Peter Haddawy

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We deene a language for representing context-sensitive probabilistic knowledge. A knowledge base consists of a set of universally quantiied probability sentences that include context constraints, which allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a(More)
This paper proposes and investigates an approach to deduction in probabilistic logic, using as its medium a language that generalizes the propositional version of Nilsson's probabilistic logic by incorporating conditional probabilities. Unlike many other approaches to deduction in probabilistic logic, this approach is based on inference rules and therefore(More)
plan is represented by an interval that includes the expected utilities of all possible instantiations of that abstract plan. Refining the plan, that is, choosing an instantiation, can only narrow the interval. We can stop refining an abstract plan when its upper expected utility bound is less than the lower bound of some other plan. Forty-one possible(More)
This paper describes COMET, a collaborative intelligent tutoring system for medical problem-based learning. The system uses Bayesian networks to model individual student knowledge and activity, as well as that of the group. It incorporates a multi-modal interface that integrates text and graphics so as to provide a rich communication channel between the(More)
Abstracting Probabilistic Actions Peter Haddawy AnHai Doan Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee PO Box 784 Milwaukee, WI 53201 fhaddawy, anhaig@cs.uwm.edu Abstract This paper discusses the problem of abstracting conditional probabilistic actions. We identify two distinct types of abstraction:(More)
We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We de-ne a xpoint theory, declarative(More)
  • L Ngo, Peter Haddawy, R A Krieger, J Helwig
  • Computers in biology and medicine
  • 1997
We present a language for representing context-sensitive temporal probabilistic knowledge. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a sound and complete algorithm for computing posterior probabilities of temporal queries, as(More)
We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. We demonstrate that when domain knowledge is available, even in the form of weak and somewhat inaccurate assumptions, significantly less data is required to build an accurate model of user preferences than when(More)