Tim@CIS.UPENN.EDU (Tim Finin) (08/13/86)
Active Reduction of Uncertainty in Multi-sensor Systems
Ph.D. Thesis Proposal
Greg Hager
(greg@upenn-grasp)
General Robots and Active Sensory Perception Laboratory
University of Pennsylvania
Department of Computer and Information Sciences
Philadelphia, PA 19104
10:00 AM, August 15, 1986
Room 554 Moore
If robots are to perform tasks in unconstrained environments, they will have
to rely on sensor information to make decisions. In general, sensor
information has some uncertainty associated with it. The uncertainty can be
conceptually divided into two types: statistical uncertainty due to signal
noise, and incompleteness of information due to limitations of sensor scope.
Inevitably, the information needed for proper action will be uncertain. In
these cases, the robot will need to take action explicitly devoted to
reducing uncertainty.
The problem of reducing uncertainty can be studied within the theoretical
framework of team decision theory. Team decision theory considers a number
of decision makers observing the world via information structures, and
taking action dictated by decision rules. Decision rules are evaluated
relative to team and individual utility considerations. In this vocabulary,
sensors are considered as controllable information structures whose behavior
is determined by individual and group utilities. For the problem of
reducing uncertainty, these utilities are based on the information expected
as the result of taking action.
In general, a robot does not only consider direct sensor observations, but
also evaluates and combines that data over time relative to some model of
the observed environment. In this proposal, information aggregation is
modeled via belief systems as studied in philosophy. Reducing uncertainty
corresponds to driving the belief system into one of a set of information
states. Within this context, the issues that will be addressed are the
specification of utilities in terms of belief states, the organization of a
sensor system, and the evaluation of decision rules. These questions will
first be studied through theory and simulation, and finally applied to an
existing multi-sensor system.
Advisor: Dr. Max Mintz
Committee: Dr. Ruzena Bajcsy (Chairperson)
Dr. Zolton Domotor (Philosophy Dept.)
Dr. Richard Paul
Dr. Stanley Rosenschein (SRI International and CSLI)