NMR 2004
Call for Papers
Important Dates
Programme Committee
Author Instructions

Foundations of Nonmonotonic Reasoning
Computational Aspects of Nonmonotonic Reasoning
Action and Causality
Belief Change
Uncertainty Frameworks
Argument, Dialogue and Decision

NMR logo

International Workshop on
Non-Monotonic Reasoning

June 6-8, 2004
Westin Whistler Resort and Spa,
Whistler BC, Canada

Invited Speakers

Henry Kyburg
The Logic of Risky Knowledge
Much of our everyday knowledge is risky. This not only includes personal judgments, but the results of measurement, data obtained from references or by report, the results of statistical testing, etc. There are two (often opposed) views in AI on how to handle risky empirical knowledge. One view, characterized often by modal or nonmonotonic logics, is that the structure of such knowledge should be captured by the formal logical properties of a set of sentences, if we can just get the logic right. The other view takes probability to be central to the characterization of risky knowledge, but often does not allow for the tentative or corrigible acceptance of a set of sentences. We examine a view, based on $\epsilon$-acceptability, that combines both probability and modality. A statement is $\epsilon$-accepted if the probability of its denial is at most $\epsilon$, where $\epsilon$ is taken to be a fixed small parameter as is customary in the practice of statistical testing. We show that given a body of evidence $\Gamma_\delta$ and a threshold $\epsilon$, the set of $\epsilon$-accepted statements $\Gamma_\epsilon$ gives rise to the logical structure of the classical modal system EMN, the smallest classical modal system E supplemented by the axiom schemas M: $\ul \Box_\epsilon(\phi \land \psi ) \rightarrow
(\Box_\epsilon \phi \land \Box_\epsilon \psi)\ur$ and N: $\Box_\epsilon\top$.

Jerome Lang
Logical languages for preference representation (and their relation to nonmonotonic reasoning)
The specification of a decision making problem needs before all the expression of the preferences of the agent over the set of feasible alternatives. Now, in many real-world domains, the set of alternatives is the set of assignments of a value to each of a given set of variables. In such cases, there are exponentially many alternatives and it is not reasonable to ask agents to report their preference in an explicit way. For this reason, several logical languages have been studied in Artificial Intelligence for encoding compactly preference relations or utility functions over a set of alternatives. Such preference representation languages are often built up on propositional logic, and allow for a much more concise representation of the preference structure than an explicit enumeration, while preserving a good readability and hence a similarity with the way agents express their preferences in natural language. This talk tries to give a synthetic review of these languages, and relates them to several research areas in nonmonotonic reasoning.

Mirek Truszcz ynski
Answer-set programming - directio ns and challenges
About five years ago answer-set programming emerged as a viable approa ch to declarative programming. In the talk we will look at the area and identify main research directions, emerging new themes and challenges for the future research. We will discuss theoretical foundations, mode ling languages, implementations of fast processing techniques, and applicat ions.

© 2003 Pacific Institute for the Mathematical Sciences
Last Modified: March 27, 2004.