayesian Networks from Probablity Logic Knowledge Bases
  • P. Haddawy
  • Computer Science, Mathematics
  • UAI
  • 29 July 1994
TLDR
A network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P(Q|E) and it is proved that the algorithm to be correct. Expand
Utility Models for Goal‐Directed, Decision‐Theoretic Planners
TLDR
AI planning agents are goal‐directed: success is measured in terms of whether an input goal is satisfied, and planning representations and algorithms have been designed to exploit that structure. Expand
Intelligent dental training simulator with objective skill assessment and feedback
TLDR
A VR dental training simulator that provides a virtual reality (VR) environment with haptic feedback for dental students to practice dental surgical skills in the context of a crown preparation procedure and a mechanism for providing objective skill assessment and feedback is introduced. Expand
Construction of a Bayesian network for mammographic diagnosis of breast cancer.
TLDR
The development and validation of a Bayesian network (MammoNet) to assist in mammographic diagnosis of breast cancer is described and the methods and issues in the system's design, implementation, and evaluation are outlined. Expand
Probabilistic Logic Programming and Bayesian Networks
TLDR
This work defines a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs, and discusses the relationship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning. Expand
Decision-theoretic Refinement Planning Using Inheritance Abstraction
TLDR
Tenenberg’s notion of inheritance abstraction for STRIPS operators to apply to conditional probabilistic actions is extended and the intent is to formalize the notion that analogous action types can be structured together into an action class at the abstract level. Expand
Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer.
TLDR
It is concluded that Bayesian networks provide a potentially useful tool for mammographic decision support. Expand
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