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- Liem Ngo, Peter Haddawy
- Theor. Comput. Sci.
- 1997

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)

- Alan M. Frisch, Peter Haddawy
- Artif. Intell.
- 1994

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)

- Peter Haddawy
- 1994

We present a method for dynamically generating Bayesian networks from knowledge bases consisting of rst-order probability logic sentences. We present a subset of probability logic su cient for representing the class of Bayesian networks with discrete-valued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base… (More)

- Peter Haddawy, Meliani Suwandi
- AIPS
- 1994

ing Actions We extend Tenenberg’s (Tenenberg 1991) notion of inheritance abstraction for STRIPS operators to apply to conditional probabilistic actions. As Tenenberg explains it, "the intent of using inheritance abstraction is to formalize the notion that analogous action types can be structured together into an action class at the abstract level,… (More)

- Peter Haddawy, Steve Hanks
- Computational Intelligence
- 1998

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)

- Siriwan Suebnukarn, Peter Haddawy
- IUI
- 2004

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)

- Peter Haddawy, AnHai Doan
- UAI
- 1994

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)

- Liem Ngo, Peter Haddawy
- ASIAN
- 1995

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)

- Ling Ngo, Peter Haddawy, Robert A. Krieger, James 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)

- Peter Haddawy, Vu A. Ha, Angelo C. Restificar, Benjamin Geisler, John Miyamoto
- Journal of Machine Learning Research
- 2003

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)