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The group conducts research in logic-based and probabilistic reasoning, commonsense reasoning decision-making in nondeterministic and stochastic domains, logic-based state estimation filtering and planning.


Logical Filtering

Filtering denotes any method whereby an agent updates its belief state - its knowledge of the state of the world - from a sequence of actions and observations. In logical filtering, the belief state is a logical formula describing possible world states and the agent has a (possibly nondeterministic) logical model of its environment and sensors. This project aims at efficient logical filtering algorithms that maintain a compact belief state representation indefinitely, for a broad range of environment classes including nondeterministic, partially observable STRIPS environments and environments in which actions permute the state space. Efficient filtering is also possible when the belief state is represented using prime implicates, or when it is approximated by a logically weaker formula.


Prof. Eyal Amir
Hannaneh Hajishirzi
Brian Hlubocky
Megan Nance
Afsaneh Shirazi
Adam Vogel

For more information on this project (including publications) go to the project homepage

Commonsense reasoning

More info to come soon...


Prof. Eyal Amir
Phil Oertel
Deepak Ramachandran

Decision-making under uncertainty

More info to come soon...


Prof. Eyal Amir
Benjamin Liebald

Knowledge Representation & Reasoning Group
Department of Computer Science
University of Illinois at Urbana-Champaign
201 N. Goodwin Ave., Urbana, IL 61801, USA 

Department of Computer Science Unversity of Illinois at Urbana-Champaign

Last updated on: March 14th, 2005, 6:54 pm

Author: Benjamin Liebald.